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Shwetansh08/simple_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. -->
# simple_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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5725
- Accuracy: 0.884
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6523 | 1.0 | 63 | 2.4919 | 0.823 |
| 1.7953 | 2.0 | 126 | 1.7663 | 0.868 |
| 1.5377 | 3.0 | 189 | 1.5725 | 0.884 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"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"
] |
AbhirajAmbasta/simple_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. -->
# simple_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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6227
- Accuracy: 0.885
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7235 | 1.0 | 63 | 2.5566 | 0.818 |
| 1.8473 | 2.0 | 126 | 1.8165 | 0.874 |
| 1.5887 | 3.0 | 189 | 1.6227 | 0.885 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"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"
] |
ragnsv/simple_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. -->
# simple_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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6109
- Accuracy: 0.892
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.708 | 1.0 | 63 | 2.5154 | 0.827 |
| 1.8206 | 2.0 | 126 | 1.7751 | 0.876 |
| 1.5443 | 3.0 | 189 | 1.6109 | 0.892 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"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"
] |
n1kooo/vit-cifar10
|
<!-- 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-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the n1kooo/vit-cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1006
- Accuracy: 0.968
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0861 | 1.0 | 2500 | 0.0955 | 0.9708 |
| 0.0927 | 2.0 | 5000 | 0.0890 | 0.974 |
| 0.0793 | 3.0 | 7500 | 0.0881 | 0.974 |
| 0.0656 | 4.0 | 10000 | 0.0864 | 0.9738 |
| 0.0787 | 5.0 | 12500 | 0.0867 | 0.9746 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"0",
"6",
"2",
"7",
"1",
"4",
"5",
"3",
"8",
"9"
] |
kleemyan/sportballs
|
<!-- 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. -->
# sportballs
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the kleemyan/sportballs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3433
- Accuracy: 0.9
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 15 | 0.7388 | 0.8333 |
| No log | 2.0 | 30 | 0.5470 | 0.9 |
| No log | 3.0 | 45 | 0.4627 | 0.8667 |
| No log | 4.0 | 60 | 0.4269 | 0.8667 |
| No log | 5.0 | 75 | 0.4168 | 0.8667 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"0",
"1",
"5"
] |
BeckerAnas/stellar-lion-210
|
<!-- 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. -->
# stellar-lion-210
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3994
- Accuracy: 0.3125
- Precision: 0.3585
- Recall: 0.3125
- F1: 0.3241
- Roc Auc: 0.5367
## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.415 | 1.0 | 17 | 1.4122 | 0.2812 | 0.3471 | 0.2812 | 0.2637 | 0.5093 |
| 1.3936 | 2.0 | 34 | 1.4003 | 0.3073 | 0.3525 | 0.3073 | 0.3176 | 0.5317 |
| 1.385 | 3.0 | 51 | 1.3994 | 0.3125 | 0.3585 | 0.3125 | 0.3241 | 0.5367 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
momogueye7/oral_cancer_detection
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"oral_normal",
"oral_scc"
] |
izeah01/shoe-classification-resnet50
|
<!-- 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. -->
# results
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0865
- Accuracy: 0.4576
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 1.1023 | 0.2632 |
| 1.103 | 2.0 | 16 | 1.0984 | 0.2807 |
| 1.099 | 3.0 | 24 | 1.0956 | 0.2807 |
| 1.0996 | 4.0 | 32 | 1.0915 | 0.3684 |
| 1.0974 | 5.0 | 40 | 1.0904 | 0.4035 |
| 1.0974 | 6.0 | 48 | 1.0904 | 0.3684 |
| 1.0938 | 7.0 | 56 | 1.0894 | 0.3509 |
| 1.0891 | 8.0 | 64 | 1.0889 | 0.3509 |
| 1.0897 | 9.0 | 72 | 1.0877 | 0.4035 |
| 1.0911 | 10.0 | 80 | 1.0873 | 0.3684 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"converse",
"adidas",
"nike"
] |
thini77/vit-food
|
<!-- 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-food
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the thini77/data-food-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2856
