model_id
stringlengths 7
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|---|---|---|
hn11235/plant-seedlings-model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1235
- Accuracy: 0.9783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3236 | 1.27 | 500 | 0.2747 | 0.9019 |
| 0.2273 | 2.54 | 1000 | 0.3031 | 0.9038 |
| 0.082 | 3.82 | 1500 | 0.2103 | 0.9280 |
| 0.061 | 5.09 | 2000 | 0.2235 | 0.9408 |
| 0.0668 | 6.36 | 2500 | 0.1633 | 0.9554 |
| 0.0739 | 7.63 | 3000 | 0.1561 | 0.9586 |
| 0.0836 | 8.91 | 3500 | 0.1904 | 0.9446 |
| 0.0078 | 10.18 | 4000 | 0.2045 | 0.9535 |
| 0.087 | 11.45 | 4500 | 0.4487 | 0.9146 |
| 0.0119 | 12.72 | 5000 | 0.2162 | 0.9567 |
| 0.0002 | 13.99 | 5500 | 0.1157 | 0.9758 |
| 0.0001 | 15.27 | 6000 | 0.1199 | 0.9771 |
| 0.0001 | 16.54 | 6500 | 0.1215 | 0.9790 |
| 0.0001 | 17.81 | 7000 | 0.1223 | 0.9790 |
| 0.0001 | 19.08 | 7500 | 0.1235 | 0.9783 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
Soulaimen/swin-tiny-patch4-window7-224-long_sleeve
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-long_sleeve
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0078
- Accuracy: 0.9965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.07 | 0.99 | 80 | 0.0078 | 0.9965 |
| 0.0402 | 1.99 | 161 | 0.0074 | 0.9965 |
| 0.0288 | 2.97 | 240 | 0.0068 | 0.9965 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"chemise",
"hoodies"
] |
Soulaimen/swin-tiny-patch4-window7-224-short_sleeve
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-short_sleeve
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0052
- Accuracy: 0.9973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1194 | 1.0 | 105 | 0.0181 | 0.992 |
| 0.1087 | 2.0 | 211 | 0.0174 | 0.992 |
| 0.0131 | 2.99 | 315 | 0.0052 | 0.9973 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"chemise",
"t-shirt"
] |
harish03/catbreed
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# catbreed
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the catbreed dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7210
- Accuracy: 0.9252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1329 | 0.99 | 29 | 1.7492 | 0.8376 |
| 1.3437 | 1.98 | 58 | 1.1638 | 0.9038 |
| 0.9266 | 2.97 | 87 | 0.9013 | 0.8974 |
| 0.7274 | 4.0 | 117 | 0.7345 | 0.9338 |
| 0.6652 | 4.96 | 145 | 0.7210 | 0.9252 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"african_leopard",
"caracal",
"cheetah",
"clouded_leopard",
"jaguar",
"lion",
"ocelot",
"puma",
"snow_leopard",
"tiger"
] |
Soulaimen/swin-tiny-patch4-window7-224-bottom
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-bottom
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1389
- Accuracy: 0.9513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3578 | 0.99 | 132 | 0.1946 | 0.9258 |
| 0.2537 | 2.0 | 265 | 0.1389 | 0.9513 |
| 0.1909 | 2.98 | 396 | 0.1285 | 0.9513 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"jeans",
"legging",
"sweatpants"
] |
Soulaimen/swin-tiny-patch4-window7-224-bottom-112
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-bottom-112
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1603
- Accuracy: 0.9576
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3441 | 0.99 | 132 | 0.1944 | 0.9534 |
| 0.2171 | 2.0 | 265 | 0.1894 | 0.9449 |
| 0.1709 | 2.98 | 396 | 0.1603 | 0.9576 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"jeans",
"legging",
"sweatpants"
] |
Soulaimen/swin-tiny-patch4-window7-224-bottom-macco
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-bottom-macco
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1369
- Accuracy: 0.9486
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.427 | 1.0 | 175 | 0.2480 | 0.9021 |
| 0.241 | 2.0 | 350 | 0.2291 | 0.9085 |
| 0.2771 | 3.0 | 525 | 0.1369 | 0.9486 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"jeans",
"leggings",
"macco",
"sweatpants"
] |
Soulaimen/swin-tiny-patch4-window7-224-bottom_cleaned_data
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-bottom_cleaned_data
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0839
- Accuracy: 0.9726
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4444 | 1.0 | 174 | 0.2271 | 0.9163 |
| 0.3518 | 2.0 | 349 | 0.2449 | 0.9034 |
| 0.225 | 3.0 | 523 | 0.1325 | 0.9501 |
| 0.2195 | 4.0 | 698 | 0.1024 | 0.9549 |
| 0.2627 | 5.0 | 872 | 0.1046 | 0.9630 |
| 0.142 | 6.0 | 1047 | 0.0839 | 0.9726 |
| 0.1516 | 7.0 | 1221 | 0.0918 | 0.9630 |
| 0.1498 | 8.0 | 1396 | 0.0780 | 0.9726 |
| 0.1189 | 9.0 | 1570 | 0.0721 | 0.9662 |
| 0.1594 | 9.97 | 1740 | 0.0668 | 0.9726 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"jeans",
"leggings",
"macco",
"sweatpants"
] |
Soulaimen/swin-tiny-patch4-window7-224-bottom_cleaned_data-hpt
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-bottom_cleaned_data-hpt
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0701
- Accuracy: 0.9694
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4307 | 0.99 | 99 | 0.2332 | 0.9227 |
| 0.3425 | 2.0 | 199 | 0.1904 | 0.9404 |
| 0.29 | 3.0 | 299 | 0.1316 | 0.9388 |
| 0.2597 | 3.99 | 398 | 0.1158 | 0.9533 |
| 0.2638 | 4.99 | 498 | 0.0987 | 0.9614 |
| 0.209 | 6.0 | 598 | 0.0802 | 0.9710 |
| 0.1776 | 7.0 | 698 | 0.0838 | 0.9597 |
| 0.1776 | 7.99 | 797 | 0.0787 | 0.9694 |
| 0.1502 | 9.0 | 897 | 0.0797 | 0.9726 |
| 0.1402 | 9.93 | 990 | 0.0701 | 0.9694 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"jeans",
"leggings",
"macco",
"sweatpants"
] |
microsoft/focalnet-tiny
|
# FocalNet (tiny-sized model)
FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet).
Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision.
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-tiny")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
author = {Jianwei Yang and
Chunyuan Li and
Jianfeng Gao},
title = {Focal Modulation Networks},
journal = {CoRR},
volume = {abs/2203.11926},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.11926},
doi = {10.48550/arXiv.2203.11926},
eprinttype = {arXiv},
eprint = {2203.11926},
timestamp = {Tue, 29 Mar 2022 18:07:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
microsoft/focalnet-tiny-lrf
|
# FocalNet (tiny-sized large reception field model)
FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet).
Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision.
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-tiny-lrf")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny-lrf")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
author = {Jianwei Yang and
Chunyuan Li and
Jianfeng Gao},
title = {Focal Modulation Networks},
journal = {CoRR},
volume = {abs/2203.11926},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.11926},
doi = {10.48550/arXiv.2203.11926},
eprinttype = {arXiv},
eprint = {2203.11926},
timestamp = {Tue, 29 Mar 2022 18:07:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
microsoft/focalnet-small-lrf
|
# FocalNet (small-sized large reception field model)
FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet).
Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision.
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-small-lrf")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-small-lrf")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
author = {Jianwei Yang and
Chunyuan Li and
Jianfeng Gao},
title = {Focal Modulation Networks},
journal = {CoRR},
volume = {abs/2203.11926},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.11926},
doi = {10.48550/arXiv.2203.11926},
eprinttype = {arXiv},
eprint = {2203.11926},
timestamp = {Tue, 29 Mar 2022 18:07:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
microsoft/focalnet-base-lrf
|
# FocalNet (base-sized large reception field model)
FocalNet model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet).
Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision.
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-base-lrf")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-base-lrf")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
author = {Jianwei Yang and
Chunyuan Li and
Jianfeng Gao},
title = {Focal Modulation Networks},
journal = {CoRR},
volume = {abs/2203.11926},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.11926},
doi = {10.48550/arXiv.2203.11926},
eprinttype = {arXiv},
eprint = {2203.11926},
timestamp = {Tue, 29 Mar 2022 18:07:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
microsoft/focalnet-base
|
# FocalNet (tiny-sized large reception field model)
FocalNet model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet).
Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision.
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-base")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-base")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
author = {Jianwei Yang and
Chunyuan Li and
Jianfeng Gao},
title = {Focal Modulation Networks},
journal = {CoRR},
volume = {abs/2203.11926},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.11926},
doi = {10.48550/arXiv.2203.11926},
eprinttype = {arXiv},
eprint = {2203.11926},
timestamp = {Tue, 29 Mar 2022 18:07:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
microsoft/focalnet-small
|
# FocalNet (small-sized model)
FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet).
Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision.
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-small")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-small")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
author = {Jianwei Yang and
Chunyuan Li and
Jianfeng Gao},
title = {Focal Modulation Networks},
journal = {CoRR},
volume = {abs/2203.11926},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.11926},
doi = {10.48550/arXiv.2203.11926},
eprinttype = {arXiv},
eprint = {2203.11926},
timestamp = {Tue, 29 Mar 2022 18:07:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
carolinetfls/plant-seedlings-model-aug-2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-aug-2
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2879
- Accuracy: 0.9490
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3968 | 0.8 | 100 | 0.2863 | 0.8975 |
| 0.3388 | 1.6 | 200 | 0.3517 | 0.8847 |
| 0.1707 | 2.4 | 300 | 0.3517 | 0.9083 |
| 0.0475 | 3.2 | 400 | 0.4085 | 0.8828 |
| 0.0823 | 4.0 | 500 | 0.2900 | 0.9274 |
| 0.0428 | 4.8 | 600 | 0.3437 | 0.9229 |
| 0.0691 | 5.6 | 700 | 0.2896 | 0.9363 |
| 0.0217 | 6.4 | 800 | 0.3413 | 0.9299 |
| 0.0341 | 7.2 | 900 | 0.2692 | 0.9471 |
| 0.0006 | 8.0 | 1000 | 0.2734 | 0.9471 |
| 0.0005 | 8.8 | 1100 | 0.2686 | 0.9503 |
| 0.0001 | 9.6 | 1200 | 0.2918 | 0.9471 |
| 0.0001 | 10.4 | 1300 | 0.2827 | 0.9490 |
| 0.0001 | 11.2 | 1400 | 0.2838 | 0.9497 |
| 0.0001 | 12.0 | 1500 | 0.2850 | 0.9490 |
| 0.0001 | 12.8 | 1600 | 0.2861 | 0.9490 |
| 0.0001 | 13.6 | 1700 | 0.2868 | 0.9490 |
| 0.0001 | 14.4 | 1800 | 0.2874 | 0.9490 |
| 0.0001 | 15.2 | 1900 | 0.2878 | 0.9490 |
| 0.0001 | 16.0 | 2000 | 0.2879 | 0.9490 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
MBZUAI/swiftformer-s
|
# SwiftFormer (swiftformer-s)
## Model description
The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.
## Intended uses & limitations
## How to use
import requests
from PIL import Image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
from transformers import ViTImageProcessor
processor = ViTImageProcessor.from_pretrained('shehan97/swiftformer-s')
inputs = processor(images=image, return_tensors="pt")
from transformers.models.swiftformer import SwiftFormerForImageClassification
new_model = SwiftFormerForImageClassification.from_pretrained('shehan97/swiftformer-s')
output = new_model(inputs['pixel_values'], output_hidden_states=True)
logits = output.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", new_model.config.id2label[predicted_class_idx])
## Limitations and bias
## Training data
The classification model is trained on the ImageNet-1K dataset.
## Training procedure
## Evaluation results
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
MBZUAI/swiftformer-l1
|
# SwiftFormer (swiftformer-l1)
## Model description
The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.
## Intended uses & limitations
## How to use
import requests
from PIL import Image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
from transformers import ViTImageProcessor
processor = ViTImageProcessor.from_pretrained('shehan97/swiftformer-l1')
inputs = processor(images=image, return_tensors="pt")
from transformers.models.swiftformer import SwiftFormerForImageClassification
new_model = SwiftFormerForImageClassification.from_pretrained('shehan97/swiftformer-l1')
output = new_model(inputs['pixel_values'], output_hidden_states=True)
logits = output.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", new_model.config.id2label[predicted_class_idx])
## Limitations and bias
## Training data
The classification model is trained on the ImageNet-1K dataset.
## Training procedure
## Evaluation results
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
MBZUAI/swiftformer-l3
|
# SwiftFormer (swiftformer-l3)
## Model description
The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.
## Intended uses & limitations
## How to use
import requests
from PIL import Image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
from transformers import ViTImageProcessor
processor = ViTImageProcessor.from_pretrained('shehan97/swiftformer-l3')
inputs = processor(images=image, return_tensors="pt")
from transformers.models.swiftformer import SwiftFormerForImageClassification
new_model = SwiftFormerForImageClassification.from_pretrained('shehan97/swiftformer-l3')
output = new_model(inputs['pixel_values'], output_hidden_states=True)
logits = output.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", new_model.config.id2label[predicted_class_idx])
## Limitations and bias
## Training data
The classification model is trained on the ImageNet-1K dataset.
## Training procedure
## Evaluation results
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
IThinkUPC/autotrain-meleg_car_parts-47031120543
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 47031120543
- CO2 Emissions (in grams): 0.0081
## Validation Metrics
- Loss: 0.044
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000
|
[
"door",
"rack"
] |
alicelouis/BeiT_NSCLC_lr2e-5
|
**This model is pre-trained from Beit-base-patch16-224**
DataSets
- Non-small carcinoma dataset. (2020). Chest CT-Scan images Dataset. https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images
- Normal CT scan slice. (2021). Large COVID-19 CT scan slice dataset. https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset
- NSCLC-Radiomics. (2023). The Cancer Imaging Archive (TCIA) Public Access. https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics
Developers
- Phakkhaphon Artburai
- Santipab Tongchan
- Natthawee Naewkumpol
|
[
"adenocarcinoma",
"large.cell",
"normal",
"squamous.cell"
] |
Nox004/vit-base-beans
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0226
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3365 | 0.38 | 50 | 0.2455 | 0.9323 |
| 0.1728 | 0.77 | 100 | 0.1544 | 0.9549 |
| 0.1519 | 1.15 | 150 | 0.1072 | 0.9624 |
| 0.0209 | 1.54 | 200 | 0.1594 | 0.9624 |
| 0.0206 | 1.92 | 250 | 0.0913 | 0.9699 |
| 0.0135 | 2.31 | 300 | 0.1488 | 0.9624 |
| 0.0079 | 2.69 | 350 | 0.0226 | 0.9925 |
| 0.0074 | 3.08 | 400 | 0.0582 | 0.9925 |
| 0.0064 | 3.46 | 450 | 0.0984 | 0.9774 |
| 0.0061 | 3.85 | 500 | 0.1151 | 0.9699 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
DanielDo/autotrain-segundaentrega-50524120657
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 50524120657
- CO2 Emissions (in grams): 0.0082
## Validation Metrics
- Loss: 0.299
- Accuracy: 0.900
- Precision: 0.931
- Recall: 0.900
- AUC: 0.952
- F1: 0.915
|
[
"normal",
"pneumonia"
] |
Kisax/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6510
- Accuracy: 0.879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7286 | 0.99 | 62 | 2.5619 | 0.828 |
| 1.8685 | 2.0 | 125 | 1.8156 | 0.866 |
| 1.6392 | 2.98 | 186 | 1.6510 | 0.879 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
andrewgray11/autotrain-solar-panel-object-detection-50559120777
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50559120777
- CO2 Emissions (in grams): 0.8787
## Validation Metrics
- Loss: 0.033
- Accuracy: 0.992
- Macro F1: 0.641
- Micro F1: 0.992
- Weighted F1: 0.989
- Macro Precision: 0.619
- Micro Precision: 0.992
- Weighted Precision: 0.986
- Macro Recall: 0.667
- Micro Recall: 0.992
- Weighted Recall: 0.992
|
[
"test",
"train",
"valid"
] |
suatatan/autotrain-red-arrow-finder-50583120813
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 50583120813
- CO2 Emissions (in grams): 0.3292
## Validation Metrics
- Loss: 0.602
- Accuracy: 0.750
- Precision: 0.812
- Recall: 0.812
- AUC: 0.793
- F1: 0.812
|
[
"labeled",
"normal"
] |
ashduino101/furry-species-classification
|
# Species Classification in Furry Art
This model was trained on ~16k images (filtered from ~40k).
I may train it on a larger dataset in the future.
The model is capable of classifying about 86 species,
including a few more common Pokemon, as well as some
mythological and fictional species. The results may
differ depending on the species, though the model is
quite good with common ones such as foxes and wolves.