- Accuracy: 0.9143
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 53 | 0.5583 | 0.9238 |
| 0.8727 | 2.0 | 106 | 0.3149 | 0.9238 |
| 0.8727 | 3.0 | 159 | 0.2508 | 0.9238 |
| 0.306 | 4.0 | 212 | 0.2295 | 0.9238 |
| 0.306 | 5.0 | 265 | 0.2246 | 0.9238 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6"
] |
BeckerAnas/deft-glitter-211
|
<!-- 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. -->
# deft-glitter-211
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3537
- Accuracy: 0.3164
- Precision: 0.4989
- Recall: 0.3164
- F1: 0.3690
- Roc Auc: 0.6235
## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.4081 | 1.0 | 17 | 1.3928 | 0.2435 | 0.4923 | 0.2435 | 0.3213 | 0.5510 |
| 1.3773 | 2.0 | 34 | 1.3727 | 0.2682 | 0.4966 | 0.2682 | 0.3358 | 0.5862 |
| 1.3568 | 3.0 | 51 | 1.3597 | 0.3008 | 0.4996 | 0.3008 | 0.3587 | 0.6121 |
| 1.3458 | 4.0 | 68 | 1.3544 | 0.3151 | 0.4995 | 0.3151 | 0.3677 | 0.6220 |
| 1.3409 | 5.0 | 85 | 1.3537 | 0.3164 | 0.4989 | 0.3164 | 0.3690 | 0.6235 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
BeckerAnas/grateful-shape-212
|
<!-- 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. -->
# grateful-shape-212
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3777
- Accuracy: 0.2708
- Precision: 0.4203
- Recall: 0.2708
- F1: 0.3127
- Roc Auc: 0.5428
## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.4367 | 1.0 | 17 | 1.3810 | 0.0898 | 0.4478 | 0.0898 | 0.1092 | 0.5060 |
| 1.3894 | 2.0 | 34 | 1.3751 | 0.1823 | 0.4052 | 0.1823 | 0.2266 | 0.5250 |
| 1.3619 | 3.0 | 51 | 1.3766 | 0.2422 | 0.4100 | 0.2422 | 0.2876 | 0.5369 |
| 1.3523 | 4.0 | 68 | 1.3776 | 0.2708 | 0.4238 | 0.2708 | 0.3132 | 0.5422 |
| 1.3475 | 5.0 | 85 | 1.3777 | 0.2708 | 0.4203 | 0.2708 | 0.3127 | 0.5428 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
BeckerAnas/flowing-yogurt-213
|
<!-- 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. -->
# flowing-yogurt-213
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3359
- Accuracy: 0.3841
- Precision: 0.5072
- Recall: 0.3841
- F1: 0.3969
- Roc Auc: 0.6977
## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.4164 | 1.0 | 17 | 1.4507 | 0.0065 | 0.1680 | 0.0065 | 0.0026 | 0.5489 |
| 1.4036 | 2.0 | 34 | 1.4003 | 0.1120 | 0.4643 | 0.1120 | 0.1725 | 0.5986 |
| 1.3866 | 3.0 | 51 | 1.3685 | 0.3516 | 0.4511 | 0.3516 | 0.3868 | 0.6448 |
| 1.3819 | 4.0 | 68 | 1.3547 | 0.4089 | 0.4425 | 0.4089 | 0.4095 | 0.6679 |
| 1.3686 | 5.0 | 85 | 1.3514 | 0.4206 | 0.4565 | 0.4206 | 0.4196 | 0.6797 |
| 1.3566 | 6.0 | 102 | 1.3485 | 0.4115 | 0.4791 | 0.4115 | 0.4149 | 0.6856 |
| 1.3505 | 7.0 | 119 | 1.3492 | 0.3763 | 0.4884 | 0.3763 | 0.4002 | 0.6901 |
| 1.3416 | 8.0 | 136 | 1.3432 | 0.3984 | 0.4683 | 0.3984 | 0.4025 | 0.6934 |
| 1.3411 | 9.0 | 153 | 1.3422 | 0.3789 | 0.4768 | 0.3789 | 0.3883 | 0.6953 |
| 1.3432 | 10.0 | 170 | 1.3400 | 0.375 | 0.5029 | 0.375 | 0.3917 | 0.6958 |
| 1.3286 | 11.0 | 187 | 1.3365 | 0.3854 | 0.4977 | 0.3854 | 0.3983 | 0.6970 |
| 1.33 | 12.0 | 204 | 1.3354 | 0.3893 | 0.5007 | 0.3893 | 0.4007 | 0.6976 |
| 1.3297 | 13.0 | 221 | 1.3360 | 0.3841 | 0.5072 | 0.3841 | 0.3969 | 0.6976 |
| 1.329 | 14.0 | 238 | 1.3359 | 0.3841 | 0.5072 | 0.3841 | 0.3969 | 0.6977 |
| 1.3398 | 15.0 | 255 | 1.3359 | 0.3841 | 0.5072 | 0.3841 | 0.3969 | 0.6977 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
loretyan/vit-base-oxford_flowers102
|
<!-- 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-oxford_flowers102
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the dpdl-benchmark/oxford_flowers102 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1771
- Accuracy: 0.9706
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 51 | 3.4099 | 0.5490 |
| 3.341 | 2.0 | 102 | 2.3499 | 0.8431 |
| 3.341 | 3.0 | 153 | 1.6991 | 0.9020 |
| 1.3666 | 4.0 | 204 | 1.3820 | 0.9412 |
| 1.3666 | 5.0 | 255 | 1.2861 | 0.9510 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.1
### Results
Accuracy: 0.0137
Precision: 0.0017
Recall: 0.0137
|
[
"72",
"84",
"70",
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"23",
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"19",
"97"
] |
ismdal/vit-base-food-classification
|
<!-- 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-food-classification