## How to Use
**Example using Pipelines (recommended):**
```py
from transformers import pipeline
classify = pipeline('image-classification', model='ashduino101/furry-species-classification')
scores = classify('your-image.png')
# Do something with the scores
```
**Example using AutoModelForImageClassification:**
```py
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
image_processor = AutoImageProcessor.from_pretrained('ashduino101/furry-species-classification')
inputs = image_processor(Image.open('your-image.png'), return_tensors='pt')
model = AutoModelForImageClassification.from_pretrained("ashduino101/furry-species-classification")
with torch.no_grad():
result = model(**inputs)
# Do something with the result
```
|
[
"alien",
"angel",
"centaur",
"chicken",
"chipmunk",
"coyote",
"cyborg",
"cyclops",
"deer",
"demon",
"dinosaur",
"dolphin",
"antelope",
"donkey",
"dragon",
"eevee",
"elf",
"fairy",
"feline",
"fish",
"flareon",
"fox",
"frog",
"ape",
"gazelle",
"ghost",
"giraffe",
"glaceon",
"goat",
"gryphon",
"hamster",
"hedgehog",
"horse",
"human",
"badger",
"husky",
"hyena",
"imp",
"jackal",
"jaguar",
"jolteon",
"kangaroo",
"kobold",
"lemur",
"lion",
"bat_pony",
"lizard",
"lucario",
"meerkat",
"mollusk",
"monkey",
"monster",
"mouse",
"otter",
"pegasus",
"pikachu",
"bear",
"pony",
"rabbit",
"raccoon",
"rat",
"reindeer",
"renamon",
"robot",
"rodent",
"satyr",
"sergal",
"bird",
"shark",
"sheep",
"sheepdog",
"skunk",
"snake",
"sylveon",
"tiger",
"troll",
"turtle",
"umbreon",
"braixen",
"unicorn",
"vaporeon",
"weasel",
"werewolf",
"wolf",
"yoshi",
"cattle"
] |
carolinetfls/plant-seedlings-model-aug-3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-aug-3
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2365
- eval_accuracy: 0.9613
- eval_runtime: 29.5637
- eval_samples_per_second: 61.224
- eval_steps_per_second: 7.678
- epoch: 23.4
- step: 4400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 24
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
carolinetfls/plant-seedlings-model-freeze-1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-freeze-1
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2053
- Accuracy: 0.9618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4645 | 0.8 | 100 | 0.3145 | 0.8965 |
| 0.1731 | 1.6 | 200 | 0.2368 | 0.9188 |
| 0.1494 | 2.4 | 300 | 0.2755 | 0.9196 |
| 0.0819 | 3.2 | 400 | 0.1742 | 0.9530 |
| 0.0847 | 4.0 | 500 | 0.2245 | 0.9459 |
| 0.1155 | 4.8 | 600 | 0.4147 | 0.9140 |
| 0.0406 | 5.6 | 700 | 0.1918 | 0.9522 |
| 0.005 | 6.4 | 800 | 0.2489 | 0.9451 |
| 0.0003 | 7.2 | 900 | 0.2258 | 0.9530 |
| 0.0002 | 8.0 | 1000 | 0.2189 | 0.9570 |
| 0.0001 | 8.8 | 1100 | 0.2014 | 0.9618 |
| 0.0001 | 9.6 | 1200 | 0.2009 | 0.9626 |
| 0.0001 | 10.4 | 1300 | 0.2026 | 0.9610 |
| 0.0001 | 11.2 | 1400 | 0.2029 | 0.9610 |
| 0.0001 | 12.0 | 1500 | 0.2035 | 0.9618 |
| 0.0001 | 12.8 | 1600 | 0.2042 | 0.9626 |
| 0.0001 | 13.6 | 1700 | 0.2046 | 0.9618 |
| 0.0001 | 14.4 | 1800 | 0.2049 | 0.9618 |
| 0.0001 | 15.2 | 1900 | 0.2052 | 0.9626 |
| 0.0001 | 16.0 | 2000 | 0.2053 | 0.9618 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
Jayahari/ViT_transformer_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
DunnBC22/dit-base-Business_Documents_Classified_v2
|
# dit-base-Business_Documents_Classified_v2
This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6715
- Accuracy: 0.826
- Weighted f1: 0.8272
- Micro f1: 0.826
- Macro f1: 0.8242
- Weighted recall: 0.826
- Micro recall: 0.826
- Macro recall: 0.8237
- Weighted precision: 0.8327
- Micro precision: 0.826
- Macro precision: 0.8293
## Model description
This is a classification model of 16 different types of documents.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Real%20World%20Documents%20Collections/Real%20World%20Documents%20Collections_v2.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/shaz13/real-world-documents-collections
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 18
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 2.7266 | 0.99 | 31 | 2.4738 | 0.208 | 0.1811 | 0.208 | 0.1827 | 0.208 | 0.208 | 0.2101 | 0.2143 | 0.208 | 0.2246 |
| 2.171 | 1.98 | 62 | 1.8510 | 0.423 | 0.3936 | 0.4230 | 0.3925 | 0.423 | 0.423 | 0.4243 | 0.4503 | 0.423 | 0.4446 |
| 1.6525 | 2.98 | 93 | 1.2633 | 0.61 | 0.5884 | 0.61 | 0.5855 | 0.61 | 0.61 | 0.6124 | 0.6377 | 0.61 | 0.6283 |
| 1.346 | 4.0 | 125 | 1.0259 | 0.706 | 0.7023 | 0.706 | 0.6992 | 0.706 | 0.706 | 0.7058 | 0.7095 | 0.706 | 0.7034 |
| 1.253 | 4.99 | 156 | 0.9180 | 0.729 | 0.7277 | 0.729 | 0.7239 | 0.729 | 0.729 | 0.7291 | 0.7340 | 0.729 | 0.7261 |
| 1.0975 | 5.98 | 187 | 0.8859 | 0.747 | 0.7480 | 0.747 | 0.7437 | 0.747 | 0.747 | 0.7472 | 0.7609 | 0.747 | 0.7526 |
| 1.1122 | 6.98 | 218 | 0.8270 | 0.76 | 0.7606 | 0.76 | 0.7578 | 0.76 | 0.76 | 0.7594 | 0.7772 | 0.76 | 0.7727 |
| 1.0365 | 8.0 | 250 | 0.7806 | 0.775 | 0.7759 | 0.775 | 0.7730 | 0.775 | 0.775 | 0.7735 | 0.7957 | 0.775 | 0.7920 |
| 1.004 | 8.99 | 281 | 0.7472 | 0.796 | 0.7977 | 0.796 | 0.7957 | 0.796 | 0.796 | 0.7956 | 0.8193 | 0.796 | 0.8151 |
| 0.9278 | 9.98 | 312 | 0.7296 | 0.795 | 0.7974 | 0.795 | 0.7957 | 0.795 | 0.795 | 0.7953 | 0.8157 | 0.795 | 0.8115 |
| 0.8767 | 10.98 | 343 | 0.7257 | 0.809 | 0.8101 | 0.809 | 0.8078 | 0.809 | 0.809 | 0.8091 | 0.8182 | 0.809 | 0.8136 |
| 0.8656 | 12.0 | 375 | 0.6875 | 0.814 | 0.8137 | 0.8140 | 0.8106 | 0.814 | 0.814 | 0.8122 | 0.8207 | 0.814 | 0.8164 |
| 0.7905 | 12.99 | 406 | 0.7060 | 0.808 | 0.8093 | 0.808 | 0.8071 | 0.808 | 0.808 | 0.8068 | 0.8182 | 0.808 | 0.8145 |
| 0.8804 | 13.98 | 437 | 0.6849 | 0.82 | 0.8214 | 0.82 | 0.8183 | 0.82 | 0.82 | 0.8183 | 0.8260 | 0.82 | 0.8215 |
| 0.8265 | 14.98 | 468 | 0.6821 | 0.816 | 0.8171 | 0.816 | 0.8143 | 0.816 | 0.816 | 0.8142 | 0.8242 | 0.816 | 0.8206 |
| 0.7929 | 16.0 | 500 | 0.6877 | 0.818 | 0.8184 | 0.818 | 0.8152 | 0.818 | 0.818 | 0.8167 | 0.8240 | 0.818 | 0.8186 |
| 0.7993 | 16.99 | 531 | 0.6718 | 0.825 | 0.8259 | 0.825 | 0.8234 | 0.825 | 0.825 | 0.8227 | 0.8306 | 0.825 | 0.8282 |
| 0.7954 | 17.86 | 558 | 0.6715 | 0.826 | 0.8272 | 0.826 | 0.8242 | 0.826 | 0.826 | 0.8237 | 0.8327 | 0.826 | 0.8293 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"specification",
"email",
"advertisement",
"handwritten",
"scientific_report",
"budget",
"scientific_publication",
"presentation",
"file_folder",
"memo",
"resume",
"invoice",
"letter",
"questionnaire",
"form",
"news_article"
] |
Lakera/autotrain-cancer-lakera-50807121081
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50807121081
- CO2 Emissions (in grams): 0.0092
## Validation Metrics
- Loss: 0.051
- Accuracy: 0.987
- Macro F1: 0.984
- Micro F1: 0.987
- Weighted F1: 0.987
- Macro Precision: 0.984
- Micro Precision: 0.987
- Weighted Precision: 0.987
- Macro Recall: 0.984
- Micro Recall: 0.987
- Weighted Recall: 0.987
|
[
"actinic_keratoses",
"basal_cell_carcinoma",
"benign_keratosis-like_lesions"
] |
Lakera/autotrain-cancer-lakera-50807121083
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50807121083
- CO2 Emissions (in grams): 2.4235
## Validation Metrics
- Loss: 0.999
- Accuracy: 0.507
- Macro F1: 0.347
- Micro F1: 0.507
- Weighted F1: 0.397
- Macro Precision: 0.626
- Micro Precision: 0.507
- Weighted Precision: 0.600
- Macro Recall: 0.409
- Micro Recall: 0.507
- Weighted Recall: 0.507
|
[
"actinic_keratoses",
"basal_cell_carcinoma",
"benign_keratosis-like_lesions"
] |
Lakera/autotrain-cancer-lakera-50807121082
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50807121082
- CO2 Emissions (in grams): 3.0179
## Validation Metrics
- Loss: 0.034
- Accuracy: 0.993
- Macro F1: 0.992
- Micro F1: 0.993
- Weighted F1: 0.993
- Macro Precision: 0.992
- Micro Precision: 0.993
- Weighted Precision: 0.993
- Macro Recall: 0.992
- Micro Recall: 0.993
- Weighted Recall: 0.993
|
[
"actinic_keratoses",
"basal_cell_carcinoma",
"benign_keratosis-like_lesions"
] |
Lakera/autotrain-cancer-lakera-50807121084
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50807121084
- CO2 Emissions (in grams): 2.6500
## Validation Metrics
- Loss: 0.052
- Accuracy: 0.980
- Macro F1: 0.980
- Micro F1: 0.980
- Weighted F1: 0.980
- Macro Precision: 0.986
- Micro Precision: 0.980
- Weighted Precision: 0.981
- Macro Recall: 0.976
- Micro Recall: 0.980
- Weighted Recall: 0.980
|
[
"actinic_keratoses",
"basal_cell_carcinoma",
"benign_keratosis-like_lesions"
] |
Lakera/autotrain-cancer-lakera-50807121085
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50807121085
- CO2 Emissions (in grams): 0.0173
## Validation Metrics
- Loss: 0.039
- Accuracy: 0.973
- Macro F1: 0.971
- Micro F1: 0.973
- Weighted F1: 0.973
- Macro Precision: 0.974
- Micro Precision: 0.973
- Weighted Precision: 0.973
- Macro Recall: 0.968
- Micro Recall: 0.973
- Weighted Recall: 0.973
|
[
"actinic_keratoses",
"basal_cell_carcinoma",
"benign_keratosis-like_lesions"
] |
geovanyuribe/platzi-vit-model-geovany-uribe
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-geovany-uribe
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0277
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1416 | 3.85 | 500 | 0.0277 | 0.9850 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
jimli0816/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jimli0816/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3713
- Validation Loss: 0.3560
- Train Accuracy: 0.902
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7850 | 1.6194 | 0.85 | 0 |
| 1.2053 | 0.7987 | 0.906 | 1 |
| 0.6998 | 0.5437 | 0.891 | 2 |
| 0.4879 | 0.4149 | 0.91 | 3 |
| 0.3713 | 0.3560 | 0.902 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
FatihC/swin-tiny-patch4-window7-224-finetuned-eurosat-watermark
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1211
- Accuracy: 0.9609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.4862 | 0.8516 |
| No log | 2.0 | 8 | 0.4103 | 0.8828 |
| 0.4518 | 3.0 | 12 | 0.3210 | 0.8984 |
| 0.4518 | 4.0 | 16 | 0.2053 | 0.9375 |
| 0.2909 | 5.0 | 20 | 0.1675 | 0.9453 |
| 0.2909 | 6.0 | 24 | 0.1439 | 0.9531 |
| 0.2909 | 7.0 | 28 | 0.1448 | 0.9297 |
| 0.1492 | 8.0 | 32 | 0.1798 | 0.9531 |
| 0.1492 | 9.0 | 36 | 0.1360 | 0.9453 |
| 0.1161 | 10.0 | 40 | 0.1670 | 0.9531 |
| 0.1161 | 11.0 | 44 | 0.1637 | 0.9531 |
| 0.1161 | 12.0 | 48 | 0.1298 | 0.9531 |
| 0.1053 | 13.0 | 52 | 0.1162 | 0.9531 |
| 0.1053 | 14.0 | 56 | 0.1353 | 0.9531 |
| 0.0839 | 15.0 | 60 | 0.1211 | 0.9609 |
| 0.0839 | 16.0 | 64 | 0.1113 | 0.9609 |
| 0.0839 | 17.0 | 68 | 0.1145 | 0.9609 |
| 0.0689 | 18.0 | 72 | 0.1239 | 0.9531 |
| 0.0689 | 19.0 | 76 | 0.1280 | 0.9531 |
| 0.0581 | 20.0 | 80 | 0.1533 | 0.9531 |
| 0.0581 | 21.0 | 84 | 0.1323 | 0.9609 |
| 0.0581 | 22.0 | 88 | 0.1327 | 0.9531 |
| 0.0545 | 23.0 | 92 | 0.1529 | 0.9531 |
| 0.0545 | 24.0 | 96 | 0.1357 | 0.9531 |
| 0.046 | 25.0 | 100 | 0.1333 | 0.9531 |
| 0.046 | 26.0 | 104 | 0.1466 | 0.9531 |
| 0.046 | 27.0 | 108 | 0.1300 | 0.9531 |
| 0.0421 | 28.0 | 112 | 0.1077 | 0.9609 |
| 0.0421 | 29.0 | 116 | 0.0985 | 0.9609 |
| 0.0371 | 30.0 | 120 | 0.1186 | 0.9531 |
| 0.0371 | 31.0 | 124 | 0.1123 | 0.9531 |
| 0.0371 | 32.0 | 128 | 0.1144 | 0.9531 |
| 0.0348 | 33.0 | 132 | 0.1276 | 0.9531 |
| 0.0348 | 34.0 | 136 | 0.1488 | 0.9531 |
| 0.0211 | 35.0 | 140 | 0.1560 | 0.9531 |
| 0.0211 | 36.0 | 144 | 0.1477 | 0.9531 |
| 0.0211 | 37.0 | 148 | 0.1488 | 0.9531 |
| 0.0274 | 38.0 | 152 | 0.1467 | 0.9531 |
| 0.0274 | 39.0 | 156 | 0.1401 | 0.9531 |
| 0.0259 | 40.0 | 160 | 0.1379 | 0.9531 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"normal",
"watermark"
] |
juanesbch/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juanesbch/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3619
- Validation Loss: 0.3258
- Train Accuracy: 0.922
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8157 | 1.6429 | 0.831 | 0 |
| 1.2314 | 0.7909 | 0.912 | 1 |
| 0.7017 | 0.4982 | 0.916 | 2 |
| 0.4934 | 0.3987 | 0.914 | 3 |
| 0.3619 | 0.3258 | 0.922 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
carolinetfls/plant-seedlings-model-freeze-2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-freeze-2
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1729
- Accuracy: 0.9720
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0346 | 0.25 | 100 | 0.2971 | 0.9573 |
| 0.158 | 0.51 | 200 | 0.3343 | 0.9452 |
| 0.0218 | 0.76 | 300 | 0.5939 | 0.9051 |
| 0.0525 | 1.02 | 400 | 0.3253 | 0.9446 |
| 0.0177 | 1.27 | 500 | 0.3484 | 0.9465 |
| 0.034 | 1.53 | 600 | 0.1919 | 0.9637 |
| 0.001 | 1.78 | 700 | 0.1797 | 0.9643 |
| 0.0093 | 2.04 | 800 | 0.1854 | 0.9682 |
| 0.0598 | 2.29 | 900 | 0.2082 | 0.9624 |
| 0.0008 | 2.54 | 1000 | 0.2089 | 0.9567 |
| 0.0501 | 2.8 | 1100 | 0.2640 | 0.9567 |
| 0.0015 | 3.05 | 1200 | 0.1899 | 0.9650 |
| 0.0431 | 3.31 | 1300 | 0.1866 | 0.9669 |
| 0.007 | 3.56 | 1400 | 0.1962 | 0.9707 |
| 0.005 | 3.82 | 1500 | 0.2161 | 0.9624 |
| 0.0009 | 4.07 | 1600 | 0.1618 | 0.9656 |
| 0.0011 | 4.33 | 1700 | 0.1340 | 0.9662 |
| 0.0008 | 4.58 | 1800 | 0.1606 | 0.9688 |
| 0.0221 | 4.83 | 1900 | 0.1498 | 0.9707 |
| 0.0085 | 5.09 | 2000 | 0.2956 | 0.9490 |
| 0.0018 | 5.34 | 2100 | 0.1322 | 0.9745 |
| 0.0002 | 5.6 | 2200 | 0.2376 | 0.9592 |
| 0.0285 | 5.85 | 2300 | 0.1476 | 0.9707 |
| 0.0001 | 6.11 | 2400 | 0.1968 | 0.9618 |
| 0.0 | 6.36 | 2500 | 0.1780 | 0.9656 |
| 0.0001 | 6.62 | 2600 | 0.1731 | 0.9682 |
| 0.0 | 6.87 | 2700 | 0.1729 | 0.9694 |
| 0.0 | 7.12 | 2800 | 0.1684 | 0.9713 |
| 0.0 | 7.38 | 2900 | 0.1692 | 0.9713 |
| 0.0 | 7.63 | 3000 | 0.1699 | 0.9713 |
| 0.0 | 7.89 | 3100 | 0.1708 | 0.9713 |
| 0.0 | 8.14 | 3200 | 0.1708 | 0.9720 |
| 0.0 | 8.4 | 3300 | 0.1712 | 0.9720 |
| 0.0 | 8.65 | 3400 | 0.1718 | 0.9720 |
| 0.0 | 8.91 | 3500 | 0.1721 | 0.9720 |
| 0.0 | 9.16 | 3600 | 0.1725 | 0.9720 |
| 0.0 | 9.41 | 3700 | 0.1727 | 0.9720 |
| 0.0 | 9.67 | 3800 | 0.1728 | 0.9720 |
| 0.0 | 9.92 | 3900 | 0.1729 | 0.9720 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
juanArevalo/autotrain-classificacion-51168121451
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51168121451
- CO2 Emissions (in grams): 0.0028
## Validation Metrics
- Loss: 0.226
- Accuracy: 0.910
- Macro F1: 0.911
- Micro F1: 0.910
- Weighted F1: 0.911
- Macro Precision: 0.918
- Micro Precision: 0.910
- Weighted Precision: 0.918
- Macro Recall: 0.910
- Micro Recall: 0.910
- Weighted Recall: 0.910
|
[
"ak",
"ala_idris",
"buzgulu",
"dimnit",
"nazli"
] |
juanArevalo/autotrain-classificacion-51168121453
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51168121453
- CO2 Emissions (in grams): 0.3089
## Validation Metrics
- Loss: 1.587
- Accuracy: 0.360
- Macro F1: 0.320
- Micro F1: 0.360
- Weighted F1: 0.320
- Macro Precision: 0.289
- Micro Precision: 0.360
- Weighted Precision: 0.289
- Macro Recall: 0.360
- Micro Recall: 0.360
- Weighted Recall: 0.360
|
[
"ak",
"ala_idris",
"buzgulu",
"dimnit",
"nazli"
] |
juanArevalo/autotrain-classificacion-51168121452
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51168121452
- CO2 Emissions (in grams): 0.7665
## Validation Metrics
- Loss: 0.248
- Accuracy: 0.920
- Macro F1: 0.920
- Micro F1: 0.920
- Weighted F1: 0.920
- Macro Precision: 0.921
- Micro Precision: 0.920
- Weighted Precision: 0.921
- Macro Recall: 0.920
- Micro Recall: 0.920
- Weighted Recall: 0.920
|
[
"ak",
"ala_idris",
"buzgulu",
"dimnit",
"nazli"
] |
juanArevalo/autotrain-classificacion-51168121455
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51168121455
- CO2 Emissions (in grams): 0.0041
## Validation Metrics
- Loss: 0.145
- Accuracy: 0.960
- Macro F1: 0.960
- Micro F1: 0.960
- Weighted F1: 0.960
- Macro Precision: 0.962
- Micro Precision: 0.960
- Weighted Precision: 0.962
- Macro Recall: 0.960
- Micro Recall: 0.960
- Weighted Recall: 0.960
|
[
"ak",
"ala_idris",
"buzgulu",
"dimnit",
"nazli"
] |
juanArevalo/autotrain-classificacion-51168121454
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51168121454
- CO2 Emissions (in grams): 0.7545
## Validation Metrics
- Loss: 0.155
- Accuracy: 0.960
- Macro F1: 0.960
- Micro F1: 0.960
- Weighted F1: 0.960
- Macro Precision: 0.961
- Micro Precision: 0.960
- Weighted Precision: 0.961
- Macro Recall: 0.960
- Micro Recall: 0.960
- Weighted Recall: 0.960
|
[
"ak",
"ala_idris",
"buzgulu",
"dimnit",
"nazli"
] |
platzi/platzi-vit-model-saul-burgos
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-saul-burgos
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0683
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0586 | 3.85 | 500 | 0.0683 | 0.9850 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
platzi/platzi-vit-model-Santiago-Garcia
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-Santiago-Garcia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0881
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1371 | 3.85 | 500 | 0.0881 | 0.9774 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
platzi/platzi-vit-model-Santiago-Garcia-Solarte
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-Santiago-Garcia-Solarte
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0276
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1434 | 3.85 | 500 | 0.0276 | 0.9925 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
platzi/platzi-vit-model-Jonathan-Castillo
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-Jonathan-Castillo
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0578
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1875 | 3.85 | 500 | 0.0941 | 0.9699 |
| 0.0064 | 7.69 | 1000 | 0.0578 | 0.9850 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Snarci/ViT-base-patch16-384-Chaoyang-from-scratch
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model trained on the [Chaoyang dataset](https://paperswithcode.com/dataset/chaoyang) at resolution 384x384, using a fixed 10% of the training set as the validation set and evaluated on the official test set using the best validation model based on the loss
# Augmentation pipeline
To address the issue of class imbalance in our training set, we performed oversampling with repetition.
Specifically, we duplicated the minority classes images until we obtained an even distribution across all classes.
This resulted in a larger training set, but ensured that our model was exposed to an equal number of samples from each class during training.
We verified that this approach did not lead to overfitting or other issues by using a validation set with the original class distribution.
We used the following [Albumentations](https://github.com/albumentations-team/albumentations)augmentation pipeline for our experiments:
- A.Resize(img_size, img_size),
- A.HorizontalFlip(p=0.5),
- A.VerticalFlip(p=0.5),
- A.RandomRotate90(p=0.5),
- A.RandomResizedCrop(img_size, img_size, scale=(0.5, 1.0), p=0.5),
- ToTensorV2(p=1.0)
This pipeline consists of the following transformations:
- Resize: resizes the image to a fixed size of (img_size, img_size).
- HorizontalFlip: flips the image horizontally with a probability of 0.5.
- VerticalFlip: flips the image vertically with a probability of 0.5.
- RandomRotate90: randomly rotates the image by 90, 180, or 270 degrees with a probability of 0.5.
- RandomResizedCrop: randomly crops and resizes the image to a size between 50% and 100% of the original size, with a probability of 0.5.
- ToTensorV2: converts the image to a PyTorch tensor.