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Kaludi/data-food-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5240
- Accuracy: 0.8667
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 53 | 0.8150 | 0.7238 |
| 0.5255 | 2.0 | 106 | 0.6319 | 0.7905 |
| 0.5255 | 3.0 | 159 | 0.7212 | 0.8381 |
| 0.0507 | 4.0 | 212 | 0.5164 | 0.8762 |
| 0.0507 | 5.0 | 265 | 0.5240 | 0.8667 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6"
] |
peter-yuma/natix-test-model-2
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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|
[
"none",
"roadwork"
] |
zet-quangming/workout
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Hardware Type:** [More Information Needed]
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|
[
"none",
"roadwork"
] |
den1125/sn72_first
|
# Model Card for Model ID
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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[
"none",
"roadwork"
] |
HyPiao009v2/streetvision
|
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[
"none",
"roadwork"
] |
CR011v2/streetvision
|
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[
"none",
"roadwork"
] |
BeckerAnas/polished-resonance-217
|
<!-- 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. -->
# polished-resonance-217
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5649
- Accuracy: 0.6562
- Precision: 0.6901
- Recall: 0.6562
- F1: 0.6610
- Roc Auc: 0.8810
## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.4116 | 1.0 | 17 | 1.3279 | 0.4857 | 0.4335 | 0.4857 | 0.4311 | 0.6864 |
| 1.3746 | 2.0 | 34 | 1.1750 | 0.4870 | 0.5240 | 0.4870 | 0.4995 | 0.7706 |
| 1.1647 | 3.0 | 51 | 0.9635 | 0.5221 | 0.5858 | 0.5221 | 0.5105 | 0.7994 |
| 1.0134 | 4.0 | 68 | 0.8929 | 0.5234 | 0.5823 | 0.5234 | 0.5402 | 0.7975 |
| 0.9625 | 5.0 | 85 | 0.8761 | 0.5729 | 0.5577 | 0.5729 | 0.5576 | 0.8274 |
| 0.9373 | 6.0 | 102 | 0.9938 | 0.5768 | 0.5625 | 0.5768 | 0.5512 | 0.8306 |
| 0.8421 | 7.0 | 119 | 0.7692 | 0.4492 | 0.6542 | 0.4492 | 0.4196 | 0.8073 |
| 0.7684 | 8.0 | 136 | 0.6549 | 0.5977 | 0.6615 | 0.5977 | 0.6121 | 0.8453 |
| 0.7312 | 9.0 | 153 | 0.6826 | 0.5794 | 0.6666 | 0.5794 | 0.5661 | 0.8618 |
| 0.6827 | 10.0 | 170 | 0.6071 | 0.6068 | 0.6560 | 0.6068 | 0.6057 | 0.8665 |
| 0.6731 | 11.0 | 187 | 0.5996 | 0.6146 | 0.6701 | 0.6146 | 0.6224 | 0.8691 |
| 0.6675 | 12.0 | 204 | 0.6269 | 0.5703 | 0.7065 | 0.5703 | 0.5792 | 0.8664 |
| 0.658 | 13.0 | 221 | 0.5710 | 0.6536 | 0.6925 | 0.6536 | 0.6603 | 0.8783 |
| 0.6081 | 14.0 | 238 | 0.5664 | 0.6562 | 0.6910 | 0.6562 | 0.6603 | 0.8807 |
| 0.6179 | 15.0 | 255 | 0.5649 | 0.6562 | 0.6901 | 0.6562 | 0.6610 | 0.8810 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
ninja990621/natix-01
|
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[
"none",
"roadwork"
] |
ninja990621/natix-02
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[
"none",
"roadwork"
] |
ninja990621/natix-03
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|
[
"none",
"roadwork"
] |
ninja990621/natix-04
|
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
[
"none",
"roadwork"
] |
ninja990621/natix-05
|
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
[
"none",
"roadwork"
] |
ninja990621/natix-06
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
[
"none",
"roadwork"
] |
izeah01/shoe_classifier
|
<!-- 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. -->
# shoe_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2791
- Accuracy: 0.9277
- F1: 0.9287
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2347 | 1.0 | 144 | 0.3991 | 0.8675 | 0.9290 |
| 0.0782 | 2.0 | 288 | 0.6030 | 0.8675 | 0.9290 |
| 0.0008 | 3.0 | 432 | 0.9098 | 0.8133 | 0.8970 |
| 0.0002 | 4.0 | 576 | 0.7551 | 0.8554 | 0.9221 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"converse",
"adidas",
"nike"
] |
MoiGonzaga/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3513
- Accuracy: 0.823
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4337 | 0.992 | 31 | 3.1965 | 0.803 |
| 2.6237 | 1.984 | 62 | 2.5410 | 0.822 |
| 2.339 | 2.976 | 93 | 2.3513 | 0.823 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"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"
] |
rugj84/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3269
- Accuracy: 0.847
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.458 | 0.992 | 31 | 3.2205 | 0.786 |
| 2.667 | 1.984 | 62 | 2.5260 | 0.829 |
| 2.3685 | 2.976 | 93 | 2.3269 | 0.847 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"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"
] |
xelamacias/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3241
- Accuracy: 0.873
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.472 | 0.992 | 31 | 3.2078 | 0.811 |
| 2.6652 | 1.984 | 62 | 2.5057 | 0.88 |
| 2.3543 | 2.976 | 93 | 2.3241 | 0.873 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"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"
] |
chriscadev/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4253
- Accuracy: 0.823
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5169 | 0.992 | 31 | 3.2970 | 0.771 |
| 2.7017 | 1.984 | 62 | 2.6157 | 0.823 |
| 2.3875 | 2.976 | 93 | 2.4253 | 0.823 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"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"
] |
zismad/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6255
- Accuracy: 0.889
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6986 | 1.0 | 63 | 2.5008 | 0.832 |
| 1.8251 | 2.0 | 126 | 1.7726 | 0.864 |
| 1.5934 | 3.0 | 189 | 1.6255 | 0.889 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"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"
] |
mudan-hua/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3422
- Accuracy: 0.838
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4688 | 0.992 | 31 | 3.2182 | 0.769 |
| 2.6434 | 1.984 | 62 | 2.5502 | 0.821 |
| 2.3202 | 2.976 | 93 | 2.3422 | 0.838 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"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"
] |
BeckerAnas/wise-cosmos-218
|
<!-- 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. -->
# wise-cosmos-218
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5895
- Accuracy: 0.6328
- Precision: 0.6756
- Recall: 0.6328
- F1: 0.6413
- Roc Auc: 0.8748
## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.3576 | 1.0 | 17 | 1.2560 | 0.2383 | 0.4653 | 0.2383 | 0.3033 | 0.7652 |
| 1.0327 | 2.0 | 34 | 0.8853 | 0.5326 | 0.5906 | 0.5326 | 0.5534 | 0.8194 |
| 0.764 | 3.0 | 51 | 0.6813 | 0.6159 | 0.6415 | 0.6159 | 0.6239 | 0.8545 |
| 0.6357 | 4.0 | 68 | 0.6066 | 0.6367 | 0.6764 | 0.6367 | 0.6464 | 0.8702 |
| 0.5797 | 5.0 | 85 | 0.5895 | 0.6328 | 0.6756 | 0.6328 | 0.6413 | 0.8748 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
ibuki95/model_172_1
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"none",
"roadwork"
] |
ibuki95/model_172_4
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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|
[
"none",
"roadwork"
] |
artpods56/resnet50_churches
|
# Model Card for Model ID
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|
[
"q176483",
"q840829",
"q46261",
"q236122",
"q245188"
] |
ibuki95/model_172_5
|
# Model Card for Model ID
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[More Information Needed]
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|
[
"none",
"roadwork"
] |
BeckerAnas/fancy-plant-219
|
<!-- 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. -->
# fancy-plant-219
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0531
- Accuracy: 0.9805
- Precision: 0.9805
- Recall: 0.9805
- F1: 0.9805
- Roc Auc: 0.9974
## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.3945 | 1.0 | 17 | 1.4430 | 0.4779 | 0.4233 | 0.4779 | 0.4381 | 0.6365 |
| 1.3203 | 2.0 | 34 | 1.2260 | 0.4049 | 0.5854 | 0.4049 | 0.4639 | 0.7864 |
| 1.1476 | 3.0 | 51 | 0.9415 | 0.5677 | 0.5641 | 0.5677 | 0.5634 | 0.8224 |
| 0.8622 | 4.0 | 68 | 0.6177 | 0.6133 | 0.6349 | 0.6133 | 0.6032 | 0.8612 |
| 0.7741 | 5.0 | 85 | 0.6050 | 0.5964 | 0.5937 | 0.5964 | 0.5373 | 0.8716 |
| 0.6872 | 6.0 | 102 | 0.4559 | 0.6536 | 0.7085 | 0.6536 | 0.6351 | 0.9020 |
| 0.5383 | 7.0 | 119 | 0.3990 | 0.7227 | 0.7441 | 0.7227 | 0.7034 | 0.9283 |
| 0.4257 | 8.0 | 136 | 0.2967 | 0.7643 | 0.8000 | 0.7643 | 0.7658 | 0.9521 |
| 0.3358 | 9.0 | 153 | 0.2559 | 0.8086 | 0.8325 | 0.8086 | 0.8089 | 0.9608 |
| 0.2355 | 10.0 | 170 | 0.1693 | 0.8958 | 0.8956 | 0.8958 | 0.8950 | 0.9789 |
| 0.1634 | 11.0 | 187 | 0.1659 | 0.8945 | 0.8979 | 0.8945 | 0.8950 | 0.9826 |
| 0.112 | 12.0 | 204 | 0.2273 | 0.8320 | 0.8777 | 0.8320 | 0.8334 | 0.9880 |