These transformations were chosen to augment the dataset with a variety of geometric transformations, while preserving important visual features.
# Results
Our model represents the current state-of-the-art in the field, as it outperforms previous state-of-the-art models proposed in papers with code,
based on the dataset's [reference paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9600806&tag=1).
The results are summarized in the following table using macro avg metrics.
| Model | Accuracy | F1-Score | Precision | Recall |
|----------------------------|-------------|-------------|-------------|-------------|
| Baseline | 0.83 | 0.77 | 0.78 | 0.75 |
| Vit-384-finetuned | 0.86 ↑3% | 0.81 ↑4% | 0.82 ↑4% | 0.80 ↑5% |
| Vit-384-from-scratch | 0.78 | 0.74 | 0.74 | 0.74 |
| Vit-224-distilled-resnet50 | 0.74 | 0.00 | 0.00 | 0.00 |
### How to use
Here is how to use this model to classify an image of the Chaoyang dataset into one of the 4 classes:
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('Snarci/ViT-base-patch16-384-Chaoyang-finetuned')
model = ViTForImageClassification.from_pretrained('Snarci/ViT-base-patch16-384-Chaoyang-finetuned')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 4 Chaoyang classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was tuned on the [Chaoyang dataset](https://paperswithcode.com/dataset/chaoyang) at resolution 384x384, using a fixed 10% of the training set as the validation set
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution 384x384 during training and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
# License
This model is provided for non-commercial use only and may not be used in any research or publication without prior written consent from the author.
|
[
"normal",
"serrated",
"adenocarcinoma",
"adenoma"
] |
thean/backup
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-finetuned-thai
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4391
- Accuracy: 0.8738
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.6781 | 0.99 | 47 | 0.5475 | 1.8040 |
| 1.3191 | 1.99 | 94 | 0.745 | 0.9501 |
| 1.078 | 2.98 | 141 | 0.7969 | 0.7767 |
| 0.9125 | 3.99 | 188 | 0.6060 | 0.8406 |
| 0.7527 | 4.99 | 235 | 0.5214 | 0.8575 |
| 0.6852 | 5.98 | 282 | 0.4588 | 0.8656 |
| 0.6233 | 6.98 | 329 | 0.4391 | 0.8738 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"bittermelonsoup",
"boopadpongali",
"curriedfishcake",
"dumpling",
"eggsstewed",
"friedchicken",
"friedkale",
"friedmusselpancakes",
"gaengjued",
"gaengkeawwan",
"gaiyang",
"goongobwoonsen",
"goongpao",
"grilledqquid",
"hoykraeng",
"hoylaiprikpao",
"joke",
"kaijeowmoosaap",
"kaithoon",
"kaomangai",
"kaomoodang",
"khanomjeennamyakati",
"khaomokgai",
"khaomootodgratiem",
"khaoniewmamuang",
"kkaoklukkaphi",
"kormooyang",
"kuakling",
"kuayjab",
"kuayteowreua",
"larbmoo",
"massamangai",
"moosatay",
"namtokmoo",
"padpakbung",
"padpakruammit",
"padthai",
"padyordmala",
"phatkaphrao",
"porkstickynoodles",
"roast_duck",
"roast_fish",
"somtam",
"soninlaweggs",
"stewedporkleg",
"suki",
"tomkhagai",
"tomyumgoong",
"yamwoonsen",
"yentafo"
] |
thean/swinv2-tiny-patch4-window8-256-finetuned-THFOOD-50
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-finetuned-THFOOD-50
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the [THFOOD-50](https://huggingface.co/datasets/thean/THFOOD-50) dataset.
It achieves the following results on the:
Train set
- Loss: 0.1669
- Accuracy: 0.9557
Validation set
- Loss: 0.2535
- Accuracy: 0.9344
Test set
- Loss: 0.2669
- Accuracy: 0.9292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6558 | 0.99 | 47 | 3.1956 | 0.28 |
| 1.705 | 1.99 | 94 | 1.1701 | 0.6787 |
| 0.9805 | 2.98 | 141 | 0.6492 | 0.8125 |
| 0.7925 | 4.0 | 189 | 0.4724 | 0.8644 |
| 0.6169 | 4.99 | 236 | 0.4129 | 0.8738 |
| 0.5343 | 5.99 | 283 | 0.3717 | 0.8825 |
| 0.5196 | 6.98 | 330 | 0.3654 | 0.8906 |
| 0.5059 | 8.0 | 378 | 0.3267 | 0.8969 |
| 0.4432 | 8.99 | 425 | 0.2996 | 0.9081 |
| 0.3819 | 9.99 | 472 | 0.3056 | 0.9087 |
| 0.3627 | 10.98 | 519 | 0.2796 | 0.9213 |
| 0.3505 | 12.0 | 567 | 0.2753 | 0.915 |
| 0.3224 | 12.99 | 614 | 0.2830 | 0.9206 |
| 0.3206 | 13.99 | 661 | 0.2797 | 0.9231 |
| 0.3141 | 14.98 | 708 | 0.2569 | 0.9287 |
| 0.2946 | 16.0 | 756 | 0.2582 | 0.9319 |
| 0.3008 | 16.99 | 803 | 0.2583 | 0.9337 |
| 0.2356 | 17.99 | 850 | 0.2567 | 0.9281 |
| 0.2954 | 18.98 | 897 | 0.2581 | 0.9319 |
| 0.2628 | 19.89 | 940 | 0.2535 | 0.9344 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"bittermelonsoup",
"boopadpongali",
"curriedfishcake",
"dumpling",
"eggsstewed",
"friedchicken",
"friedkale",
"friedmusselpancakes",
"gaengjued",
"gaengkeawwan",
"gaiyang",
"goongobwoonsen",
"goongpao",
"grilledqquid",
"hoykraeng",
"hoylaiprikpao",
"joke",
"kaijeowmoosaap",
"kaithoon",
"kaomangai",
"kaomoodang",
"khanomjeennamyakati",
"khaomokgai",
"khaomootodgratiem",
"khaoniewmamuang",
"kkaoklukkaphi",
"kormooyang",
"kuakling",
"kuayjab",
"kuayteowreua",
"larbmoo",
"massamangai",
"moosatay",
"namtokmoo",
"padpakbung",
"padpakruammit",
"padthai",
"padyordmala",
"phatkaphrao",
"porkstickynoodles",
"roast_duck",
"roast_fish",
"somtam",
"soninlaweggs",
"stewedporkleg",
"suki",
"tomkhagai",
"tomyumgoong",
"yamwoonsen",
"yentafo"
] |
carolinetfls/plant-seedlings-resnet-50
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-resnet-50
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4375
- Accuracy: 0.8492
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3644 | 0.53 | 100 | 2.3284 | 0.2294 |
| 1.8235 | 1.06 | 200 | 1.8020 | 0.4396 |
| 1.4216 | 1.6 | 300 | 1.2533 | 0.6341 |
| 1.1626 | 2.13 | 400 | 0.8825 | 0.7186 |
| 0.9175 | 2.66 | 500 | 0.7782 | 0.7598 |
| 0.819 | 3.19 | 600 | 0.6772 | 0.7814 |
| 0.7846 | 3.72 | 700 | 0.6027 | 0.8006 |
| 0.6403 | 4.26 | 800 | 0.5450 | 0.8148 |
| 0.6702 | 4.79 | 900 | 0.5621 | 0.8119 |
| 0.6038 | 5.32 | 1000 | 0.4854 | 0.8374 |
| 0.7048 | 5.85 | 1100 | 0.4793 | 0.8325 |
| 0.4157 | 6.38 | 1200 | 0.4724 | 0.8345 |
| 0.4833 | 6.91 | 1300 | 0.4614 | 0.8404 |
| 0.5064 | 7.45 | 1400 | 0.4901 | 0.8340 |
| 0.4966 | 7.98 | 1500 | 0.4365 | 0.8522 |
| 0.5718 | 8.51 | 1600 | 0.4375 | 0.8423 |
| 0.41 | 9.04 | 1700 | 0.4322 | 0.8536 |
| 0.4956 | 9.57 | 1800 | 0.4375 | 0.8492 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
carolinetfls/plant-seedlings-freeze-0-6-aug-3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-freeze-0-6-aug-3
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2030
- Accuracy: 0.9401
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5655 | 0.2 | 100 | 0.7734 | 0.7520 |
| 0.663 | 0.39 | 200 | 0.5191 | 0.8276 |
| 0.4009 | 0.59 | 300 | 0.6602 | 0.7706 |
| 0.6175 | 0.79 | 400 | 0.5263 | 0.8129 |
| 0.2571 | 0.98 | 500 | 0.5162 | 0.8502 |
| 0.3698 | 1.18 | 600 | 0.4021 | 0.8684 |
| 0.4092 | 1.38 | 700 | 0.3559 | 0.8787 |
| 0.2506 | 1.57 | 800 | 0.4771 | 0.8571 |
| 0.2597 | 1.77 | 900 | 0.4663 | 0.8517 |
| 0.4016 | 1.96 | 1000 | 0.3367 | 0.8802 |
| 0.2899 | 2.16 | 1100 | 0.3276 | 0.8959 |
| 0.2713 | 2.36 | 1200 | 0.3035 | 0.8988 |
| 0.2707 | 2.55 | 1300 | 0.3661 | 0.8846 |
| 0.4251 | 2.75 | 1400 | 0.3720 | 0.8669 |
| 0.1669 | 2.95 | 1500 | 0.3323 | 0.8870 |
| 0.3079 | 3.14 | 1600 | 0.3322 | 0.8875 |
| 0.2596 | 3.34 | 1700 | 0.3666 | 0.8969 |
| 0.3019 | 3.54 | 1800 | 0.2772 | 0.9023 |
| 0.3429 | 3.73 | 1900 | 0.2936 | 0.9037 |
| 0.2508 | 3.93 | 2000 | 0.3525 | 0.8846 |
| 0.2212 | 4.13 | 2100 | 0.3199 | 0.8934 |
| 0.3104 | 4.32 | 2200 | 0.3546 | 0.8915 |
| 0.1682 | 4.52 | 2300 | 0.2823 | 0.8939 |
| 0.2014 | 4.72 | 2400 | 0.2813 | 0.9150 |
| 0.2805 | 4.91 | 2500 | 0.2907 | 0.9077 |
| 0.1471 | 5.11 | 2600 | 0.2811 | 0.9091 |
| 0.179 | 5.3 | 2700 | 0.2752 | 0.9106 |
| 0.1992 | 5.5 | 2800 | 0.2894 | 0.9072 |
| 0.1635 | 5.7 | 2900 | 0.2397 | 0.9194 |
| 0.2045 | 5.89 | 3000 | 0.2717 | 0.9037 |
| 0.1893 | 6.09 | 3100 | 0.2339 | 0.9273 |
| 0.2664 | 6.29 | 3200 | 0.2772 | 0.9131 |
| 0.1991 | 6.48 | 3300 | 0.2475 | 0.9234 |
| 0.0713 | 6.68 | 3400 | 0.2509 | 0.9185 |
| 0.1968 | 6.88 | 3500 | 0.2410 | 0.9194 |
| 0.1378 | 7.07 | 3600 | 0.2177 | 0.9288 |
| 0.1609 | 7.27 | 3700 | 0.2182 | 0.9214 |
| 0.1001 | 7.47 | 3800 | 0.2110 | 0.9317 |
| 0.097 | 7.66 | 3900 | 0.2949 | 0.9224 |
| 0.1234 | 7.86 | 4000 | 0.2365 | 0.9337 |
| 0.1572 | 8.06 | 4100 | 0.2352 | 0.9283 |
| 0.1402 | 8.25 | 4200 | 0.2299 | 0.9258 |
| 0.1089 | 8.45 | 4300 | 0.2465 | 0.9298 |
| 0.1376 | 8.64 | 4400 | 0.2257 | 0.9298 |
| 0.0911 | 8.84 | 4500 | 0.2057 | 0.9342 |
| 0.1406 | 9.04 | 4600 | 0.2064 | 0.9386 |
| 0.1318 | 9.23 | 4700 | 0.2241 | 0.9347 |
| 0.052 | 9.43 | 4800 | 0.1821 | 0.9425 |
| 0.106 | 9.63 | 4900 | 0.2407 | 0.9283 |
| 0.0975 | 9.82 | 5000 | 0.2013 | 0.9381 |
| 0.1277 | 10.02 | 5100 | 0.1717 | 0.9445 |
| 0.0859 | 10.22 | 5200 | 0.2233 | 0.9352 |
| 0.1242 | 10.41 | 5300 | 0.2232 | 0.9361 |
| 0.0355 | 10.61 | 5400 | 0.1898 | 0.9381 |
| 0.1613 | 10.81 | 5500 | 0.2030 | 0.9401 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
carolinetfls/plant-seedlings-resnet-152
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-resnet-152
This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2604
- Accuracy: 0.9147
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.175 | 0.53 | 100 | 2.1135 | 0.3247 |
| 1.146 | 1.06 | 200 | 1.0761 | 0.6654 |
| 0.8299 | 1.6 | 300 | 0.7586 | 0.7391 |
| 0.7896 | 2.13 | 400 | 0.7093 | 0.7680 |
| 0.7327 | 2.66 | 500 | 0.5140 | 0.8207 |
| 0.5207 | 3.19 | 600 | 0.5375 | 0.8183 |
| 0.6465 | 3.72 | 700 | 0.4620 | 0.8465 |
| 0.2745 | 4.26 | 800 | 0.4784 | 0.8324 |
| 0.5366 | 4.79 | 900 | 0.4804 | 0.8355 |
| 0.4467 | 5.32 | 1000 | 0.4354 | 0.8551 |
| 0.3604 | 5.85 | 1100 | 0.3950 | 0.8680 |
| 0.2511 | 6.38 | 1200 | 0.4279 | 0.8594 |
| 0.326 | 6.91 | 1300 | 0.3677 | 0.8852 |
| 0.3444 | 7.45 | 1400 | 0.3539 | 0.8748 |
| 0.4015 | 7.98 | 1500 | 0.3161 | 0.8950 |
| 0.2821 | 8.51 | 1600 | 0.4394 | 0.8686 |
| 0.435 | 9.04 | 1700 | 0.3408 | 0.8920 |
| 0.3318 | 9.57 | 1800 | 0.3886 | 0.8778 |
| 0.2441 | 10.11 | 1900 | 0.2854 | 0.9042 |
| 0.2467 | 10.64 | 2000 | 0.3248 | 0.8883 |
| 0.2082 | 11.17 | 2100 | 0.3080 | 0.8956 |
| 0.1983 | 11.7 | 2200 | 0.3394 | 0.8963 |
| 0.2609 | 12.23 | 2300 | 0.3582 | 0.8870 |
| 0.2055 | 12.77 | 2400 | 0.3330 | 0.8963 |
| 0.3476 | 13.3 | 2500 | 0.2852 | 0.9091 |
| 0.223 | 13.83 | 2600 | 0.3115 | 0.8999 |
| 0.2307 | 14.36 | 2700 | 0.2986 | 0.9098 |
| 0.3113 | 14.89 | 2800 | 0.3103 | 0.8993 |
| 0.1792 | 15.43 | 2900 | 0.2862 | 0.9098 |
| 0.1685 | 15.96 | 3000 | 0.2935 | 0.9055 |
| 0.2429 | 16.49 | 3100 | 0.2882 | 0.9122 |
| 0.2548 | 17.02 | 3200 | 0.2748 | 0.9165 |
| 0.3561 | 17.55 | 3300 | 0.2684 | 0.9171 |
| 0.1982 | 18.09 | 3400 | 0.2647 | 0.9147 |
| 0.1638 | 18.62 | 3500 | 0.2509 | 0.9171 |
| 0.2404 | 19.15 | 3600 | 0.2936 | 0.9165 |
| 0.2424 | 19.68 | 3700 | 0.2604 | 0.9147 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
krasserm/perceiver-io-img-clf
|
# Perceiver IO image classifier
This model is a Perceiver IO model pretrained on ImageNet (14 million images, 1,000 classes). It is weight-equivalent
to the [deepmind/vision-perceiver-fourier](https://huggingface.co/deepmind/vision-perceiver-fourier) model but based on
implementation classes of the [perceiver-io](https://github.com/krasserm/perceiver-io) library. It can be created from
the `deepmind/vision-perceiver-fourier` model with a library-specific [conversion utility](#model-conversion). Both
models generate equal output for the same input.
Content of the `deepmind/vision-perceiver-fourier` [model card](https://huggingface.co/deepmind/vision-perceiver-fourier)
also applies to this model except [usage examples](#usage-examples). Refer to the linked card for further model and
training details.
## Model description
The model is specif in Appendix A of the [Perceiver IO paper](https://arxiv.org/abs/2107.14795) (2D Fourier features).
## Intended use and limitations
The model can be used for image classification.
## Usage examples
To use this model you first need to [install](https://github.com/krasserm/perceiver-io/blob/main/README.md#installation)
the `perceiver-io` library with extension `text`.
```shell
pip install perceiver-io[text]
```
Then the model can be used with PyTorch. Either use the model and image processor directly
```python
import requests
from PIL import Image
from transformers import AutoModelForImageClassification, AutoImageProcessor
from perceiver.model.vision import image_classifier # auto-class registration
repo_id = "krasserm/perceiver-io-img-clf"
# An image of a baseball player from MS-COCO validation set
url = "http://images.cocodataset.org/val2017/000000507223.jpg"
image = Image.open(requests.get(url, stream=True).raw)
model = AutoModelForImageClassification.from_pretrained(repo_id)
processor = AutoImageProcessor.from_pretrained(repo_id)
processed = processor(image, return_tensors="pt")
prediction = model(**processed).logits.argmax(dim=-1)
print(f"Predicted class = {model.config.id2label[prediction.item()]}")
```
```
Predicted class = ballplayer, baseball player
```
or use an `image-classification` pipeline:
```python
import requests
from PIL import Image
from transformers import pipeline
from perceiver.model.vision import image_classifier # auto-class registration
repo_id = "krasserm/perceiver-io-img-clf"
# An image of a baseball player from MS-COCO validation set
url = "http://images.cocodataset.org/val2017/000000507223.jpg"
image = Image.open(requests.get(url, stream=True).raw)
classifier = pipeline("image-classification", model=repo_id)
prediction = classifier(image)
print(f"Predicted class = {prediction[0]['label']}")
```
```
Predicted class = ballplayer, baseball player
```
## Model conversion
The `krasserm/perceiver-io-img-clf` model has been created from the source `deepmind/vision-perceiver-fourier` model
with:
```python
from perceiver.model.vision.image_classifier import convert_model
convert_model(
save_dir="krasserm/perceiver-io-img-clf",
source_repo_id="deepmind/vision-perceiver-fourier",
push_to_hub=True,
)
```
## Citation
```bibtex
@article{jaegle2021perceiver,
title={Perceiver IO: A General Architecture for Structured Inputs \& Outputs},
author={Jaegle, Andrew and Borgeaud, Sebastian and Alayrac, Jean-Baptiste and Doersch, Carl and Ionescu, Catalin and Ding, David and Koppula, Skanda and Zoran, Daniel and Brock, Andrew and Shelhamer, Evan and others},
journal={arXiv preprint arXiv:2107.14795},
year={2021}
}
```
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
krasserm/perceiver-io-img-clf-mnist
|
# Perceiver IO image classifier (MNIST)
This model is a small Perceiver IO image classifier (907K parameters) trained from scratch on the [MNIST](https://huggingface.co/datasets/mnist)
dataset. It is a [training example](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#image-classification)
of the [perceiver-io](https://github.com/krasserm/perceiver-io) library.
## Model description
Like [krasserm/perceiver-io-img-clf](https://huggingface.co/krasserm/perceiver-io-img-clf) this model also uses 2D
Fourier features for position encoding and cross-attends to individual pixels of an input image but uses repeated
cross-attention, a configuration that was described in the original [Perceiver paper](https://arxiv.org/abs/2103.03206)
which has been dropped in the follow-up [Perceiver IO paper](https://arxiv.org/abs/2107.14795) (see
[building blocks](https://github.com/krasserm/perceiver-io/blob/main/docs/building-blocks.md) for more details).
## Model training
The model was [trained](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#image-classification)
with randomly initialized weights on the MNIST handwritten digits dataset. Images were normalized, data augmentations
were turned off. Training was done with [PyTorch Lightning](https://www.pytorchlightning.ai/index.html) and the resulting
checkpoint was converted to this 🤗 model with a library-specific [conversion utility](#checkpoint-conversion).
## Intended use and limitations
The model can be used for MNIST handwritten digit classification.
## Usage examples
To use this model you first need to [install](https://github.com/krasserm/perceiver-io/blob/main/README.md#installation)
the `perceiver-io` library with extension `vision`.