| 0.1065 | 13.0 | 221 | 0.1733 | 0.8984 | 0.9139 | 0.8984 | 0.8966 | 0.9901 |
| 0.1051 | 14.0 | 238 | 0.1226 | 0.9258 | 0.9298 | 0.9258 | 0.9245 | 0.9934 |
| 0.056 | 15.0 | 255 | 0.0664 | 0.9479 | 0.9499 | 0.9479 | 0.9481 | 0.9959 |
| 0.0501 | 16.0 | 272 | 0.0804 | 0.9531 | 0.9532 | 0.9531 | 0.9532 | 0.9960 |
| 0.0306 | 17.0 | 289 | 0.0784 | 0.9557 | 0.9563 | 0.9557 | 0.9556 | 0.9960 |
| 0.0315 | 18.0 | 306 | 0.0870 | 0.9648 | 0.9652 | 0.9648 | 0.9647 | 0.9968 |
| 0.0263 | 19.0 | 323 | 0.0637 | 0.9622 | 0.9624 | 0.9622 | 0.9623 | 0.9972 |
| 0.0259 | 20.0 | 340 | 0.0949 | 0.9479 | 0.9505 | 0.9479 | 0.9479 | 0.9945 |
| 0.0171 | 21.0 | 357 | 0.0903 | 0.9596 | 0.9601 | 0.9596 | 0.9594 | 0.9952 |
| 0.0236 | 22.0 | 374 | 0.0777 | 0.9714 | 0.9717 | 0.9714 | 0.9713 | 0.9962 |
| 0.0295 | 23.0 | 391 | 0.0733 | 0.9648 | 0.9664 | 0.9648 | 0.9650 | 0.9977 |
| 0.0143 | 24.0 | 408 | 0.0790 | 0.9701 | 0.9705 | 0.9701 | 0.9701 | 0.9967 |
| 0.0116 | 25.0 | 425 | 0.0849 | 0.9635 | 0.9637 | 0.9635 | 0.9634 | 0.9974 |
| 0.0079 | 26.0 | 442 | 0.0496 | 0.9792 | 0.9794 | 0.9792 | 0.9792 | 0.9978 |
| 0.0111 | 27.0 | 459 | 0.0382 | 0.9805 | 0.9806 | 0.9805 | 0.9805 | 0.9977 |
| 0.0097 | 28.0 | 476 | 0.0456 | 0.9792 | 0.9792 | 0.9792 | 0.9792 | 0.9971 |
| 0.004 | 29.0 | 493 | 0.0628 | 0.9766 | 0.9769 | 0.9766 | 0.9765 | 0.9970 |
| 0.0089 | 30.0 | 510 | 0.0584 | 0.9766 | 0.9768 | 0.9766 | 0.9765 | 0.9966 |
| 0.0035 | 31.0 | 527 | 0.0527 | 0.9792 | 0.9792 | 0.9792 | 0.9792 | 0.9982 |
| 0.007 | 32.0 | 544 | 0.0531 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9974 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
kalemlhub/sn72-roadwork
|
# Model Card for Model ID
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
[
"none",
"roadwork"
] |
halimalm/vogue
|
<!-- 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. -->
# vogue
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the vogue-designer-looks dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7083
- Accuracy: 0.8655
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 86 | 1.2303 | 0.6 |
| 1.491 | 2.0 | 172 | 0.9322 | 0.7706 |
| 0.9886 | 3.0 | 258 | 0.7960 | 0.8235 |
| 0.7985 | 4.0 | 344 | 0.7546 | 0.8471 |
| 0.6993 | 5.0 | 430 | 0.7032 | 0.8765 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0
- Datasets 3.4.1
- Tokenizers 0.21.1
|
[
"chanel",
"dior",
"gucci",
"prada",
"saint-laurent",
"versace"
] |
Dalmatiner/cv_animals
|
<!-- 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. -->
# cv_animals
## Model description
Dieses Modell ist ein feingetuntes Vision Transformer (ViT) Modell, das auf dem vortrainierten [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) basiert. Es wurde speziell für die Klassifikation von Bildern in 90 verschiedene Tierklassen trainiert. Die zugrunde liegenden Daten stammen aus dem öffentlich verfügbaren Animal Image Dataset, das Bilder aus Google aggregiert.
Das Modell eignet sich für Anwendungen, bei denen Tiere auf Fotos automatisch erkannt und klassifiziert werden sollen. Typische Einsatzgebiete sind beispielsweise:
- automatisierte Artenbestimmung
- bildgestützte Tierdatenerfassung in Forschung und Lehre
- edukative oder interaktive Anwendungen (z. B. in Museen oder Apps).
Erzielte Resultate:
- Loss: 0.0876
- Accuracy: 0.9833
## Intended uses & limitations
### Vorgesehene Verwendungen
Dieses Modell wurde für die Bildklassifikation von Tieren in 90 Kategorien entwickelt. Es eignet sich insbesondere für:
- Bildbasierte Tiererkennung in Anwendungen wie Lernplattformen, mobilen Apps, oder interaktiven Tools
- Unterstützung bei biologischer Arterkennung (z. B. für Bildungsprojekte oder Citizen Science)
- Vergleich von Transfer-Learning-Modellen mit Zero-Shot-Modellen (z. B. CLIP)
### Einschränkungen
- Die Vorhersagen sind stark abhängig von Bildqualität und Perspektive
- Das Modell wurde nur mit Bildern aus dem Kaggle-Datensatz trainiert – es ist nicht garantiert, dass es bei anderen Tierarten oder in freier Wildbahn korrekt klassifiziert
- Mehrdeutige oder unklare Bilder (z. B. mit mehreren Tieren, Zeichnungen, Verdeckungen) können zu falschen Vorhersagen führen
## Training and evaluation data
Datensatz: Animal Image Dataset - 90 different animals
Anzahl Bilder: 5.400
Klassen: 90 Tierarten (z. B. Hund, Katze, Elefant, Biene, Löwe etc.)