```shell
pip install perceiver-io[vision]
```
Then the model can be used with PyTorch. Either use the model and image processor directly
```python
from datasets import load_dataset
from transformers import AutoModelForImageClassification, AutoImageProcessor
from perceiver.model.vision import image_classifier # auto-class registration
repo_id = "krasserm/perceiver-io-img-clf-mnist"
mnist_dataset = load_dataset("mnist", split="test")[:9]
images = mnist_dataset["image"]
labels = mnist_dataset["label"]
model = AutoModelForImageClassification.from_pretrained(repo_id)
processor = AutoImageProcessor.from_pretrained(repo_id)
inputs = processor(images, return_tensors="pt")
logits = model(**inputs).logits
print(f"Labels: {labels}")
print(f"Predictions: {logits.argmax(dim=-1).numpy().tolist()}")
```
```
Labels: [7, 2, 1, 0, 4, 1, 4, 9, 5]
Predictions: [7, 2, 1, 0, 4, 1, 4, 9, 5]
```
or use an `image-classification` pipeline:
```python
from datasets import load_dataset
from transformers import pipeline
from perceiver.model.vision import image_classifier # auto-class registration
repo_id = "krasserm/perceiver-io-img-clf-mnist"
mnist_dataset = load_dataset("mnist", split="test")[:9]
images = mnist_dataset["image"]
labels = mnist_dataset["label"]
classifier = pipeline("image-classification", model=repo_id)
predictions = [pred[0]["label"] for pred in classifier(images)]
print(f"Labels: {labels}")
print(f"Predictions: {predictions}")
```
```
Labels: [7, 2, 1, 0, 4, 1, 4, 9, 5]
Predictions: [7, 2, 1, 0, 4, 1, 4, 9, 5]
```
## Checkpoint conversion
The `krasserm/perceiver-io-img-clf-mnist` model has been created from a training checkpoint with:
```python
from perceiver.model.vision.image_classifier import convert_mnist_classifier_checkpoint
convert_mnist_classifier_checkpoint(
save_dir="krasserm/perceiver-io-img-clf-mnist",
ckpt_url="https://martin-krasser.com/perceiver/logs-0.8.0/img_clf/version_0/checkpoints/epoch=025-val_loss=0.065.ckpt",
push_to_hub=True,
)
```
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
carolinetfls/plant-seedlings-vit-freeze-0-6-aug-3-all-train
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-freeze-0-6-aug-3-all-train
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2362
- Accuracy: 0.9454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1589 | 0.25 | 100 | 0.3561 | 0.9141 |
| 0.1629 | 0.49 | 200 | 0.3792 | 0.8932 |
| 0.1222 | 0.74 | 300 | 0.3749 | 0.8975 |
| 0.1363 | 0.98 | 400 | 0.3021 | 0.9122 |
| 0.0699 | 1.23 | 500 | 0.4802 | 0.8883 |
| 0.0759 | 1.47 | 600 | 0.3129 | 0.9214 |
| 0.2157 | 1.72 | 700 | 0.3362 | 0.9159 |
| 0.1148 | 1.97 | 800 | 0.2898 | 0.9122 |
| 0.3137 | 2.21 | 900 | 0.4267 | 0.8969 |
| 0.072 | 2.46 | 1000 | 0.3180 | 0.9141 |
| 0.0775 | 2.7 | 1100 | 0.3856 | 0.9067 |
| 0.1019 | 2.95 | 1200 | 0.3182 | 0.9153 |
| 0.1119 | 3.19 | 1300 | 0.4458 | 0.8944 |
| 0.1342 | 3.44 | 1400 | 0.4718 | 0.8889 |
| 0.1658 | 3.69 | 1500 | 0.3697 | 0.9012 |
| 0.1609 | 3.93 | 1600 | 0.4079 | 0.9024 |
| 0.1223 | 4.18 | 1700 | 0.3688 | 0.9147 |
| 0.1821 | 4.42 | 1800 | 0.3392 | 0.9116 |
| 0.0901 | 4.67 | 1900 | 0.3726 | 0.8969 |
| 0.0857 | 4.91 | 2000 | 0.3158 | 0.9196 |
| 0.1245 | 5.16 | 2100 | 0.3503 | 0.9122 |
| 0.133 | 5.41 | 2200 | 0.3712 | 0.9134 |
| 0.171 | 5.65 | 2300 | 0.3543 | 0.9067 |
| 0.1222 | 5.9 | 2400 | 0.3031 | 0.9227 |
| 0.1504 | 6.14 | 2500 | 0.3356 | 0.9085 |
| 0.0889 | 6.39 | 2600 | 0.3695 | 0.9116 |
| 0.0185 | 6.63 | 2700 | 0.3509 | 0.9141 |
| 0.1201 | 6.88 | 2800 | 0.3330 | 0.9177 |
| 0.0766 | 7.13 | 2900 | 0.2718 | 0.9251 |
| 0.0998 | 7.37 | 3000 | 0.3471 | 0.9233 |
| 0.1654 | 7.62 | 3100 | 0.3285 | 0.9196 |
| 0.0529 | 7.86 | 3200 | 0.3394 | 0.9190 |
| 0.1199 | 8.11 | 3300 | 0.2968 | 0.9294 |
| 0.0338 | 8.35 | 3400 | 0.2784 | 0.9251 |
| 0.124 | 8.6 | 3500 | 0.3099 | 0.9251 |
| 0.0581 | 8.85 | 3600 | 0.3372 | 0.9263 |
| 0.1776 | 9.09 | 3700 | 0.3580 | 0.9134 |
| 0.1598 | 9.34 | 3800 | 0.3158 | 0.9196 |
| 0.1122 | 9.58 | 3900 | 0.3369 | 0.9190 |
| 0.0808 | 9.83 | 4000 | 0.3259 | 0.9368 |
| 0.1086 | 10.07 | 4100 | 0.3691 | 0.9190 |
| 0.0197 | 10.32 | 4200 | 0.3101 | 0.9355 |
| 0.065 | 10.57 | 4300 | 0.3479 | 0.9227 |
| 0.1183 | 10.81 | 4400 | 0.3281 | 0.9319 |
| 0.044 | 11.06 | 4500 | 0.4357 | 0.9134 |
| 0.1021 | 11.3 | 4600 | 0.3211 | 0.9337 |
| 0.0615 | 11.55 | 4700 | 0.2947 | 0.9398 |
| 0.0664 | 11.79 | 4800 | 0.4421 | 0.9184 |
| 0.092 | 12.04 | 4900 | 0.3333 | 0.9202 |
| 0.1544 | 12.29 | 5000 | 0.3062 | 0.9245 |
| 0.1324 | 12.53 | 5100 | 0.2756 | 0.9294 |
| 0.1132 | 12.78 | 5200 | 0.2570 | 0.9362 |
| 0.0899 | 13.02 | 5300 | 0.2486 | 0.9386 |
| 0.0712 | 13.27 | 5400 | 0.2878 | 0.9306 |
| 0.0411 | 13.51 | 5500 | 0.2663 | 0.9368 |
| 0.0559 | 13.76 | 5600 | 0.2751 | 0.9355 |
| 0.0928 | 14.0 | 5700 | 0.3093 | 0.9269 |
| 0.0504 | 14.25 | 5800 | 0.2954 | 0.9319 |
| 0.0995 | 14.5 | 5900 | 0.2636 | 0.9337 |
| 0.1139 | 14.74 | 6000 | 0.2827 | 0.9349 |
| 0.0992 | 14.99 | 6100 | 0.2662 | 0.9368 |
| 0.1519 | 15.23 | 6200 | 0.2720 | 0.9398 |
| 0.0192 | 15.48 | 6300 | 0.3252 | 0.9269 |
| 0.0592 | 15.72 | 6400 | 0.3382 | 0.9263 |
| 0.0382 | 15.97 | 6500 | 0.2710 | 0.9349 |
| 0.0723 | 16.22 | 6600 | 0.2671 | 0.9374 |
| 0.0073 | 16.46 | 6700 | 0.3451 | 0.9263 |
| 0.1796 | 16.71 | 6800 | 0.3196 | 0.9196 |
| 0.0919 | 16.95 | 6900 | 0.2464 | 0.9337 |
| 0.0739 | 17.2 | 7000 | 0.2258 | 0.9392 |
| 0.0468 | 17.44 | 7100 | 0.2483 | 0.9411 |
| 0.145 | 17.69 | 7200 | 0.2639 | 0.9312 |
| 0.0243 | 17.94 | 7300 | 0.2574 | 0.9362 |
| 0.0648 | 18.18 | 7400 | 0.2554 | 0.9331 |
| 0.0508 | 18.43 | 7500 | 0.2554 | 0.9374 |
| 0.0475 | 18.67 | 7600 | 0.2915 | 0.9337 |
| 0.0708 | 18.92 | 7700 | 0.2801 | 0.9300 |
| 0.1476 | 19.16 | 7800 | 0.2479 | 0.9411 |
| 0.1535 | 19.41 | 7900 | 0.2412 | 0.9411 |
| 0.0873 | 19.66 | 8000 | 0.2544 | 0.9398 |
| 0.0416 | 19.9 | 8100 | 0.2334 | 0.9423 |
| 0.1157 | 20.15 | 8200 | 0.2059 | 0.9540 |
| 0.039 | 20.39 | 8300 | 0.2601 | 0.9362 |
| 0.0223 | 20.64 | 8400 | 0.2234 | 0.9484 |
| 0.0779 | 20.88 | 8500 | 0.2468 | 0.9405 |
| 0.0604 | 21.13 | 8600 | 0.2334 | 0.9374 |
| 0.1206 | 21.38 | 8700 | 0.2504 | 0.9398 |
| 0.0738 | 21.62 | 8800 | 0.2505 | 0.9398 |
| 0.0438 | 21.87 | 8900 | 0.2148 | 0.9472 |
| 0.0689 | 22.11 | 9000 | 0.2286 | 0.9435 |
| 0.0505 | 22.36 | 9100 | 0.1956 | 0.9472 |
| 0.0581 | 22.6 | 9200 | 0.2104 | 0.9484 |
| 0.1575 | 22.85 | 9300 | 0.2309 | 0.9441 |
| 0.048 | 23.1 | 9400 | 0.2685 | 0.9398 |
| 0.0784 | 23.34 | 9500 | 0.2329 | 0.9454 |
| 0.0771 | 23.59 | 9600 | 0.2294 | 0.9466 |
| 0.0545 | 23.83 | 9700 | 0.2037 | 0.9484 |
| 0.0481 | 24.08 | 9800 | 0.1994 | 0.9540 |
| 0.0663 | 24.32 | 9900 | 0.1993 | 0.9490 |
| 0.0921 | 24.57 | 10000 | 0.2204 | 0.9521 |
| 0.0939 | 24.82 | 10100 | 0.2362 | 0.9454 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
hn11235/plant-seedlings-freeze-0-6-aug-3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-freeze-0-6-aug-3
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1722
- Accuracy: 0.9425
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.799 | 0.2 | 100 | 0.5965 | 0.7976 |
| 0.7161 | 0.39 | 200 | 0.7085 | 0.7633 |
| 0.7159 | 0.59 | 300 | 0.5736 | 0.8094 |
| 0.5998 | 0.79 | 400 | 0.5116 | 0.8202 |
| 0.4901 | 0.98 | 500 | 0.4973 | 0.8468 |
| 0.4669 | 1.18 | 600 | 0.5753 | 0.8021 |
| 0.418 | 1.38 | 700 | 0.5035 | 0.8310 |
| 0.2675 | 1.57 | 800 | 0.4583 | 0.8502 |
| 0.3409 | 1.77 | 900 | 0.4194 | 0.8600 |
| 0.5934 | 1.96 | 1000 | 0.4950 | 0.8320 |
| 0.3727 | 2.16 | 1100 | 0.5086 | 0.8222 |
| 0.2635 | 2.36 | 1200 | 0.4315 | 0.8551 |
| 0.3595 | 2.55 | 1300 | 0.3716 | 0.8728 |
| 0.4091 | 2.75 | 1400 | 0.3692 | 0.8787 |
| 0.3579 | 2.95 | 1500 | 0.3331 | 0.8865 |
| 0.3034 | 3.14 | 1600 | 0.3121 | 0.8895 |
| 0.2943 | 3.34 | 1700 | 0.3551 | 0.8816 |
| 0.3346 | 3.54 | 1800 | 0.4268 | 0.8625 |
| 0.3261 | 3.73 | 1900 | 0.3222 | 0.8880 |
| 0.3287 | 3.93 | 2000 | 0.3072 | 0.8964 |
| 0.2753 | 4.13 | 2100 | 0.3209 | 0.8910 |
| 0.1975 | 4.32 | 2200 | 0.3564 | 0.8757 |
| 0.2291 | 4.52 | 2300 | 0.3057 | 0.9003 |
| 0.232 | 4.72 | 2400 | 0.3124 | 0.8929 |
| 0.2834 | 4.91 | 2500 | 0.2631 | 0.9165 |
| 0.3484 | 5.11 | 2600 | 0.2987 | 0.9008 |
| 0.2019 | 5.3 | 2700 | 0.2976 | 0.8998 |
| 0.2179 | 5.5 | 2800 | 0.2596 | 0.9082 |
| 0.2068 | 5.7 | 2900 | 0.2852 | 0.9121 |
| 0.2791 | 5.89 | 3000 | 0.2523 | 0.9145 |
| 0.1888 | 6.09 | 3100 | 0.2554 | 0.9145 |
| 0.1909 | 6.29 | 3200 | 0.2623 | 0.9170 |
| 0.1677 | 6.48 | 3300 | 0.2885 | 0.9091 |
| 0.1832 | 6.68 | 3400 | 0.2345 | 0.9190 |
| 0.1088 | 6.88 | 3500 | 0.2448 | 0.9214 |
| 0.2065 | 7.07 | 3600 | 0.2341 | 0.9170 |
| 0.2561 | 7.27 | 3700 | 0.2253 | 0.9209 |
| 0.1902 | 7.47 | 3800 | 0.2196 | 0.9244 |
| 0.236 | 7.66 | 3900 | 0.2217 | 0.9293 |
| 0.2483 | 7.86 | 4000 | 0.2314 | 0.9150 |
| 0.1761 | 8.06 | 4100 | 0.2327 | 0.9268 |
| 0.2349 | 8.25 | 4200 | 0.2408 | 0.9258 |
| 0.0809 | 8.45 | 4300 | 0.1858 | 0.9361 |
| 0.1723 | 8.64 | 4400 | 0.2643 | 0.9204 |
| 0.1762 | 8.84 | 4500 | 0.2194 | 0.9278 |
| 0.1438 | 9.04 | 4600 | 0.1897 | 0.9357 |
| 0.0805 | 9.23 | 4700 | 0.2169 | 0.9322 |
| 0.1513 | 9.43 | 4800 | 0.1635 | 0.9460 |
| 0.1356 | 9.63 | 4900 | 0.1940 | 0.9347 |
| 0.0737 | 9.82 | 5000 | 0.2014 | 0.9342 |
| 0.0908 | 10.02 | 5100 | 0.1707 | 0.9460 |
| 0.1082 | 10.22 | 5200 | 0.1620 | 0.9494 |
| 0.0773 | 10.41 | 5300 | 0.2009 | 0.9332 |
| 0.1614 | 10.61 | 5400 | 0.1735 | 0.9425 |
| 0.0983 | 10.81 | 5500 | 0.1722 | 0.9425 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
hgolchha/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
scscsc/vit-base-patch16-224-finetuned-flower
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
nicolasvillamilsanchez/autotrain-petrography-51985122663
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51985122663
- CO2 Emissions (in grams): 0.6950
## Validation Metrics
- Loss: 0.149
- Accuracy: 0.980
- Macro F1: 0.978
- Micro F1: 0.980
- Weighted F1: 0.980
- Macro Precision: 0.976
- Micro Precision: 0.980
- Weighted Precision: 0.983
- Macro Recall: 0.983
- Micro Recall: 0.980
- Weighted Recall: 0.980
|
[
"allanite",
"antipertite",
"calcite",
"jotunite",
"synneryte",
"tonalite"
] |
nicolasvillamilsanchez/autotrain-igneus-petrography-52100122886
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 52100122886
- CO2 Emissions (in grams): 2.6171
## Validation Metrics
- Loss: 0.115
- Accuracy: 0.944
- Macro F1: 0.923
- Micro F1: 0.944
- Weighted F1: 0.942
- Macro Precision: 0.938
- Micro Precision: 0.944
- Weighted Precision: 0.947
- Macro Recall: 0.919
- Micro Recall: 0.944
- Weighted Recall: 0.944
|
[
"hornblende olivine bearing piroxene",
"serpentinized lherzolite",
"jotunite",
"lujavrite",
"luxulianite",
"nepheline pegmatite",
"nepheline syenite",
"norite",
"olivine gabbro",
"olivine hornblendite",
"ortopiroxenite",
"pulaskite",
"allanite",
"quartz gabbro",
"sienite",
"sienite sodalite",
"sienogranito",
"antipertite",
"calcite",
"cancrinite pegmatite",
"cumberlandite",
"granite peralkaline",
"granitic pegmatite",
"hornblende -olivine bearing piroxenite"
] |
platzi/platzi-vit-model-julio-garcia
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-julio-garcia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1061
- Accuracy: 0.9699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1352 | 3.85 | 500 | 0.1061 | 0.9699 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
nicolasvillamilsanchez/autotrain-igeous-52117122942
|
[
"hornblende olivine bearing piroxene",
"serpentinized lherzolite",
"jotunite",
"lujavrite",
"luxulianite",
"nepheline pegmatite",
"nepheline syenite",
"norite",
"olivine gabbro",
"olivine hornblendite",
"ortopiroxenite",
"pulaskite",
"allanite",
"quartz gabbro",
"sienite",
"sienite sodalite",
"sienogranito",
"antipertite",
"calcite",
"cancrinite pegmatite",
"cumberlandite",
"granite peralkaline",
"granitic pegmatite",
"hornblende -olivine bearing piroxenite"
] |
|
carolinetfls/plant-seedlings-vit-freeze-0-6-aug-3-all-train-2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-freeze-0-6-aug-3-all-train-2
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1475
- Accuracy: 0.9503
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.256 | 0.16 | 100 | 0.2983 | 0.9153 |
| 0.145 | 0.31 | 200 | 0.1943 | 0.9392 |
| 0.2502 | 0.47 | 300 | 0.2294 | 0.9227 |
| 0.0876 | 0.63 | 400 | 0.1904 | 0.9355 |
| 0.1218 | 0.79 | 500 | 0.1430 | 0.9540 |
| 0.0952 | 0.94 | 600 | 0.1475 | 0.9503 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
carolinetfls/plant-seedlings-freeze-0-6-aug-3-lr-decay
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-freeze-0-6-aug-3-lr-decay
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5661
- Accuracy: 0.8129
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5566 | 1.59 | 100 | 0.5549 | 0.8193 |
| 0.4338 | 3.17 | 200 | 0.5507 | 0.8212 |
| 0.4637 | 4.76 | 300 | 0.5418 | 0.8193 |
| 0.5061 | 6.35 | 400 | 0.5586 | 0.8139 |
| 0.3512 | 7.94 | 500 | 0.5550 | 0.8291 |
| 0.4779 | 9.52 | 600 | 0.5414 | 0.8256 |
| 0.449 | 11.11 | 700 | 0.5815 | 0.8055 |
| 0.5039 | 12.7 | 800 | 0.5661 | 0.8129 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
carolinetfls/plant-seedlings-freeze-0-6-aug-3-no-lr-decay
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-freeze-0-6-aug-3-no-lr-decay
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4328
- Accuracy: 0.8772
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.546 | 1.59 | 100 | 0.7861 | 0.7421 |
| 0.4435 | 3.17 | 200 | 0.6630 | 0.7785 |
| 0.4183 | 4.76 | 300 | 0.5526 | 0.8286 |
| 0.2947 | 6.35 | 400 | 0.4689 | 0.8625 |
| 0.0971 | 7.94 | 500 | 0.4795 | 0.8620 |
| 0.1414 | 9.52 | 600 | 0.4423 | 0.8708 |
| 0.1034 | 11.11 | 700 | 0.4327 | 0.8806 |
| 0.0293 | 12.7 | 800 | 0.4328 | 0.8772 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
carolinetfls/plant-seedlings-vit-freeze-0-6-aug-3-whole-data-train
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-freeze-0-6-aug-3-whole-data-train
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6146 | 0.16 | 100 | 0.0402 | 1.0 |
| 0.6062 | 0.31 | 200 | 0.2393 | 1.0 |
| 0.4847 | 0.47 | 300 | 0.2164 | 1.0 |
| 0.5282 | 0.63 | 400 | 0.0427 | 1.0 |
| 0.4153 | 0.79 | 500 | 1.0996 | 0.0 |
| 0.3295 | 0.94 | 600 | 0.0499 | 1.0 |
| 0.3541 | 1.1 | 700 | 0.0009 | 1.0 |
| 0.4617 | 1.26 | 800 | 0.0106 | 1.0 |
| 0.3014 | 1.42 | 900 | 0.0045 | 1.0 |
| 0.3558 | 1.57 | 1000 | 0.0038 | 1.0 |
| 0.2357 | 1.73 | 1100 | 0.0140 | 1.0 |
| 0.3055 | 1.89 | 1200 | 0.0809 | 1.0 |
| 0.3278 | 2.04 | 1300 | 0.0077 | 1.0 |
| 0.3075 | 2.2 | 1400 | 0.0059 | 1.0 |
| 0.3462 | 2.36 | 1500 | 0.0377 | 1.0 |
| 0.2968 | 2.52 | 1600 | 0.0082 | 1.0 |
| 0.3392 | 2.67 | 1700 | 0.8628 | 0.0 |
| 0.2155 | 2.83 | 1800 | 0.0022 | 1.0 |
| 0.3521 | 2.99 | 1900 | 0.0671 | 1.0 |
| 0.3968 | 3.14 | 2000 | 0.0014 | 1.0 |
| 0.32 | 3.3 | 2100 | 0.0075 | 1.0 |
| 0.1787 | 3.46 | 2200 | 0.0015 | 1.0 |
| 0.2598 | 3.62 | 2300 | 0.0086 | 1.0 |
| 0.3424 | 3.77 | 2400 | 0.0008 | 1.0 |
| 0.2371 | 3.93 | 2500 | 0.0054 | 1.0 |
| 0.2773 | 4.09 | 2600 | 0.0028 | 1.0 |
| 0.3192 | 4.25 | 2700 | 0.0088 | 1.0 |
| 0.2173 | 4.4 | 2800 | 0.1174 | 1.0 |
| 0.2181 | 4.56 | 2900 | 0.0056 | 1.0 |
| 0.2476 | 4.72 | 3000 | 0.0006 | 1.0 |
| 0.2417 | 4.87 | 3100 | 0.0005 | 1.0 |
| 0.1915 | 5.03 | 3200 | 0.0002 | 1.0 |
| 0.149 | 5.19 | 3300 | 0.0004 | 1.0 |
| 0.1618 | 5.35 | 3400 | 0.1542 | 1.0 |
| 0.1752 | 5.5 | 3500 | 0.0001 | 1.0 |
| 0.1094 | 5.66 | 3600 | 0.0045 | 1.0 |
| 0.2532 | 5.82 | 3700 | 0.0016 | 1.0 |
| 0.1606 | 5.97 | 3800 | 0.0004 | 1.0 |
| 0.1781 | 6.13 | 3900 | 0.0007 | 1.0 |
| 0.1459 | 6.29 | 4000 | 0.0003 | 1.0 |
| 0.2357 | 6.45 | 4100 | 2.6113 | 0.0 |
| 0.2524 | 6.6 | 4200 | 0.0003 | 1.0 |
| 0.1708 | 6.76 | 4300 | 0.0006 | 1.0 |
| 0.1875 | 6.92 | 4400 | 0.0011 | 1.0 |
| 0.1462 | 7.08 | 4500 | 0.0004 | 1.0 |
| 0.1534 | 7.23 | 4600 | 0.0002 | 1.0 |
| 0.2834 | 7.39 | 4700 | 0.0003 | 1.0 |
| 0.2264 | 7.55 | 4800 | 0.0001 | 1.0 |
| 0.1007 | 7.7 | 4900 | 0.0001 | 1.0 |
| 0.2376 | 7.86 | 5000 | 0.0006 | 1.0 |
| 0.2233 | 8.02 | 5100 | 0.0002 | 1.0 |
| 0.1804 | 8.18 | 5200 | 0.0034 | 1.0 |
| 0.185 | 8.33 | 5300 | 0.0002 | 1.0 |
| 0.1149 | 8.49 | 5400 | 0.0007 | 1.0 |
| 0.2048 | 8.65 | 5500 | 0.0009 | 1.0 |
| 0.0786 | 8.81 | 5600 | 0.9478 | 0.0 |
| 0.2222 | 8.96 | 5700 | 0.0007 | 1.0 |
| 0.1289 | 9.12 | 5800 | 0.0009 | 1.0 |
| 0.2248 | 9.28 | 5900 | 0.0005 | 1.0 |
| 0.0987 | 9.43 | 6000 | 0.0002 | 1.0 |
| 0.2897 | 9.59 | 6100 | 0.0002 | 1.0 |
| 0.2023 | 9.75 | 6200 | 0.0042 | 1.0 |
| 0.1481 | 9.91 | 6300 | 0.0003 | 1.0 |
| 0.1224 | 10.06 | 6400 | 0.0009 | 1.0 |
| 0.1353 | 10.22 | 6500 | 0.0080 | 1.0 |
| 0.0659 | 10.38 | 6600 | 0.0006 | 1.0 |
| 0.1692 | 10.53 | 6700 | 0.0005 | 1.0 |
| 0.1713 | 10.69 | 6800 | 0.0006 | 1.0 |
| 0.1131 | 10.85 | 6900 | 0.0012 | 1.0 |
| 0.2325 | 11.01 | 7000 | 0.0003 | 1.0 |
| 0.0817 | 11.16 | 7100 | 0.0003 | 1.0 |
| 0.1854 | 11.32 | 7200 | 0.0001 | 1.0 |
| 0.0956 | 11.48 | 7300 | 0.0002 | 1.0 |
| 0.0758 | 11.64 | 7400 | 0.0127 | 1.0 |
| 0.0928 | 11.79 | 7500 | 0.0002 | 1.0 |
| 0.1563 | 11.95 | 7600 | 0.0004 | 1.0 |
| 0.0596 | 12.11 | 7700 | 0.0003 | 1.0 |
| 0.1266 | 12.26 | 7800 | 0.0031 | 1.0 |
| 0.1788 | 12.42 | 7900 | 0.0002 | 1.0 |
| 0.1663 | 12.58 | 8000 | 0.0071 | 1.0 |
| 0.064 | 12.74 | 8100 | 0.0003 | 1.0 |
| 0.1459 | 12.89 | 8200 | 0.0005 | 1.0 |
| 0.1237 | 13.05 | 8300 | 0.0001 | 1.0 |
| 0.1334 | 13.21 | 8400 | 0.0001 | 1.0 |
| 0.0802 | 13.36 | 8500 | 0.0001 | 1.0 |
| 0.1418 | 13.52 | 8600 | 0.0000 | 1.0 |
| 0.048 | 13.68 | 8700 | 0.0001 | 1.0 |
| 0.1267 | 13.84 | 8800 | 0.0121 | 1.0 |
| 0.1298 | 13.99 | 8900 | 0.0001 | 1.0 |
| 0.16 | 14.15 | 9000 | 0.0001 | 1.0 |
| 0.1295 | 14.31 | 9100 | 0.0001 | 1.0 |
| 0.1714 | 14.47 | 9200 | 0.0001 | 1.0 |
| 0.1377 | 14.62 | 9300 | 0.0001 | 1.0 |
| 0.1336 | 14.78 | 9400 | 0.0001 | 1.0 |
| 0.1293 | 14.94 | 9500 | 0.0001 | 1.0 |
| 0.111 | 15.09 | 9600 | 0.0001 | 1.0 |
| 0.0818 | 15.25 | 9700 | 0.0000 | 1.0 |
| 0.1884 | 15.41 | 9800 | 0.0001 | 1.0 |
| 0.1004 | 15.57 | 9900 | 0.0002 | 1.0 |
| 0.1029 | 15.72 | 10000 | 0.0000 | 1.0 |
| 0.0772 | 15.88 | 10100 | 0.0000 | 1.0 |
| 0.1573 | 16.04 | 10200 | 0.0001 | 1.0 |
| 0.0748 | 16.19 | 10300 | 0.0001 | 1.0 |
| 0.088 | 16.35 | 10400 | 0.0001 | 1.0 |
| 0.1062 | 16.51 | 10500 | 0.0001 | 1.0 |
| 0.0237 | 16.67 | 10600 | 0.0001 | 1.0 |
| 0.0729 | 16.82 | 10700 | 0.0000 | 1.0 |
| 0.1028 | 16.98 | 10800 | 0.0001 | 1.0 |
| 0.0423 | 17.14 | 10900 | 0.0000 | 1.0 |
| 0.0922 | 17.3 | 11000 | 0.0002 | 1.0 |
| 0.0788 | 17.45 | 11100 | 0.0001 | 1.0 |
| 0.0357 | 17.61 | 11200 | 0.0001 | 1.0 |
| 0.0519 | 17.77 | 11300 | 0.0000 | 1.0 |
| 0.108 | 17.92 | 11400 | 0.0001 | 1.0 |
| 0.1746 | 18.08 | 11500 | 0.1221 | 1.0 |
| 0.1 | 18.24 | 11600 | 0.0006 | 1.0 |
| 0.0798 | 18.4 | 11700 | 0.0001 | 1.0 |
| 0.0118 | 18.55 | 11800 | 0.0001 | 1.0 |
| 0.1151 | 18.71 | 11900 | 0.0001 | 1.0 |
| 0.0617 | 18.87 | 12000 | 0.0001 | 1.0 |
| 0.1577 | 19.03 | 12100 | 0.0001 | 1.0 |
| 0.1928 | 19.18 | 12200 | 0.0001 | 1.0 |
| 0.0462 | 19.34 | 12300 | 0.0001 | 1.0 |
| 0.0461 | 19.5 | 12400 | 0.3145 | 1.0 |
| 0.0454 | 19.65 | 12500 | 0.0001 | 1.0 |
| 0.0637 | 19.81 | 12600 | 0.0001 | 1.0 |
| 0.0733 | 19.97 | 12700 | 0.0001 | 1.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
platzi/Pokemons_Clasiffication-Santiago-Garcia
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Pokemons_Clasiffication-Santiago-Garcia
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the pokemon-classification dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"porygon",
"goldeen",
"articuno",
"clefable",
"cubone",
"marowak",
"nidorino",
"jolteon",
"muk",
"magikarp",
"slowbro",
"tauros",
"kabuto",
"seaking",
"spearow",
"sandshrew",
"eevee",
"kakuna",
"omastar",
"ekans",
"geodude",
"magmar",
"snorlax",
"meowth",
"dugtrio",
"pidgeotto",
"venusaur",
"persian",
"rhydon",
"starmie",
"charmeleon",
"lapras",
"alakazam",
"graveler",
"psyduck",
"machop",
"rapidash",
"doduo",
"magneton",
"arcanine",
"electrode",
"omanyte",
"poliwhirl",
"mew",
"alolan sandslash",
"mewtwo",
"jynx",
"weezing",
"gastly",
"victreebel",
"ivysaur",
"mrmime",
"shellder",
"scyther",
"diglett",
"primeape",
"raichu",
"oddish",
"dodrio",
"dragonair",
"weedle",
"golduck",
"hitmonlee",
"flareon",
"krabby",
"parasect",
"ninetales",
"nidoqueen",
"kabutops",
"drowzee",
"caterpie",
"jigglypuff",
"machamp",
"hitmonchan",
"clefairy",
"kangaskhan",
"dragonite",
"weepinbell",
"fearow",
"bellsprout",
"grimer",
"nidorina",
"staryu",
"horsea",
"gloom",
"electabuzz",
"dratini",
"machoke",
"magnemite",
"squirtle",
"gyarados",
"pidgeot",
"bulbasaur",
"nidoking",
"golem",
"aerodactyl",
"dewgong",
"moltres",
"zapdos",
"poliwrath",
"vulpix",
"beedrill",
"charmander",
"abra",
"zubat",
"golbat",
"mankey",
"wigglytuff",
"charizard",
"slowpoke",
"poliwag",
"tentacruel",
"rhyhorn",
"onix",
"butterfree",
"exeggcute",
"sandslash",
"seadra",
"pinsir",
"rattata",
"growlithe",
"haunter",
"pidgey",
"ditto",
"farfetchd",
"pikachu",
"raticate",
"wartortle",
"gengar",
"vaporeon",
"cloyster",
"hypno",
"arbok",
"metapod",
"tangela",
"kingler",
"exeggutor",
"kadabra",
"seel",
"venonat",
"voltorb",
"chansey",
"venomoth",
"ponyta",
"vileplume",
"koffing",
"blastoise",
"tentacool",
"lickitung",
"paras"
] |
Santenana/Pokemons_Clasiffication2-Santiago-Garcia
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Pokemons_Clasiffication2-Santiago-Garcia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the pokemon-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 7.8691
- Accuracy: 0.0906
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8204 | 0.82 | 500 | 6.3147 | 0.0144 |
| 0.3221 | 1.64 | 1000 | 7.2082 | 0.0770 |
| 0.0757 | 2.46 | 1500 | 7.7975 | 0.0763 |
| 0.0314 | 3.28 | 2000 | 7.8691 | 0.0906 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"porygon",
"goldeen",
"articuno",
"clefable",
"cubone",
"marowak",
"nidorino",
"jolteon",
"muk",
"magikarp",
"slowbro",
"tauros",
"kabuto",
"seaking",
"spearow",
"sandshrew",
"eevee",
"kakuna",
"omastar",
"ekans",
"geodude",
"magmar",
"snorlax",
"meowth",
"dugtrio",
"pidgeotto",
"venusaur",
"persian",
"rhydon",
"starmie",
"charmeleon",
"lapras",
"alakazam",
"graveler",
"psyduck",
"machop",
"rapidash",
"doduo",
"magneton",
"arcanine",
"electrode",
"omanyte",
"poliwhirl",
"mew",
"alolan sandslash",
"mewtwo",
"jynx",
"weezing",
"gastly",
"victreebel",
"ivysaur",
"mrmime",
"shellder",
"scyther",
"diglett",
"primeape",
"raichu",
"oddish",
"dodrio",
"dragonair",
"weedle",
"golduck",
"hitmonlee",
"flareon",
"krabby",
"parasect",
"ninetales",
"nidoqueen",
"kabutops",
"drowzee",
"caterpie",
"jigglypuff",
"machamp",
"hitmonchan",
"clefairy",
"kangaskhan",
"dragonite",
"weepinbell",
"fearow",
"bellsprout",
"grimer",
"nidorina",
"staryu",
"horsea",
"gloom",
"electabuzz",
"dratini",
"machoke",
"magnemite",
"squirtle",
"gyarados",
"pidgeot",
"bulbasaur",
"nidoking",
"golem",
"aerodactyl",
"dewgong",
"moltres",
"zapdos",
"poliwrath",
"vulpix",
"beedrill",
"charmander",
"abra",
"zubat",
"golbat",
"mankey",
"wigglytuff",
"charizard",
"slowpoke",
"poliwag",
"tentacruel",
"rhyhorn",
"onix",
"butterfree",
"exeggcute",
"sandslash",
"seadra",
"pinsir",
"rattata",
"growlithe",
"haunter",
"pidgey",
"ditto",
"farfetchd",
"pikachu",
"raticate",
"wartortle",
"gengar",
"vaporeon",
"cloyster",
"hypno",
"arbok",
"metapod",
"tangela",
"kingler",
"exeggutor",
"kadabra",
"seel",
"venonat",
"voltorb",
"chansey",
"venomoth",
"ponyta",
"vileplume",
"koffing",
"blastoise",
"tentacool",
"lickitung",
"paras"
] |
Soulaimen/swin-tiny-patch4-window7-224-mixed-bottoms
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-mixed-bottoms
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0272
- Accuracy: 0.9889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1479 | 0.99 | 86 | 0.1040 | 0.9592 |
| 0.145 | 2.0 | 173 | 0.1033 | 0.9592 |
| 0.1248 | 2.99 | 259 | 0.0640 | 0.9777 |
| 0.0667 | 4.0 | 346 | 0.0378 | 0.9907 |
| 0.0826 | 4.99 | 432 | 0.0400 | 0.9814 |
| 0.0635 | 6.0 | 519 | 0.0331 | 0.9907 |
| 0.0688 | 7.0 | 606 | 0.0461 | 0.9852 |
| 0.0549 | 7.99 | 692 | 0.0335 | 0.9889 |
| 0.0501 | 9.0 | 779 | 0.0241 | 0.9907 |
| 0.0434 | 9.93 | 860 | 0.0272 | 0.9889 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"casual",
"sports"
] |
Binssin/autotrain-faceclassifier-52303123291
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 52303123291
- CO2 Emissions (in grams): 0.8387
## Validation Metrics
- Loss: 0.018
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000
|
[
"abdullah_gul",
"alejandro_toledo",
"alvaro_uribe",
"amelie_mauresmo",
"andre_agassi",
"angelina_jolie",
"ariel_sharon",
"arnold_schwarzenegger",
"atal_bihari_vajpayee"
] |
Soulaimen/resnet-50-resnet50_fashion
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50-resnet50_fashion
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1064
- Accuracy: 0.9740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6532 | 0.99 | 86 | 0.6781 | 0.6345 |
| 0.5407 | 2.0 | 173 | 0.5222 | 0.8590 |
| 0.4086 | 2.99 | 259 | 0.3595 | 0.8924 |
| 0.3449 | 4.0 | 346 | 0.2616 | 0.9184 |
| 0.3518 | 4.99 | 432 | 0.2288 | 0.9443 |
| 0.308 | 6.0 | 519 | 0.2758 | 0.9425 |
| 0.3209 | 7.0 | 606 | 0.3777 | 0.9369 |
| 0.284 | 7.99 | 692 | 0.1704 | 0.9555 |
| 0.2466 | 9.0 | 779 | 0.1571 | 0.9462 |
| 0.3123 | 9.99 | 865 | 0.6492 | 0.9406 |
| 0.2827 | 11.0 | 952 | 0.4968 | 0.9406 |
| 0.2736 | 11.99 | 1038 | 0.1370 | 0.9592 |
| 0.2476 | 13.0 | 1125 | 0.1616 | 0.9499 |
| 0.195 | 14.0 | 1212 | 0.1362 | 0.9610 |
| 0.2536 | 14.99 | 1298 | 0.1298 | 0.9536 |
| 0.2022 | 16.0 | 1385 | 0.7470 | 0.9518 |
| 0.2406 | 16.99 | 1471 | 0.1241 | 0.9647 |
| 0.2019 | 18.0 | 1558 | 0.1278 | 0.9536 |
| 0.2073 | 18.99 | 1644 | 0.1134 | 0.9685 |
| 0.1873 | 20.0 | 1731 | 0.6738 | 0.9629 |
| 0.2446 | 21.0 | 1818 | 0.1033 | 0.9685 |
| 0.1999 | 21.99 | 1904 | 0.1181 | 0.9647 |
| 0.1716 | 23.0 | 1991 | 0.1099 | 0.9610 |
| 0.175 | 23.99 | 2077 | 0.1064 | 0.9740 |
| 0.1962 | 25.0 | 2164 | 0.1174 | 0.9722 |
| 0.1943 | 25.99 | 2250 | 1.0625 | 0.9518 |
| 0.2044 | 27.0 | 2337 | 0.8419 | 0.9573 |
| 0.1835 | 28.0 | 2424 | 0.1112 | 0.9703 |
| 0.191 | 28.99 | 2510 | 0.1142 | 0.9685 |
| 0.1676 | 30.0 | 2597 | 0.1080 | 0.9647 |
| 0.1533 | 30.99 | 2683 | 0.1494 | 0.9647 |
| 0.1991 | 32.0 | 2770 | 0.1000 | 0.9703 |