Aufteilung:
- 80 % Training (4.320 Bilder)
- 10 % Validierung (540 Bilder)
- 10 % Test (540 Bilder)
### Augmentierungen
Zur Verbesserung der Generalisierbarkeit wurden folgende Transformationen auf die Trainingsbilder angewendet:
- RandomHorizontalFlip() – zufälliges horizontales Spiegeln
- RandomRotation(25) – zufällige Drehung um ±25°
- ColorJitter() – zufällige Helligkeits-, Kontrast-, Sättigungs- und Farbtonänderungen
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1951 | 1.0 | 270 | 0.3316 | 0.9648 |
| 0.2763 | 2.0 | 540 | 0.1710 | 0.9667 |
| 0.1772 | 3.0 | 810 | 0.1482 | 0.9648 |
| 0.1533 | 4.0 | 1080 | 0.1391 | 0.9704 |
| 0.1462 | 5.0 | 1350 | 0.1350 | 0.9685 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
[
"antelope",
"badger",
"bat",
"bear",
"bee",
"beetle",
"bison",
"boar",
"butterfly",
"cat",
"caterpillar",
"chimpanzee",
"cockroach",
"cow",
"coyote",
"crab",
"crow",
"deer",
"dog",
"dolphin",
"donkey",
"dragonfly",
"duck",
"eagle",
"elephant",
"flamingo",
"fly",
"fox",
"goat",
"goldfish",
"goose",
"gorilla",
"grasshopper",
"hamster",
"hare",
"hedgehog",
"hippopotamus",
"hornbill",
"horse",
"hummingbird",
"hyena",
"jellyfish",
"kangaroo",
"koala",
"ladybugs",
"leopard",
"lion",
"lizard",
"lobster",
"mosquito",
"moth",
"mouse",
"octopus",
"okapi",
"orangutan",
"otter",
"owl",
"ox",
"oyster",
"panda",
"parrot",
"pelecaniformes",
"penguin",
"pig",
"pigeon",
"porcupine",
"possum",
"raccoon",
"rat",
"reindeer",
"rhinoceros",
"sandpiper",
"seahorse",
"seal",
"shark",
"sheep",
"snake",
"sparrow",
"squid",
"squirrel",
"starfish",
"swan",
"tiger",
"turkey",
"turtle",
"whale",
"wolf",
"wombat",
"woodpecker",
"zebra"
] |
paulinusjua/cameroon-meals
|
# 🇨🇲 Cameroon Meals Classifier
...
A PyTorch image classification model trained to identify 37 traditional Cameroonian meals using a ResNet34 backbone.
---
## 🍽️ Classes (37 meals)
- Pepper Soup
- Kati Kati and Njama Njama
- Koki Beans
- Mbongo Tchobi
- Dodo
- Soya
- Chin-Chin
- Groundnut Soup
- Sese Plantains
- Okra Soup
- Puff Puff
- Beignet Haricot
- Egusi Soup
- Yassa Chicken
- Meat Pie
- Kwacoco Bible
- Kondre
- Roasted Plantain and Plum
- Banga Soup
- Ekwang
- Bobolo
- Black Soup
- Cornchaff
- Accra Banana
- Egusi Pudding
- Poulet DG
- Sangah
- Banane Malaxée
- Hot Pot Potatoes
- Groundnut Sweet
- Fish Roll
- Garri with Groundnuts
- Eru
- Ndolé
- Achu
- Jollof Rice
---
## 🧠 Model Details
- Backbone: ResNet50
- Framework: PyTorch
- Format: `model_weights.pth`
- Custom class: `CameroonMealsModel` (see `modeling.py`)
---
## 🚀 Usage
```python
from transformers import AutoModel
import torch
model = AutoModel.from_pretrained("paulinusjua/cameroon-meals", trust_remote_code=True)
model.eval()
# Example forward (x: a batch of image tensors)
logits = model(x)
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"label_23",
"label_24",
"label_25",
"label_26",
"label_27",
"label_28",
"label_29",
"label_30",
"label_31",
"label_32",
"label_33",
"label_34",
"label_35"
] |
bombshelll/15
|
<!-- 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. -->
# 15
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: 0.5997
- Accuracy: 0.7812
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.154 | 0.9655 | 7 | 0.5254 | 0.7812 |
| 0.1262 | 1.9310 | 14 | 0.8508 | 0.7188 |
| 0.098 | 2.8966 | 21 | 0.6353 | 0.7969 |
| 0.0822 | 4.0 | 29 | 0.6771 | 0.7734 |
| 0.0834 | 4.9655 | 36 | 0.9025 | 0.7344 |
| 0.1243 | 5.9310 | 43 | 0.7911 | 0.7734 |
| 0.1599 | 6.8966 | 50 | 0.7152 | 0.7969 |
| 0.1698 | 8.0 | 58 | 0.7211 | 0.8047 |
| 0.241 | 8.9655 | 65 | 0.5903 | 0.7891 |
| 0.1972 | 9.9310 | 72 | 0.5848 | 0.7656 |
| 0.1741 | 10.8966 | 79 | 0.5149 | 0.7812 |