| 0.1845 | 32.99 | 2856 | 0.0989 | 0.9740 |
| 0.1605 | 34.0 | 2943 | 0.0975 | 0.9685 |
| 0.1928 | 35.0 | 3030 | 0.4555 | 0.9629 |
| 0.1506 | 35.99 | 3116 | 0.1059 | 0.9703 |
| 0.1912 | 37.0 | 3203 | 0.1016 | 0.9647 |
| 0.1689 | 37.99 | 3289 | 0.5421 | 0.9666 |
| 0.1467 | 39.0 | 3376 | 0.1095 | 0.9647 |
| 0.1513 | 39.99 | 3462 | 0.3828 | 0.9703 |
| 0.1768 | 41.0 | 3549 | 0.0945 | 0.9703 |
| 0.1633 | 42.0 | 3636 | 0.2250 | 0.9592 |
| 0.1945 | 42.99 | 3722 | 0.2015 | 0.9685 |
| 0.1896 | 44.0 | 3809 | 0.1114 | 0.9666 |
| 0.1629 | 44.99 | 3895 | 0.0954 | 0.9666 |
| 0.1825 | 46.0 | 3982 | 0.0974 | 0.9740 |
| 0.1664 | 46.99 | 4068 | 0.0939 | 0.9703 |
| 0.1535 | 48.0 | 4155 | 0.0935 | 0.9722 |
| 0.1801 | 49.0 | 4242 | 0.0999 | 0.9703 |
| 0.1502 | 49.67 | 4300 | 0.1959 | 0.9703 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"casual",
"sports"
] |
carolinetfls/plant-seedlings-model-ConvNet-final
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-ConvNet-all-train
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2653
- Accuracy: 0.9392
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2307 | 0.25 | 100 | 0.4912 | 0.8729 |
| 0.0652 | 0.49 | 200 | 0.3280 | 0.9085 |
| 0.1854 | 0.74 | 300 | 0.4850 | 0.8711 |
| 0.1831 | 0.98 | 400 | 0.3827 | 0.8938 |
| 0.1636 | 1.23 | 500 | 0.4071 | 0.9012 |
| 0.0868 | 1.47 | 600 | 0.3980 | 0.8999 |
| 0.2298 | 1.72 | 700 | 0.4855 | 0.8846 |
| 0.2291 | 1.97 | 800 | 0.4019 | 0.8883 |
| 0.2698 | 2.21 | 900 | 0.3855 | 0.8944 |
| 0.0923 | 2.46 | 1000 | 0.3690 | 0.8938 |
| 0.1396 | 2.7 | 1100 | 0.4715 | 0.8760 |
| 0.174 | 2.95 | 1200 | 0.3710 | 0.9006 |
| 0.1009 | 3.19 | 1300 | 0.3481 | 0.9030 |
| 0.1162 | 3.44 | 1400 | 0.3502 | 0.9153 |
| 0.1737 | 3.69 | 1500 | 0.4034 | 0.8999 |
| 0.2478 | 3.93 | 1600 | 0.4053 | 0.8913 |
| 0.1471 | 4.18 | 1700 | 0.3555 | 0.9036 |
| 0.1873 | 4.42 | 1800 | 0.3769 | 0.9122 |
| 0.0615 | 4.67 | 1900 | 0.4147 | 0.8987 |
| 0.1718 | 4.91 | 2000 | 0.2779 | 0.9214 |
| 0.1012 | 5.16 | 2100 | 0.3239 | 0.9159 |
| 0.0967 | 5.41 | 2200 | 0.3290 | 0.9079 |
| 0.0873 | 5.65 | 2300 | 0.4057 | 0.9055 |
| 0.0567 | 5.9 | 2400 | 0.3821 | 0.9018 |
| 0.1356 | 6.14 | 2500 | 0.4183 | 0.8944 |
| 0.168 | 6.39 | 2600 | 0.3755 | 0.9067 |
| 0.1592 | 6.63 | 2700 | 0.3413 | 0.9079 |
| 0.1239 | 6.88 | 2800 | 0.3299 | 0.9091 |
| 0.0382 | 7.13 | 2900 | 0.3391 | 0.9165 |
| 0.1167 | 7.37 | 3000 | 0.4274 | 0.8987 |
| 0.109 | 7.62 | 3100 | 0.3952 | 0.9018 |
| 0.0591 | 7.86 | 3200 | 0.4043 | 0.9122 |
| 0.1407 | 8.11 | 3300 | 0.3325 | 0.9134 |
| 0.054 | 8.35 | 3400 | 0.3333 | 0.9177 |
| 0.0633 | 8.6 | 3500 | 0.3275 | 0.9208 |
| 0.1038 | 8.85 | 3600 | 0.3982 | 0.9042 |
| 0.0435 | 9.09 | 3700 | 0.3656 | 0.9190 |
| 0.1549 | 9.34 | 3800 | 0.3367 | 0.9190 |
| 0.2299 | 9.58 | 3900 | 0.3872 | 0.9134 |
| 0.0375 | 9.83 | 4000 | 0.3206 | 0.9245 |
| 0.0204 | 10.07 | 4100 | 0.3133 | 0.9263 |
| 0.1208 | 10.32 | 4200 | 0.3373 | 0.9196 |
| 0.0617 | 10.57 | 4300 | 0.3045 | 0.9220 |
| 0.1426 | 10.81 | 4400 | 0.2972 | 0.9294 |
| 0.0351 | 11.06 | 4500 | 0.3409 | 0.9147 |
| 0.0311 | 11.3 | 4600 | 0.3003 | 0.9233 |
| 0.1255 | 11.55 | 4700 | 0.3447 | 0.9282 |
| 0.0569 | 11.79 | 4800 | 0.2703 | 0.9331 |
| 0.0918 | 12.04 | 4900 | 0.3170 | 0.9245 |
| 0.0656 | 12.29 | 5000 | 0.3223 | 0.9190 |
| 0.0971 | 12.53 | 5100 | 0.3209 | 0.9196 |
| 0.0742 | 12.78 | 5200 | 0.3030 | 0.9282 |
| 0.0662 | 13.02 | 5300 | 0.2780 | 0.9319 |
| 0.0453 | 13.27 | 5400 | 0.3360 | 0.9227 |
| 0.0869 | 13.51 | 5500 | 0.2417 | 0.9343 |
| 0.1786 | 13.76 | 5600 | 0.3078 | 0.9263 |
| 0.1563 | 14.0 | 5700 | 0.3046 | 0.9312 |
| 0.0584 | 14.25 | 5800 | 0.3011 | 0.9288 |
| 0.0783 | 14.5 | 5900 | 0.2705 | 0.9288 |
| 0.0486 | 14.74 | 6000 | 0.2583 | 0.9288 |
| 0.094 | 14.99 | 6100 | 0.2854 | 0.9282 |
| 0.0852 | 15.23 | 6200 | 0.2693 | 0.9325 |
| 0.0665 | 15.48 | 6300 | 0.2754 | 0.9282 |
| 0.0948 | 15.72 | 6400 | 0.2598 | 0.9349 |
| 0.0368 | 15.97 | 6500 | 0.2875 | 0.9355 |
| 0.0031 | 16.22 | 6600 | 0.2679 | 0.9325 |
| 0.0796 | 16.46 | 6700 | 0.2642 | 0.9300 |
| 0.0903 | 16.71 | 6800 | 0.2977 | 0.9269 |
| 0.0952 | 16.95 | 6900 | 0.2615 | 0.9337 |
| 0.1344 | 17.2 | 7000 | 0.2948 | 0.9251 |
| 0.0854 | 17.44 | 7100 | 0.2748 | 0.9368 |
| 0.0891 | 17.69 | 7200 | 0.2386 | 0.9325 |
| 0.1202 | 17.94 | 7300 | 0.2509 | 0.9355 |
| 0.0832 | 18.18 | 7400 | 0.2406 | 0.9398 |
| 0.0949 | 18.43 | 7500 | 0.2356 | 0.9386 |
| 0.0404 | 18.67 | 7600 | 0.2415 | 0.9386 |
| 0.1008 | 18.92 | 7700 | 0.2582 | 0.9355 |
| 0.092 | 19.16 | 7800 | 0.2724 | 0.9325 |
| 0.0993 | 19.41 | 7900 | 0.2655 | 0.9325 |
| 0.0593 | 19.66 | 8000 | 0.2423 | 0.9386 |
| 0.1011 | 19.9 | 8100 | 0.2653 | 0.9392 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
carolinetfls/plant-seedlings-model-resnet-152-2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-resnet-152-2
This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2242
- Accuracy: 0.9381
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.0105 | 0.2 | 100 | 1.8953 | 0.4062 |
| 0.995 | 0.39 | 200 | 1.0372 | 0.6685 |
| 0.9354 | 0.59 | 300 | 0.7713 | 0.7461 |
| 0.6444 | 0.79 | 400 | 0.6037 | 0.8026 |
| 0.6477 | 0.98 | 500 | 0.5981 | 0.7991 |
| 0.6551 | 1.18 | 600 | 0.5224 | 0.8310 |
| 0.6466 | 1.38 | 700 | 0.5216 | 0.8222 |
| 0.4006 | 1.57 | 800 | 0.4244 | 0.8541 |
| 0.4484 | 1.77 | 900 | 0.4513 | 0.8566 |
| 0.5155 | 1.96 | 1000 | 0.4071 | 0.8649 |
| 0.518 | 2.16 | 1100 | 0.4155 | 0.8679 |
| 0.3762 | 2.36 | 1200 | 0.4152 | 0.8733 |
| 0.5409 | 2.55 | 1300 | 0.4038 | 0.8728 |
| 0.3184 | 2.75 | 1400 | 0.3683 | 0.8777 |
| 0.3861 | 2.95 | 1500 | 0.3675 | 0.8811 |
| 0.4824 | 3.14 | 1600 | 0.4404 | 0.8595 |
| 0.2793 | 3.34 | 1700 | 0.3696 | 0.8816 |
| 0.4095 | 3.54 | 1800 | 0.3102 | 0.8939 |
| 0.4151 | 3.73 | 1900 | 0.3558 | 0.8875 |
| 0.4036 | 3.93 | 2000 | 0.3215 | 0.8998 |
| 0.3547 | 4.13 | 2100 | 0.3511 | 0.8885 |
| 0.3071 | 4.32 | 2200 | 0.3376 | 0.8885 |
| 0.3448 | 4.52 | 2300 | 0.3807 | 0.8743 |
| 0.3574 | 4.72 | 2400 | 0.2826 | 0.9106 |
| 0.4435 | 4.91 | 2500 | 0.3275 | 0.9013 |
| 0.2811 | 5.11 | 2600 | 0.3285 | 0.9003 |
| 0.3514 | 5.3 | 2700 | 0.3562 | 0.8949 |
| 0.2323 | 5.5 | 2800 | 0.3023 | 0.9037 |
| 0.3736 | 5.7 | 2900 | 0.3012 | 0.8998 |
| 0.2659 | 5.89 | 3000 | 0.3243 | 0.8964 |
| 0.3934 | 6.09 | 3100 | 0.3007 | 0.9042 |
| 0.1951 | 6.29 | 3200 | 0.2643 | 0.9204 |
| 0.2882 | 6.48 | 3300 | 0.2816 | 0.9175 |
| 0.1887 | 6.68 | 3400 | 0.2669 | 0.9165 |
| 0.3612 | 6.88 | 3500 | 0.3215 | 0.8993 |
| 0.1423 | 7.07 | 3600 | 0.2684 | 0.9170 |
| 0.2935 | 7.27 | 3700 | 0.2826 | 0.9072 |
| 0.1549 | 7.47 | 3800 | 0.2783 | 0.9072 |
| 0.2678 | 7.66 | 3900 | 0.2535 | 0.9140 |
| 0.1954 | 7.86 | 4000 | 0.2578 | 0.9136 |
| 0.2319 | 8.06 | 4100 | 0.2595 | 0.9106 |
| 0.2016 | 8.25 | 4200 | 0.2671 | 0.9160 |
| 0.284 | 8.45 | 4300 | 0.2688 | 0.9136 |
| 0.1635 | 8.64 | 4400 | 0.3101 | 0.9111 |
| 0.2609 | 8.84 | 4500 | 0.2990 | 0.9145 |
| 0.1826 | 9.04 | 4600 | 0.2630 | 0.9077 |
| 0.2091 | 9.23 | 4700 | 0.2712 | 0.9180 |
| 0.1217 | 9.43 | 4800 | 0.2550 | 0.9126 |
| 0.198 | 9.63 | 4900 | 0.2648 | 0.9140 |
| 0.2123 | 9.82 | 5000 | 0.2819 | 0.9116 |
| 0.1399 | 10.02 | 5100 | 0.2690 | 0.9165 |
| 0.2429 | 10.22 | 5200 | 0.2685 | 0.9194 |
| 0.1376 | 10.41 | 5300 | 0.2930 | 0.9091 |
| 0.192 | 10.61 | 5400 | 0.3042 | 0.9101 |
| 0.1872 | 10.81 | 5500 | 0.2693 | 0.9160 |
| 0.1629 | 11.0 | 5600 | 0.2563 | 0.9185 |
| 0.2487 | 11.2 | 5700 | 0.2476 | 0.9258 |
| 0.242 | 11.39 | 5800 | 0.2407 | 0.9283 |
| 0.166 | 11.59 | 5900 | 0.2382 | 0.9317 |
| 0.1181 | 11.79 | 6000 | 0.2576 | 0.9140 |
| 0.1407 | 11.98 | 6100 | 0.2520 | 0.9268 |
| 0.1931 | 12.18 | 6200 | 0.2634 | 0.9204 |
| 0.1064 | 12.38 | 6300 | 0.2655 | 0.9219 |
| 0.1261 | 12.57 | 6400 | 0.2569 | 0.9209 |
| 0.1978 | 12.77 | 6500 | 0.2801 | 0.9131 |
| 0.2031 | 12.97 | 6600 | 0.2541 | 0.9190 |
| 0.1245 | 13.16 | 6700 | 0.2331 | 0.9249 |
| 0.2824 | 13.36 | 6800 | 0.2573 | 0.9199 |
| 0.1302 | 13.56 | 6900 | 0.2452 | 0.9219 |
| 0.0825 | 13.75 | 7000 | 0.2384 | 0.9258 |
| 0.1491 | 13.95 | 7100 | 0.2373 | 0.9303 |
| 0.1859 | 14.15 | 7200 | 0.2623 | 0.9253 |
| 0.2094 | 14.34 | 7300 | 0.2308 | 0.9303 |
| 0.14 | 14.54 | 7400 | 0.2377 | 0.9298 |
| 0.1836 | 14.73 | 7500 | 0.2389 | 0.9268 |
| 0.1347 | 14.93 | 7600 | 0.2205 | 0.9327 |
| 0.0747 | 15.13 | 7700 | 0.2375 | 0.9288 |
| 0.1448 | 15.32 | 7800 | 0.2277 | 0.9342 |
| 0.0885 | 15.52 | 7900 | 0.2560 | 0.9219 |
| 0.0975 | 15.72 | 8000 | 0.2082 | 0.9293 |
| 0.1185 | 15.91 | 8100 | 0.2561 | 0.9214 |
| 0.1544 | 16.11 | 8200 | 0.2599 | 0.9283 |
| 0.0959 | 16.31 | 8300 | 0.2418 | 0.9263 |
| 0.0835 | 16.5 | 8400 | 0.2521 | 0.9352 |
| 0.0846 | 16.7 | 8500 | 0.2258 | 0.9347 |
| 0.1255 | 16.9 | 8600 | 0.2170 | 0.9342 |
| 0.1116 | 17.09 | 8700 | 0.2462 | 0.9288 |
| 0.1331 | 17.29 | 8800 | 0.2123 | 0.9420 |
| 0.0895 | 17.49 | 8900 | 0.2513 | 0.9293 |
| 0.1628 | 17.68 | 9000 | 0.2223 | 0.9283 |
| 0.2152 | 17.88 | 9100 | 0.2144 | 0.9396 |
| 0.1074 | 18.07 | 9200 | 0.2295 | 0.9376 |
| 0.1888 | 18.27 | 9300 | 0.2557 | 0.9337 |
| 0.1014 | 18.47 | 9400 | 0.2007 | 0.9411 |
| 0.0341 | 18.66 | 9500 | 0.2289 | 0.9371 |
| 0.0365 | 18.86 | 9600 | 0.2434 | 0.9337 |
| 0.1099 | 19.06 | 9700 | 0.2222 | 0.9337 |
| 0.1303 | 19.25 | 9800 | 0.2208 | 0.9317 |
| 0.1209 | 19.45 | 9900 | 0.2151 | 0.9401 |
| 0.2119 | 19.65 | 10000 | 0.2209 | 0.9376 |
| 0.0734 | 19.84 | 10100 | 0.2242 | 0.9381 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
GyanShashwat/vit-base-patch16-224-finetuned-flower
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
Gofaone/swin-finetuned-food101
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-finetuned-food101
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2744
- Accuracy: 0.9236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.548 | 1.0 | 1183 | 0.3899 | 0.8848 |
| 0.2979 | 2.0 | 2367 | 0.2940 | 0.9152 |
| 0.1004 | 3.0 | 3549 | 0.2744 | 0.9236 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
carolinetfls/plant-seedlings-model-beit
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-beit
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3466
- Accuracy: 0.8969
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4966 | 0.8 | 100 | 1.2583 | 0.5909 |
| 0.8239 | 1.6 | 200 | 0.9266 | 0.6979 |
| 0.7583 | 2.4 | 300 | 0.6527 | 0.7834 |
| 0.5222 | 3.2 | 400 | 0.5186 | 0.8035 |
| 0.5233 | 4.0 | 500 | 0.5527 | 0.8060 |
| 0.516 | 4.8 | 600 | 0.5558 | 0.8148 |
| 0.4848 | 5.6 | 700 | 0.4780 | 0.8409 |
| 0.1949 | 6.4 | 800 | 0.5876 | 0.8320 |
| 0.2581 | 7.2 | 900 | 0.4364 | 0.8482 |
| 0.2748 | 8.0 | 1000 | 0.3565 | 0.8777 |
| 0.2973 | 8.8 | 1100 | 0.4623 | 0.8615 |
| 0.1655 | 9.6 | 1200 | 0.3700 | 0.8875 |
| 0.1744 | 10.4 | 1300 | 0.3751 | 0.8905 |
| 0.3044 | 11.2 | 1400 | 0.3799 | 0.8919 |
| 0.0981 | 12.0 | 1500 | 0.3466 | 0.8969 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
Soulaimen/convnext-large-224-22k-1k-convnext_bottom
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext-large-224-22k-1k-convnext_bottom
This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0064
- Accuracy: 0.9981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0899 | 0.99 | 86 | 0.0290 | 0.9852 |
| 0.0651 | 2.0 | 173 | 0.0217 | 0.9889 |
| 0.0364 | 2.99 | 259 | 0.0170 | 0.9944 |
| 0.0678 | 4.0 | 346 | 0.0135 | 0.9963 |
| 0.0129 | 4.99 | 432 | 0.0120 | 0.9944 |
| 0.0189 | 6.0 | 519 | 0.0095 | 0.9944 |
| 0.0399 | 7.0 | 606 | 0.0098 | 0.9944 |
| 0.029 | 7.99 | 692 | 0.0121 | 0.9963 |
| 0.0153 | 9.0 | 779 | 0.0068 | 0.9981 |
| 0.0252 | 9.93 | 860 | 0.0064 | 0.9981 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"casual",
"sports"
] |
shahukareem/coral-classification
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 52977124783
- CO2 Emissions (in grams): 0.5895
## Validation Metrics
- Loss: 0.175
- Accuracy: 0.949
- Macro F1: 0.950
- Micro F1: 0.949
- Weighted F1: 0.950
- Macro Precision: 0.957
- Micro Precision: 0.949
- Weighted Precision: 0.956
- Macro Recall: 0.948
- Micro Recall: 0.949
- Weighted Recall: 0.949
|
[
"bleached",
"dead",
"healthy"
] |
carolinetfls/plant-seedlings-model-beit-free-0-6
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-beit-free-0-6
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7557
- Accuracy: 0.7475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.4892 | 0.2 | 100 | 2.4909 | 0.0751 |
| 2.4906 | 0.39 | 200 | 2.4886 | 0.0756 |
| 2.3925 | 0.59 | 300 | 2.3344 | 0.1537 |
| 2.31 | 0.79 | 400 | 2.3306 | 0.1464 |
| 2.2355 | 0.98 | 500 | 2.2335 | 0.1778 |
| 2.2642 | 1.18 | 600 | 2.1889 | 0.1807 |
| 2.0806 | 1.38 | 700 | 2.3229 | 0.1680 |
| 2.1013 | 1.57 | 800 | 2.1519 | 0.2004 |
| 2.0094 | 1.77 | 900 | 2.0611 | 0.2146 |
| 2.0387 | 1.96 | 1000 | 2.0413 | 0.2210 |
| 2.0032 | 2.16 | 1100 | 1.9758 | 0.2618 |
| 1.986 | 2.36 | 1200 | 1.9238 | 0.2638 |
| 2.0885 | 2.55 | 1300 | 1.8944 | 0.2942 |
| 1.8808 | 2.75 | 1400 | 1.9330 | 0.2868 |
| 1.915 | 2.95 | 1500 | 1.8919 | 0.2814 |
| 1.958 | 3.14 | 1600 | 1.8762 | 0.3114 |
| 1.9001 | 3.34 | 1700 | 1.8389 | 0.3232 |
| 1.8572 | 3.54 | 1800 | 1.7978 | 0.3487 |
| 1.9969 | 3.73 | 1900 | 1.9371 | 0.3089 |
| 1.9186 | 3.93 | 2000 | 1.8055 | 0.3502 |
| 1.7591 | 4.13 | 2100 | 1.7695 | 0.3428 |
| 1.8368 | 4.32 | 2200 | 1.7498 | 0.3502 |
| 1.9842 | 4.52 | 2300 | 1.8049 | 0.3193 |
| 1.7606 | 4.72 | 2400 | 1.6730 | 0.3954 |
| 1.7787 | 4.91 | 2500 | 1.7104 | 0.3777 |
| 1.6377 | 5.11 | 2600 | 1.6647 | 0.3870 |
| 1.8834 | 5.3 | 2700 | 1.6325 | 0.3973 |
| 1.6149 | 5.5 | 2800 | 1.6722 | 0.3787 |
| 1.7038 | 5.7 | 2900 | 1.6425 | 0.3973 |
| 1.682 | 5.89 | 3000 | 1.5927 | 0.4180 |
| 1.6326 | 6.09 | 3100 | 1.4982 | 0.4622 |
| 1.5687 | 6.29 | 3200 | 1.4440 | 0.4774 |
| 1.3637 | 6.48 | 3300 | 1.4477 | 0.4877 |
| 1.4079 | 6.68 | 3400 | 1.3827 | 0.5020 |
| 1.3721 | 6.88 | 3500 | 1.4069 | 0.5010 |
| 1.5675 | 7.07 | 3600 | 1.3595 | 0.5083 |
| 1.5725 | 7.27 | 3700 | 1.3790 | 0.4956 |
| 1.4522 | 7.47 | 3800 | 1.3116 | 0.5378 |
| 1.4692 | 7.66 | 3900 | 1.3729 | 0.4980 |
| 1.5073 | 7.86 | 4000 | 1.3799 | 0.5216 |
| 1.2529 | 8.06 | 4100 | 1.2706 | 0.5486 |
| 1.3727 | 8.25 | 4200 | 1.2519 | 0.5535 |
| 1.2451 | 8.45 | 4300 | 1.2595 | 0.5648 |
| 1.339 | 8.64 | 4400 | 1.3614 | 0.5172 |
| 1.2858 | 8.84 | 4500 | 1.3028 | 0.5393 |
| 1.1039 | 9.04 | 4600 | 1.2309 | 0.5771 |
| 1.0351 | 9.23 | 4700 | 1.2678 | 0.5609 |
| 1.1125 | 9.43 | 4800 | 1.2786 | 0.5624 |
| 1.1667 | 9.63 | 4900 | 1.2131 | 0.5840 |
| 1.1386 | 9.82 | 5000 | 1.1359 | 0.6154 |
| 1.1888 | 10.02 | 5100 | 1.1309 | 0.6041 |
| 1.1777 | 10.22 | 5200 | 1.1288 | 0.6287 |
| 1.3693 | 10.41 | 5300 | 1.3827 | 0.5182 |
| 1.1016 | 10.61 | 5400 | 1.2255 | 0.5594 |
| 1.1527 | 10.81 | 5500 | 1.0772 | 0.6434 |
| 1.1039 | 11.0 | 5600 | 1.1032 | 0.6100 |
| 1.2502 | 11.2 | 5700 | 1.1230 | 0.6169 |
| 1.0818 | 11.39 | 5800 | 1.0750 | 0.6302 |
| 1.0872 | 11.59 | 5900 | 1.0397 | 0.6331 |
| 1.0425 | 11.79 | 6000 | 1.0231 | 0.6483 |
| 1.0791 | 11.98 | 6100 | 1.0250 | 0.6636 |
| 0.9736 | 12.18 | 6200 | 1.0879 | 0.6267 |
| 0.9788 | 12.38 | 6300 | 1.1334 | 0.5968 |
| 0.8982 | 12.57 | 6400 | 0.9934 | 0.6528 |
| 1.077 | 12.77 | 6500 | 0.9698 | 0.6812 |
| 1.0347 | 12.97 | 6600 | 1.0265 | 0.6513 |
| 0.9159 | 13.16 | 6700 | 0.9442 | 0.6788 |
| 1.1187 | 13.36 | 6800 | 0.9738 | 0.6685 |
| 0.9624 | 13.56 | 6900 | 1.0008 | 0.6699 |
| 0.922 | 13.75 | 7000 | 0.9502 | 0.6906 |
| 0.9317 | 13.95 | 7100 | 0.9687 | 0.6758 |
| 0.9979 | 14.15 | 7200 | 0.9869 | 0.6768 |
| 0.8362 | 14.34 | 7300 | 0.9220 | 0.6994 |
| 0.8449 | 14.54 | 7400 | 0.9181 | 0.6861 |
| 0.9678 | 14.73 | 7500 | 0.9789 | 0.6729 |
| 0.9119 | 14.93 | 7600 | 0.8879 | 0.7009 |
| 0.9517 | 15.13 | 7700 | 0.8816 | 0.6994 |
| 0.9688 | 15.32 | 7800 | 0.8803 | 0.7117 |
| 0.8625 | 15.52 | 7900 | 0.8782 | 0.7038 |
| 0.9121 | 15.72 | 8000 | 0.8225 | 0.7191 |
| 0.9035 | 15.91 | 8100 | 0.8649 | 0.7087 |
| 0.8762 | 16.11 | 8200 | 0.8427 | 0.7102 |
| 0.7708 | 16.31 | 8300 | 0.8685 | 0.7117 |
| 0.8893 | 16.5 | 8400 | 0.8178 | 0.7264 |
| 0.9584 | 16.7 | 8500 | 0.8709 | 0.7092 |
| 0.757 | 16.9 | 8600 | 0.8244 | 0.7254 |
| 0.8184 | 17.09 | 8700 | 0.8128 | 0.7240 |
| 0.8858 | 17.29 | 8800 | 0.8360 | 0.7156 |
| 0.7116 | 17.49 | 8900 | 0.7952 | 0.7279 |
| 0.9579 | 17.68 | 9000 | 0.8263 | 0.7274 |
| 0.7037 | 17.88 | 9100 | 0.7884 | 0.7348 |
| 1.0359 | 18.07 | 9200 | 0.8118 | 0.7402 |
| 1.067 | 18.27 | 9300 | 0.8203 | 0.7186 |
| 0.8503 | 18.47 | 9400 | 0.7918 | 0.7362 |
| 0.8552 | 18.66 | 9500 | 0.7972 | 0.7382 |
| 0.7498 | 18.86 | 9600 | 0.8038 | 0.7343 |
| 0.8542 | 19.06 | 9700 | 0.7799 | 0.7333 |
| 0.9539 | 19.25 | 9800 | 0.7795 | 0.7333 |
| 0.7369 | 19.45 | 9900 | 0.8103 | 0.7269 |
| 0.6637 | 19.65 | 10000 | 0.7597 | 0.7441 |
| 0.6712 | 19.84 | 10100 | 0.7557 | 0.7475 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
sergiocannata/prove_melanomaprova_melanoma
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prove_melanomaprova_melanoma
This model is a fine-tuned version of [UnipaPolitoUnimore/vit-large-patch32-384-melanoma](https://huggingface.co/UnipaPolitoUnimore/vit-large-patch32-384-melanoma) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5191
- Accuracy: 0.8467
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8964 | 0.99 | 31 | 1.0906 | 0.52 |
| 0.6588 | 1.98 | 62 | 1.0817 | 0.52 |
| 0.6774 | 2.98 | 93 | 0.9474 | 0.52 |
| 0.7785 | 4.0 | 125 | 0.8185 | 0.6267 |
| 0.6732 | 4.99 | 156 | 0.7531 | 0.7267 |
| 0.5438 | 5.98 | 187 | 0.6972 | 0.7333 |
| 0.5497 | 6.98 | 218 | 0.6714 | 0.7533 |
| 0.4161 | 8.0 | 250 | 0.6440 | 0.7667 |
| 0.4968 | 8.99 | 281 | 0.6438 | 0.78 |
| 0.5861 | 9.98 | 312 | 0.6266 | 0.7933 |
| 0.5182 | 10.98 | 343 | 0.6158 | 0.7867 |
| 0.6797 | 12.0 | 375 | 0.6237 | 0.8133 |
| 0.622 | 12.99 | 406 | 0.5858 | 0.8333 |
| 0.6419 | 13.98 | 437 | 0.5735 | 0.8267 |
| 0.3727 | 14.98 | 468 | 0.5641 | 0.8133 |
| 0.3822 | 16.0 | 500 | 0.5520 | 0.8267 |
| 0.4766 | 16.99 | 531 | 0.5642 | 0.8267 |
| 0.4791 | 17.98 | 562 | 0.5309 | 0.8267 |
| 0.3918 | 18.98 | 593 | 0.5749 | 0.8267 |
| 0.3847 | 20.0 | 625 | 0.5317 | 0.84 |
| 0.3722 | 20.99 | 656 | 0.5719 | 0.8267 |
| 0.5402 | 21.98 | 687 | 0.5316 | 0.84 |
| 0.4358 | 22.98 | 718 | 0.5292 | 0.8333 |
| 0.2957 | 24.0 | 750 | 0.5172 | 0.8467 |
| 0.4801 | 24.99 | 781 | 0.5376 | 0.84 |
| 0.3656 | 25.98 | 812 | 0.5118 | 0.8333 |
| 0.3956 | 26.98 | 843 | 0.5081 | 0.8533 |
| 0.3343 | 28.0 | 875 | 0.5198 | 0.8533 |
| 0.3839 | 28.99 | 906 | 0.5269 | 0.8467 |
| 0.4286 | 29.98 | 937 | 0.5163 | 0.8467 |
| 0.2736 | 30.98 | 968 | 0.5359 | 0.8333 |
| 0.3465 | 32.0 | 1000 | 0.5277 | 0.84 |
| 0.4244 | 32.99 | 1031 | 0.5385 | 0.8333 |
| 0.308 | 33.98 | 1062 | 0.5141 | 0.8533 |
| 0.3494 | 34.98 | 1093 | 0.5129 | 0.8533 |
| 0.3851 | 36.0 | 1125 | 0.5199 | 0.84 |
| 0.3949 | 36.99 | 1156 | 0.5250 | 0.84 |
| 0.3235 | 37.98 | 1187 | 0.5142 | 0.8533 |
| 0.3076 | 38.98 | 1218 | 0.5166 | 0.8533 |
| 0.3679 | 39.68 | 1240 | 0.5191 | 0.8467 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"melanoma",
"nevus",
"seborrheic_keratosis"
] |
carolinetfls/plant-seedlings-model-ConvNet-freeze-0-6-20ep
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-ConvNet-freeze-0-6-24ep
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2699
- eval_accuracy: 0.9269
- eval_runtime: 20.7217
- eval_samples_per_second: 78.613
- eval_steps_per_second: 9.845
- epoch: 18.92
- step: 7700
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
Gofaone/swin-kidneys
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-kidneys
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0035 | 1.0 | 109 | 0.0001 | 1.0 |
| 0.0001 | 2.0 | 219 | 0.0000 | 1.0 |
| 0.0001 | 3.0 | 328 | 0.0011 | 0.9997 |
| 0.0001 | 4.0 | 438 | 0.0000 | 1.0 |
| 0.0 | 4.98 | 545 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"kidney_normal",
"kidney_tumor"
] |
michotic/vit_cancer_model
|
Fine-tuned ViT model trained to predict types of lung cancer / no-cancer from CT-scans of the torso.
|
[
"adeno carcinoma",
"large cell carcinoma",
"normal",
"squamous cell carcinoma"
] |
carolinetfls/plant-seedlings-model-mit
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-mit
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2052
- Accuracy: 0.9401
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.459 | 0.2 | 100 | 2.4084 | 0.1424 |
| 1.7264 | 0.39 | 200 | 1.5604 | 0.4430 |
| 1.427 | 0.59 | 300 | 1.2719 | 0.5447 |
| 1.1796 | 0.79 | 400 | 0.9608 | 0.6469 |
| 0.6449 | 0.98 | 500 | 0.9086 | 0.6783 |
| 0.819 | 1.18 | 600 | 0.8235 | 0.7230 |
| 0.711 | 1.38 | 700 | 0.8286 | 0.7161 |
| 0.6829 | 1.57 | 800 | 0.6853 | 0.7829 |
| 0.7093 | 1.77 | 900 | 0.8823 | 0.7112 |
| 0.6265 | 1.96 | 1000 | 0.5434 | 0.8129 |
| 0.6062 | 2.16 | 1100 | 0.4865 | 0.8301 |
| 0.6318 | 2.36 | 1200 | 0.5239 | 0.8256 |
| 0.5195 | 2.55 | 1300 | 0.5997 | 0.7809 |
| 0.5847 | 2.75 | 1400 | 0.5282 | 0.8099 |
| 0.4684 | 2.95 | 1500 | 0.4301 | 0.8502 |
| 0.7026 | 3.14 | 1600 | 0.4628 | 0.8522 |
| 0.443 | 3.34 | 1700 | 0.4201 | 0.8492 |
| 0.6532 | 3.54 | 1800 | 0.4979 | 0.8330 |
| 0.5021 | 3.73 | 1900 | 0.5098 | 0.8202 |
| 0.4203 | 3.93 | 2000 | 0.4277 | 0.8512 |
| 0.4201 | 4.13 | 2100 | 0.4046 | 0.8649 |
| 0.397 | 4.32 | 2200 | 0.5747 | 0.8158 |
| 0.472 | 4.52 | 2300 | 0.5175 | 0.8237 |
| 0.5614 | 4.72 | 2400 | 0.4351 | 0.8443 |
| 0.3184 | 4.91 | 2500 | 0.3635 | 0.8787 |
| 0.3409 | 5.11 | 2600 | 0.4374 | 0.8571 |
| 0.3132 | 5.3 | 2700 | 0.3622 | 0.8767 |
| 0.3928 | 5.5 | 2800 | 0.3522 | 0.8797 |
| 0.4538 | 5.7 | 2900 | 0.3652 | 0.8718 |
| 0.5516 | 5.89 | 3000 | 0.4128 | 0.8689 |
| 0.4113 | 6.09 | 3100 | 0.3973 | 0.8649 |
| 0.3365 | 6.29 | 3200 | 0.4116 | 0.8635 |
| 0.4611 | 6.48 | 3300 | 0.3312 | 0.8846 |
| 0.312 | 6.68 | 3400 | 0.3888 | 0.8679 |
| 0.3811 | 6.88 | 3500 | 0.3388 | 0.8841 |
| 0.3711 | 7.07 | 3600 | 0.3300 | 0.8954 |
| 0.4593 | 7.27 | 3700 | 0.3491 | 0.8831 |
| 0.5211 | 7.47 | 3800 | 0.3682 | 0.8895 |
| 0.2319 | 7.66 | 3900 | 0.3326 | 0.8861 |
| 0.3811 | 7.86 | 4000 | 0.3407 | 0.8910 |
| 0.4044 | 8.06 | 4100 | 0.3076 | 0.9028 |
| 0.367 | 8.25 | 4200 | 0.3126 | 0.9023 |
| 0.3862 | 8.45 | 4300 | 0.3281 | 0.8954 |
| 0.2489 | 8.64 | 4400 | 0.3166 | 0.8929 |
| 0.3197 | 8.84 | 4500 | 0.3564 | 0.8802 |
| 0.3114 | 9.04 | 4600 | 0.2978 | 0.8969 |
| 0.3589 | 9.23 | 4700 | 0.3438 | 0.8895 |
| 0.3075 | 9.43 | 4800 | 0.2894 | 0.9082 |
| 0.3862 | 9.63 | 4900 | 0.2880 | 0.9047 |
| 0.3319 | 9.82 | 5000 | 0.3628 | 0.8915 |
| 0.3022 | 10.02 | 5100 | 0.2624 | 0.9145 |
| 0.2697 | 10.22 | 5200 | 0.3866 | 0.8851 |
| 0.218 | 10.41 | 5300 | 0.2632 | 0.9101 |
| 0.3331 | 10.61 | 5400 | 0.3117 | 0.9023 |
| 0.3043 | 10.81 | 5500 | 0.3604 | 0.8900 |
| 0.3105 | 11.0 | 5600 | 0.2847 | 0.9111 |
| 0.1758 | 11.2 | 5700 | 0.3144 | 0.9082 |
| 0.2081 | 11.39 | 5800 | 0.2898 | 0.9101 |
| 0.4005 | 11.59 | 5900 | 0.3138 | 0.8998 |
| 0.264 | 11.79 | 6000 | 0.2792 | 0.9136 |
| 0.2765 | 11.98 | 6100 | 0.3021 | 0.9003 |
| 0.2595 | 12.18 | 6200 | 0.2625 | 0.9091 |
| 0.2745 | 12.38 | 6300 | 0.3078 | 0.9057 |
| 0.2437 | 12.57 | 6400 | 0.2533 | 0.9194 |
| 0.3765 | 12.77 | 6500 | 0.2972 | 0.9008 |
| 0.2911 | 12.97 | 6600 | 0.2909 | 0.9096 |
| 0.2335 | 13.16 | 6700 | 0.2684 | 0.9136 |
| 0.3099 | 13.36 | 6800 | 0.3057 | 0.9086 |
| 0.2377 | 13.56 | 6900 | 0.2862 | 0.9140 |
| 0.3159 | 13.75 | 7000 | 0.2271 | 0.9273 |
| 0.1893 | 13.95 | 7100 | 0.2519 | 0.9244 |
| 0.1703 | 14.15 | 7200 | 0.2616 | 0.9209 |
| 0.2527 | 14.34 | 7300 | 0.2393 | 0.9293 |
| 0.3772 | 14.54 | 7400 | 0.2662 | 0.9160 |
| 0.2574 | 14.73 | 7500 | 0.2724 | 0.9155 |
| 0.1803 | 14.93 | 7600 | 0.2549 | 0.9199 |
| 0.2935 | 15.13 | 7700 | 0.2561 | 0.9185 |
| 0.2105 | 15.32 | 7800 | 0.2202 | 0.9244 |
| 0.2877 | 15.52 | 7900 | 0.2428 | 0.9234 |
| 0.2467 | 15.72 | 8000 | 0.2531 | 0.9229 |
| 0.2955 | 15.91 | 8100 | 0.3258 | 0.9194 |
| 0.3136 | 16.11 | 8200 | 0.2430 | 0.9263 |
| 0.2543 | 16.31 | 8300 | 0.2502 | 0.9204 |
| 0.161 | 16.5 | 8400 | 0.2241 | 0.9352 |
| 0.194 | 16.7 | 8500 | 0.2313 | 0.9298 |
| 0.1951 | 16.9 | 8600 | 0.2446 | 0.9219 |
| 0.2515 | 17.09 | 8700 | 0.2476 | 0.9224 |
| 0.1274 | 17.29 | 8800 | 0.2445 | 0.9273 |
| 0.3035 | 17.49 | 8900 | 0.2704 | 0.9239 |
| 0.2253 | 17.68 | 9000 | 0.2436 | 0.9332 |
| 0.0982 | 17.88 | 9100 | 0.2523 | 0.9327 |
| 0.1778 | 18.07 | 9200 | 0.2425 | 0.9322 |
| 0.1362 | 18.27 | 9300 | 0.2653 | 0.9219 |
| 0.2342 | 18.47 | 9400 | 0.2076 | 0.9401 |
| 0.2231 | 18.66 | 9500 | 0.2238 | 0.9361 |
| 0.2159 | 18.86 | 9600 | 0.2115 | 0.9357 |
| 0.1826 | 19.06 | 9700 | 0.2079 | 0.9332 |
| 0.2221 | 19.25 | 9800 | 0.2003 | 0.9366 |
| 0.136 | 19.45 | 9900 | 0.2170 | 0.9401 |
| 0.0959 | 19.65 | 10000 | 0.1891 | 0.9440 |
| 0.1525 | 19.84 | 10100 | 0.2052 | 0.9401 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
seena18/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9108
- Accuracy: 0.8405
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4869 | 1.0 | 125 | 1.4082 | 0.754 |
| 1.0009 | 2.0 | 250 | 1.0099 | 0.823 |
| 0.8853 | 3.0 | 375 | 0.9108 | 0.8405 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"dam",
"parking lot",
"sparse shrub land",
"works",
"oil field",
"meadow",
"ground track field",
"detached house",
"golf course",
"forest",
"desert",
"lake",
"greenhouse",
"beach",
"paddy field",
"ice land",
"bare land",
"storage tank",
"basketball court",
"island",
"substation",
"mobile home park",
"cemetery",
"pier",
"quarry",
"solar power plant",
"helipad",
"roundabout",
"runway",
"wastewater plant",
"river",
"apartment",
"dry field",
"intersection",
"bridge",
"swimming pool",
"commercial area",
"church",
"road",
"orchard",
"terraced field",
"stadium",
"train station",
"railway",
"viaduct",
"mine",
"wind turbine",
"rock land",
"baseball field",
"apron",
"tennis court"
] |
carolinetfls/plant-seedlings-model-swin
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-swin
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2169
- Accuracy: 0.9474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8259 | 0.2 | 100 | 0.7181 | 0.7520 |
| 1.0121 | 0.39 | 200 | 0.7504 | 0.7092 |
| 0.5952 | 0.59 | 300 | 0.6254 | 0.7986 |
| 0.6031 | 0.79 | 400 | 0.4595 | 0.8438 |
| 0.637 | 0.98 | 500 | 0.5830 | 0.8080 |
| 0.5896 | 1.18 | 600 | 0.5042 | 0.8384 |
| 0.6758 | 1.38 | 700 | 0.4827 | 0.8325 |
| 0.543 | 1.57 | 800 | 0.4713 | 0.8433 |
| 0.3312 | 1.77 | 900 | 0.4752 | 0.8546 |
| 0.5559 | 1.96 | 1000 | 0.4578 | 0.8369 |