| 0.1673 | 12.0 | 87 | 0.4927 | 0.7891 |
| 0.1793 | 12.9655 | 94 | 0.5655 | 0.8125 |
| 0.1717 | 13.9310 | 101 | 0.5873 | 0.8047 |
| 0.1705 | 14.8966 | 108 | 0.5591 | 0.7734 |
| 0.1455 | 16.0 | 116 | 0.5839 | 0.8047 |
| 0.1373 | 16.9655 | 123 | 0.5683 | 0.7734 |
| 0.1467 | 17.9310 | 130 | 0.5917 | 0.7891 |
| 0.1477 | 18.8966 | 137 | 0.6008 | 0.7891 |
| 0.0904 | 19.3103 | 140 | 0.5997 | 0.7812 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"hgg",
"lgg"
] |
bombshelll/2D_hgg_lgg_classification
|
<!-- 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. -->
# 2D_hgg_lgg_classification
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: 0.7560
- Accuracy: 0.8203
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.7243 | 0.9655 | 7 | 0.6272 | 0.7656 |
| 0.5807 | 1.9310 | 14 | 0.5266 | 0.7812 |
| 0.556 | 2.8966 | 21 | 0.5086 | 0.7812 |
| 0.4675 | 4.0 | 29 | 0.4844 | 0.7812 |
| 0.4992 | 4.9655 | 36 | 0.4664 | 0.7812 |
| 0.4562 | 5.9310 | 43 | 0.4430 | 0.7344 |
| 0.4344 | 6.8966 | 50 | 0.4726 | 0.7109 |
| 0.3778 | 8.0 | 58 | 0.4302 | 0.7656 |
| 0.3922 | 8.9655 | 65 | 0.4350 | 0.8125 |
| 0.3864 | 9.9310 | 72 | 0.4259 | 0.7656 |
| 0.3388 | 10.8966 | 79 | 0.4462 | 0.7656 |
| 0.3071 | 12.0 | 87 | 0.5272 | 0.7969 |
| 0.3233 | 12.9655 | 94 | 0.4723 | 0.7188 |
| 0.3103 | 13.9310 | 101 | 0.4494 | 0.7656 |
| 0.2818 | 14.8966 | 108 | 0.4279 | 0.8047 |
| 0.2341 | 16.0 | 116 | 0.4069 | 0.7891 |
| 0.2103 | 16.9655 | 123 | 0.4237 | 0.7969 |
| 0.219 | 17.9310 | 130 | 0.4467 | 0.8047 |
| 0.21 | 18.8966 | 137 | 0.4380 | 0.7812 |
| 0.1994 | 20.0 | 145 | 0.4629 | 0.7969 |
| 0.1865 | 20.9655 | 152 | 0.5012 | 0.7891 |
| 0.1872 | 21.9310 | 159 | 0.5055 | 0.8203 |
| 0.2144 | 22.8966 | 166 | 0.6089 | 0.8125 |
| 0.1737 | 24.0 | 174 | 0.4914 | 0.7969 |
| 0.1633 | 24.9655 | 181 | 0.5137 | 0.7812 |
| 0.1624 | 25.9310 | 188 | 0.5985 | 0.7812 |
| 0.1525 | 26.8966 | 195 | 0.5090 | 0.8047 |
| 0.136 | 28.0 | 203 | 0.5170 | 0.8125 |
| 0.1451 | 28.9655 | 210 | 0.6165 | 0.8203 |
| 0.1405 | 29.9310 | 217 | 0.6124 | 0.7969 |
| 0.1384 | 30.8966 | 224 | 0.5578 | 0.8047 |
| 0.1246 | 32.0 | 232 | 0.5967 | 0.8125 |
| 0.1371 | 32.9655 | 239 | 0.6135 | 0.7812 |
| 0.1111 | 33.9310 | 246 | 0.6878 | 0.8047 |
| 0.1305 | 34.8966 | 253 | 0.7300 | 0.8125 |
| 0.1124 | 36.0 | 261 | 0.6687 | 0.8203 |
| 0.1214 | 36.9655 | 268 | 0.6692 | 0.8047 |
| 0.1065 | 37.9310 | 275 | 0.7058 | 0.8125 |
| 0.1183 | 38.8966 | 282 | 0.6884 | 0.7969 |
| 0.0928 | 40.0 | 290 | 0.7104 | 0.7969 |
| 0.1248 | 40.9655 | 297 | 0.6961 | 0.7969 |
| 0.0949 | 41.9310 | 304 | 0.7265 | 0.8203 |
| 0.1048 | 42.8966 | 311 | 0.7430 | 0.8281 |
| 0.0887 | 44.0 | 319 | 0.7627 | 0.8047 |
| 0.0866 | 44.9655 | 326 | 0.7483 | 0.8203 |
| 0.0978 | 45.9310 | 333 | 0.7515 | 0.8125 |
| 0.0901 | 46.8966 | 340 | 0.7518 | 0.8125 |
| 0.0785 | 48.0 | 348 | 0.7557 | 0.8203 |
| 0.0747 | 48.2759 | 350 | 0.7560 | 0.8203 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"hgg",
"lgg"
] |
Schram03/fruits-classification
|
<!-- 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. -->
# fruits-classification
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the augmented Schram03/fruits dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3039
- Accuracy: 0.9280
## Model description
This model classifies the 33 different types of fruit in the Schram03/fruits dataset.
## Intended uses & limitations
With further developement it could be possible to differentiat foul fruits or specific subtypes of fruit. This however would need an extensive Dataset.
Currently the model is able to differentiat the trained 33 types of fruit. However, there are limitations especially when using similar fruits like peaches and nectarines.