| 0.4303 | 2.16 | 1100 | 0.5034 | 0.8389 |
| 0.5705 | 2.36 | 1200 | 0.4322 | 0.8502 |
| 0.5369 | 2.55 | 1300 | 0.4646 | 0.8404 |
| 0.3628 | 2.75 | 1400 | 0.3984 | 0.8659 |
| 0.4071 | 2.95 | 1500 | 0.3872 | 0.8689 |
| 0.4988 | 3.14 | 1600 | 0.3543 | 0.8792 |
| 0.4607 | 3.34 | 1700 | 0.3933 | 0.8674 |
| 0.3342 | 3.54 | 1800 | 0.3883 | 0.8639 |
| 0.4141 | 3.73 | 1900 | 0.3886 | 0.8644 |
| 0.5513 | 3.93 | 2000 | 0.3335 | 0.8900 |
| 0.4659 | 4.13 | 2100 | 0.4286 | 0.8590 |
| 0.2263 | 4.32 | 2200 | 0.3587 | 0.8772 |
| 0.4518 | 4.52 | 2300 | 0.3332 | 0.8870 |
| 0.3422 | 4.72 | 2400 | 0.2723 | 0.9062 |
| 0.6113 | 4.91 | 2500 | 0.2811 | 0.9057 |
| 0.3636 | 5.11 | 2600 | 0.3157 | 0.8939 |
| 0.2794 | 5.3 | 2700 | 0.2773 | 0.9082 |
| 0.3486 | 5.5 | 2800 | 0.3099 | 0.8978 |
| 0.2563 | 5.7 | 2900 | 0.3077 | 0.9052 |
| 0.3709 | 5.89 | 3000 | 0.3650 | 0.8836 |
| 0.3732 | 6.09 | 3100 | 0.3132 | 0.8988 |
| 0.2218 | 6.29 | 3200 | 0.2947 | 0.9052 |
| 0.2488 | 6.48 | 3300 | 0.2737 | 0.9131 |
| 0.2689 | 6.68 | 3400 | 0.3471 | 0.8924 |
| 0.3212 | 6.88 | 3500 | 0.3447 | 0.8905 |
| 0.3604 | 7.07 | 3600 | 0.2974 | 0.9086 |
| 0.2492 | 7.27 | 3700 | 0.3057 | 0.8993 |
| 0.1674 | 7.47 | 3800 | 0.3241 | 0.9032 |
| 0.3248 | 7.66 | 3900 | 0.2952 | 0.9077 |
| 0.204 | 7.86 | 4000 | 0.2883 | 0.9111 |
| 0.2783 | 8.06 | 4100 | 0.3017 | 0.9047 |
| 0.3721 | 8.25 | 4200 | 0.2782 | 0.9136 |
| 0.2554 | 8.45 | 4300 | 0.2625 | 0.9170 |
| 0.1104 | 8.64 | 4400 | 0.2590 | 0.9190 |
| 0.247 | 8.84 | 4500 | 0.3021 | 0.9096 |
| 0.3316 | 9.04 | 4600 | 0.3190 | 0.8988 |
| 0.3214 | 9.23 | 4700 | 0.2883 | 0.9140 |
| 0.192 | 9.43 | 4800 | 0.2770 | 0.9155 |
| 0.3568 | 9.63 | 4900 | 0.2475 | 0.9229 |
| 0.3365 | 9.82 | 5000 | 0.2568 | 0.9229 |
| 0.1226 | 10.02 | 5100 | 0.2534 | 0.9204 |
| 0.2359 | 10.22 | 5200 | 0.2679 | 0.9131 |
| 0.1623 | 10.41 | 5300 | 0.3127 | 0.9204 |
| 0.2369 | 10.61 | 5400 | 0.2779 | 0.9170 |
| 0.1234 | 10.81 | 5500 | 0.2486 | 0.9273 |
| 0.1823 | 11.0 | 5600 | 0.2608 | 0.9239 |
| 0.2875 | 11.2 | 5700 | 0.2612 | 0.9190 |
| 0.1408 | 11.39 | 5800 | 0.2208 | 0.9298 |
| 0.1094 | 11.59 | 5900 | 0.2399 | 0.9332 |
| 0.213 | 11.79 | 6000 | 0.2636 | 0.9209 |
| 0.1599 | 11.98 | 6100 | 0.2458 | 0.9249 |
| 0.2565 | 12.18 | 6200 | 0.2698 | 0.9204 |
| 0.0773 | 12.38 | 6300 | 0.2348 | 0.9322 |
| 0.1515 | 12.57 | 6400 | 0.2370 | 0.9263 |
| 0.2308 | 12.77 | 6500 | 0.2185 | 0.9307 |
| 0.2009 | 12.97 | 6600 | 0.2211 | 0.9342 |
| 0.2126 | 13.16 | 6700 | 0.2552 | 0.9342 |
| 0.1348 | 13.36 | 6800 | 0.2206 | 0.9371 |
| 0.1473 | 13.56 | 6900 | 0.2199 | 0.9357 |
| 0.1861 | 13.75 | 7000 | 0.2512 | 0.9224 |
| 0.1136 | 13.95 | 7100 | 0.2803 | 0.9214 |
| 0.1726 | 14.15 | 7200 | 0.2201 | 0.9361 |
| 0.202 | 14.34 | 7300 | 0.2105 | 0.9371 |
| 0.2043 | 14.54 | 7400 | 0.2472 | 0.9263 |
| 0.1427 | 14.73 | 7500 | 0.2250 | 0.9381 |
| 0.1599 | 14.93 | 7600 | 0.2270 | 0.9391 |
| 0.1216 | 15.13 | 7700 | 0.2409 | 0.9307 |
| 0.2869 | 15.32 | 7800 | 0.2208 | 0.9386 |
| 0.1254 | 15.52 | 7900 | 0.2298 | 0.9332 |
| 0.1314 | 15.72 | 8000 | 0.1959 | 0.9416 |
| 0.1106 | 15.91 | 8100 | 0.2183 | 0.9342 |
| 0.2211 | 16.11 | 8200 | 0.2581 | 0.9337 |
| 0.1589 | 16.31 | 8300 | 0.2091 | 0.9381 |
| 0.0791 | 16.5 | 8400 | 0.1792 | 0.9455 |
| 0.0849 | 16.7 | 8500 | 0.2481 | 0.9298 |
| 0.089 | 16.9 | 8600 | 0.2143 | 0.9386 |
| 0.0609 | 17.09 | 8700 | 0.2020 | 0.9524 |
| 0.1509 | 17.29 | 8800 | 0.2039 | 0.9396 |
| 0.0934 | 17.49 | 8900 | 0.2242 | 0.9322 |
| 0.0398 | 17.68 | 9000 | 0.1891 | 0.9460 |
| 0.1106 | 17.88 | 9100 | 0.1939 | 0.9470 |
| 0.1742 | 18.07 | 9200 | 0.1965 | 0.9479 |
| 0.1015 | 18.27 | 9300 | 0.1886 | 0.9440 |
| 0.089 | 18.47 | 9400 | 0.1851 | 0.9479 |
| 0.1393 | 18.66 | 9500 | 0.1844 | 0.9484 |
| 0.0849 | 18.86 | 9600 | 0.2205 | 0.9396 |
| 0.0708 | 19.06 | 9700 | 0.1888 | 0.9435 |
| 0.1037 | 19.25 | 9800 | 0.2070 | 0.9450 |
| 0.1109 | 19.45 | 9900 | 0.2079 | 0.9460 |
| 0.0533 | 19.65 | 10000 | 0.2036 | 0.9489 |
| 0.0757 | 19.84 | 10100 | 0.2169 | 0.9474 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
Binssin/autotrain-faceclassifiervideo-53244125373
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 53244125373
- CO2 Emissions (in grams): 0.3567
## Validation Metrics
- Loss: 0.002
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6"
] |
carolinetfls/plant-seedlings-model-ResNet18-freeze-0-12-20ep
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plant-seedlings-model-ResNet18-freeze-0-12-20ep
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2060
- Accuracy: 0.9327
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4528 | 0.25 | 128 | 0.6686 | 0.7878 |
| 0.4291 | 0.5 | 256 | 0.6259 | 0.7903 |
| 0.4961 | 0.75 | 384 | 0.5677 | 0.8055 |
| 0.4637 | 1.01 | 512 | 0.5073 | 0.8330 |
| 0.6897 | 1.26 | 640 | 0.5817 | 0.8060 |
| 0.5257 | 1.51 | 768 | 0.5118 | 0.8276 |
| 0.5381 | 1.76 | 896 | 0.4809 | 0.8384 |
| 0.4736 | 2.01 | 1024 | 0.3976 | 0.8595 |
| 0.4967 | 2.26 | 1152 | 0.4192 | 0.8566 |
| 0.4505 | 2.51 | 1280 | 0.4128 | 0.8590 |
| 0.4211 | 2.77 | 1408 | 0.4075 | 0.8576 |
| 0.3877 | 3.02 | 1536 | 0.3796 | 0.8738 |
| 0.3134 | 3.27 | 1664 | 0.3906 | 0.8762 |
| 0.3596 | 3.52 | 1792 | 0.3703 | 0.8846 |
| 0.3859 | 3.77 | 1920 | 0.3125 | 0.8954 |
| 0.4076 | 4.02 | 2048 | 0.3718 | 0.8615 |
| 0.3109 | 4.28 | 2176 | 0.3449 | 0.8924 |
| 0.4588 | 4.53 | 2304 | 0.3377 | 0.8875 |
| 0.2923 | 4.78 | 2432 | 0.3001 | 0.8998 |
| 0.3273 | 5.03 | 2560 | 0.3187 | 0.8880 |
| 0.2541 | 5.28 | 2688 | 0.3432 | 0.8856 |
| 0.3059 | 5.53 | 2816 | 0.3236 | 0.8988 |
| 0.2979 | 5.78 | 2944 | 0.3532 | 0.8851 |
| 0.2748 | 6.04 | 3072 | 0.3407 | 0.8885 |
| 0.3537 | 6.29 | 3200 | 0.2925 | 0.8988 |
| 0.3364 | 6.54 | 3328 | 0.3071 | 0.9047 |
| 0.2135 | 6.79 | 3456 | 0.2765 | 0.9077 |
| 0.2023 | 7.04 | 3584 | 0.2919 | 0.9037 |
| 0.1977 | 7.29 | 3712 | 0.2812 | 0.8978 |
| 0.4042 | 7.54 | 3840 | 0.2954 | 0.8998 |
| 0.3662 | 7.8 | 3968 | 0.2857 | 0.9018 |
| 0.1872 | 8.05 | 4096 | 0.2504 | 0.9140 |
| 0.3959 | 8.3 | 4224 | 0.2984 | 0.8993 |
| 0.2403 | 8.55 | 4352 | 0.2847 | 0.8998 |
| 0.3689 | 8.8 | 4480 | 0.2872 | 0.9023 |
| 0.2819 | 9.05 | 4608 | 0.3104 | 0.9008 |
| 0.1926 | 9.3 | 4736 | 0.2871 | 0.8969 |
| 0.2371 | 9.56 | 4864 | 0.2733 | 0.9082 |
| 0.2566 | 9.81 | 4992 | 0.2816 | 0.9101 |
| 0.2174 | 10.06 | 5120 | 0.2719 | 0.9160 |
| 0.2359 | 10.31 | 5248 | 0.2497 | 0.9175 |
| 0.2986 | 10.56 | 5376 | 0.2847 | 0.9096 |
| 0.2239 | 10.81 | 5504 | 0.2493 | 0.9180 |
| 0.2132 | 11.06 | 5632 | 0.2567 | 0.9121 |
| 0.1934 | 11.32 | 5760 | 0.2722 | 0.9028 |
| 0.2026 | 11.57 | 5888 | 0.2456 | 0.9229 |
| 0.2457 | 11.82 | 6016 | 0.2483 | 0.9234 |
| 0.2537 | 12.07 | 6144 | 0.2409 | 0.9165 |
| 0.193 | 12.32 | 6272 | 0.2215 | 0.9239 |
| 0.1738 | 12.57 | 6400 | 0.2421 | 0.9165 |
| 0.2925 | 12.83 | 6528 | 0.2499 | 0.9150 |
| 0.1173 | 13.08 | 6656 | 0.2174 | 0.9258 |
| 0.2147 | 13.33 | 6784 | 0.2917 | 0.9131 |
| 0.1581 | 13.58 | 6912 | 0.2734 | 0.9175 |
| 0.1349 | 13.83 | 7040 | 0.2485 | 0.9165 |
| 0.1212 | 14.08 | 7168 | 0.2247 | 0.9268 |
| 0.2178 | 14.33 | 7296 | 0.2289 | 0.9268 |
| 0.0879 | 14.59 | 7424 | 0.2512 | 0.9219 |
| 0.2006 | 14.84 | 7552 | 0.2321 | 0.9293 |
| 0.2308 | 15.09 | 7680 | 0.2491 | 0.9263 |
| 0.2137 | 15.34 | 7808 | 0.2270 | 0.9312 |
| 0.1112 | 15.59 | 7936 | 0.2205 | 0.9249 |
| 0.1477 | 15.84 | 8064 | 0.2328 | 0.9307 |
| 0.1794 | 16.09 | 8192 | 0.2051 | 0.9332 |
| 0.0596 | 16.35 | 8320 | 0.2234 | 0.9347 |
| 0.0533 | 16.6 | 8448 | 0.2469 | 0.9293 |
| 0.1096 | 16.85 | 8576 | 0.1871 | 0.9401 |
| 0.1117 | 17.1 | 8704 | 0.2302 | 0.9249 |
| 0.1349 | 17.35 | 8832 | 0.2084 | 0.9391 |
| 0.1031 | 17.6 | 8960 | 0.2200 | 0.9283 |
| 0.2428 | 17.85 | 9088 | 0.2201 | 0.9298 |
| 0.1283 | 18.11 | 9216 | 0.2293 | 0.9273 |
| 0.1688 | 18.36 | 9344 | 0.2120 | 0.9307 |
| 0.0877 | 18.61 | 9472 | 0.2200 | 0.9229 |
| 0.1508 | 18.86 | 9600 | 0.2204 | 0.9327 |
| 0.0868 | 19.11 | 9728 | 0.2224 | 0.9293 |
| 0.211 | 19.36 | 9856 | 0.1988 | 0.9401 |
| 0.1059 | 19.61 | 9984 | 0.2082 | 0.9322 |
| 0.182 | 19.87 | 10112 | 0.2060 | 0.9327 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"black-grass",
"charlock",
"small-flowered cranesbill",
"sugar beet",
"cleavers",
"common chickweed",
"common wheat",
"fat hen",
"loose silky-bent",
"maize",
"scentless mayweed",
"shepherds purse"
] |
Santenana/ClasificacionComida-Santiago-Garcia
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ClasificacionComida-Santiago-Garcia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the snacks dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9699
- Accuracy: 0.0620
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.0127 | 0.83 | 500 | 3.0029 | 0.0525 |
| 2.9986 | 1.65 | 1000 | 2.9878 | 0.0536 |
| 2.9891 | 2.48 | 1500 | 2.9693 | 0.0662 |
| 2.9783 | 3.31 | 2000 | 2.9699 | 0.0620 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"apple",
"banana",
"juice",
"muffin",
"orange",
"pineapple",
"popcorn",
"pretzel",
"salad",
"strawberry",
"waffle",
"watermelon",
"cake",
"candy",
"carrot",
"cookie",
"doughnut",
"grape",
"hot dog",
"ice cream"
] |
NadaElmasry/swin-tiny-patch4-window7-224-finetuned-imageclds
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-imageclds
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.019 | 1.0 | 241 | 0.0031 | 0.9990 |
| 0.0364 | 2.0 | 482 | 0.0020 | 0.9995 |
| 0.0151 | 3.0 | 723 | 0.0003 | 0.9999 |
| 0.025 | 4.0 | 964 | 0.0005 | 0.9999 |
| 0.0191 | 5.0 | 1205 | 0.0003 | 1.0 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
[
"chart",
"math",
"scene",
"text"
] |
NadaElmasry/resnet-50-finetuned-imageclds
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50-finetuned-imageclds
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0044
- Accuracy: 0.9992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.177 | 1.0 | 241 | 0.0830 | 0.9942 |
| 0.0597 | 2.0 | 482 | 0.0107 | 0.9982 |
| 0.0387 | 3.0 | 723 | 0.0068 | 0.9988 |
| 0.0381 | 4.0 | 964 | 0.0044 | 0.9992 |
| 0.0361 | 5.0 | 1205 | 0.0040 | 0.9991 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
[
"chart",
"math",
"scene",
"text"
] |
ShreyasM/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ShreyasM/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3738
- Validation Loss: 0.3564
- Train Accuracy: 0.906
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7898 | 1.6354 | 0.829 | 0 |
| 1.2367 | 0.8213 | 0.882 | 1 |
| 0.7048 | 0.5298 | 0.902 | 2 |
| 0.4881 | 0.3896 | 0.919 | 3 |
| 0.3738 | 0.3564 | 0.906 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
NadaElmasry/vit-base-patch16-224-finetuned-imageclds
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-imageclds
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 0.9997
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0213 | 1.0 | 241 | 0.0067 | 0.9981 |
| 0.0251 | 2.0 | 482 | 0.0025 | 0.9994 |
| 0.0243 | 3.0 | 723 | 0.0146 | 0.9938 |
| 0.022 | 4.0 | 964 | 0.0010 | 0.9997 |
| 0.0206 | 5.0 | 1205 | 0.0019 | 0.9997 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
[
"chart",
"math",
"scene",
"text"
] |
Hederson/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hederson/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3719
- Validation Loss: 0.3497
- Train Accuracy: 0.913
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7895 | 1.6158 | 0.831 | 0 |
| 1.1952 | 0.8275 | 0.877 | 1 |
| 0.6961 | 0.5219 | 0.908 | 2 |
| 0.4885 | 0.4042 | 0.915 | 3 |
| 0.3719 | 0.3497 | 0.913 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
taohu88/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0688
- Accuracy: 0.9796
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2582 | 1.0 | 190 | 0.1358 | 0.9578 |
| 0.1701 | 2.0 | 380 | 0.0875 | 0.9733 |
| 0.1131 | 3.0 | 570 | 0.0688 | 0.9796 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
DunnBC22/vit-base-patch16-224-in21k-Intel_Images
|
# vit-base-patch16-224-in21k-Intel_Images
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
It achieves the following results on the evaluation set:
- Loss: 0.1822
- Accuracy: 0.9487
- F1
- Weighted: 0.9485
- Micro: 0.9487
- Macro: 0.9497
- Recall
- Weighted: 0.9487
- Micro: 0.9487
- Macro: 0.9500
- Precision
- Weighted: 0.9485
- Micro: 0.9487
- Macro: 0.9496
## Model description
This is a multiclass image classification model of different scenery types.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Intel%20Image%20Classification/Intel_ViT.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/puneet6060/intel-image-classification
_Sample Images From Dataset:_
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.2305 | 1.0 | 878 | 0.2362 | 0.9153 | 0.9144 | 0.9153 | 0.9152 | 0.9153 | 0.9153 | 0.9148 | 0.9208 | 0.9153 | 0.9231 |
| 0.1136 | 2.0 | 1756 | 0.1785 | 0.9393 | 0.9391 | 0.9393 | 0.9405 | 0.9393 | 0.9393 | 0.9405 | 0.9391 | 0.9393 | 0.9407 |
| 0.0435 | 3.0 | 2634 | 0.1822 | 0.9487 | 0.9485 | 0.9487 | 0.9497 | 0.9487 | 0.9487 | 0.9500 | 0.9485 | 0.9487 | 0.9496 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"buildings",
"forest",
"glacier",
"mountain",
"sea",
"street"
] |
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