## Training and evaluation data
80 % Train, 20 % Evaluation
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5816 | 1.0 | 1070 | 0.5238 | 0.8887 |
| 0.3761 | 2.0 | 2140 | 0.3568 | 0.9182 |
| 0.2934 | 3.0 | 3210 | 0.2852 | 0.9322 |
| 0.2719 | 4.0 | 4280 | 0.2681 | 0.9388 |
| 0.253 | 5.0 | 5350 | 0.2705 | 0.9388 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"apple",
"apricot",
"avocado",
"banana",
"blackberrie",
"blueberry",
"cactus fruit",
"cherry",
"clementine",
"cocos",
"grape",
"grapefruit",
"guava",
"kaki",
"kiwi",
"lemon",
"limes",
"lychee",
"mandarine",
"mango",
"maracuja",
"nectarine",
"orange",
"papaya",
"passion fruit",
"peach",
"pear",
"pineapple",
"plum",
"pomegranate",
"quince",
"strawberry",
"watermelon"
] |
loretyan/vit-base-oxford-flowers-102
|
<!-- 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-oxford-flowers-102
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Multimodal-Fatima/OxfordFlowers_test_facebook_opt_1.3b_Visclues_ns_6149 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0540
- Accuracy: 0.9951
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3164 | 1.0 | 308 | 0.1577 | 0.9919 |
| 0.0703 | 2.0 | 616 | 0.0557 | 0.9951 |
| 0.0416 | 3.0 | 924 | 0.0417 | 0.9967 |
| 0.0297 | 4.0 | 1232 | 0.0369 | 0.9967 |
| 0.0286 | 5.0 | 1540 | 0.0358 | 0.9967 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
### Zero-shot classification results
Accuracy: 0.7214
Precision: 0.7092
Recall: 0.7214
|
[
"pink primrose",
"hard-leaved pocket orchid",
"canterbury bells",
"sweet pea",
"english marigold",
"tiger lily",
"moon orchid",
"bird of paradise",
"monkshood",
"globe thistle",
"snapdragon",
"colt's foot",
"king protea",
"spear thistle",
"yellow iris",
"globe-flower",
"fire lily",
"purple coneflower",
"peruvian lily",
"balloon flower",
"giant white arum lily",
"pincushion flower",
"fritillary",
"red ginger",
"grape hyacinth",
"corn poppy",
"prince of wales feathers",
"stemless gentian",
"artichoke",
"sweet william",
"carnation",
"garden phlox",
"love in the mist",
"mexican aster",
"alpine sea holly",
"ruby-lipped cattleya",
"cape flower",
"great masterwort",
"siam tulip",
"lenten rose",
"barbeton daisy",
"daffodil",
"sword lily",
"poinsettia",
"bolero deep blue",
"wallflower",
"marigold",
"buttercup",
"oxeye daisy",
"common dandelion",
"petunia",
"wild pansy",
"primula",
"sunflower",
"pelargonium",
"bishop of llandaff",
"gaura",
"geranium",
"orange dahlia",
"pink-yellow dahlia?",
"cautleya spicata",
"japanese anemone",
"black-eyed susan",
"silverbush",
"californian poppy",
"osteospermum",
"spring crocus",
"bearded iris",
"windflower",
"tree poppy",
"gazania",
"azalea",
"water lily",
"rose",
"thorn apple",
"morning glory",
"passion flower",
"lotus",
"toad lily",
"anthurium",
"frangipani",
"clematis",
"hibiscus",
"columbine",
"desert-rose",
"tree mallow",
"magnolia",
"cyclamen",
"watercress",
"canna lily",
"hippeastrum",
"bee balm",
"ball moss",
"foxglove",
"bougainvillea",
"camellia",
"mallow",
"mexican petunia",
"bromelia",
"blanket flower",
"trumpet creeper",
"blackberry lily"
] |
Jedrzej-Smok/2025-05-30_11-03-27
|
<!-- 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. -->
# 2025-05-30_11-03-27
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 generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3906
- Accuracy: 0.9583
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7169 | 1.0 | 1 | 0.7186 | 0.5 |
| 0.7222 | 2.0 | 2 | 0.6373 | 0.4583 |
| 0.6389 | 3.0 | 3 | 0.5806 | 0.7083 |
| 0.5736 | 4.0 | 4 | 0.5353 | 0.9583 |
| 0.5305 | 5.0 | 5 | 0.4890 | 1.0 |
| 0.49 | 6.0 | 6 | 0.4688 | 1.0 |
| 0.4505 | 7.0 | 7 | 0.4366 | 1.0 |
| 0.4273 | 8.0 | 8 | 0.4119 | 1.0 |
| 0.4075 | 9.0 | 9 | 0.4050 | 1.0 |
| 0.3909 | 10.0 | 10 | 0.3906 | 0.9583 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"healthy",
"sick"
] |
Jedrzej-Smok/2025-05-30_11-48-34
|
<!-- 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. -->
# 2025-05-30_11-48-34
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 generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3985
- 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7264 | 1.0 | 1 | 0.7131 | 0.5 |
| 0.7239 | 2.0 | 2 | 0.6431 | 0.5 |
| 0.6495 | 3.0 | 3 | 0.5960 | 0.5833 |
| 0.5979 | 4.0 | 4 | 0.5529 | 0.8333 |
| 0.5506 | 5.0 | 5 | 0.5156 | 1.0 |
| 0.5064 | 6.0 | 6 | 0.4762 | 1.0 |
| 0.4715 | 7.0 | 7 | 0.4439 | 1.0 |
| 0.4309 | 8.0 | 8 | 0.4305 | 1.0 |
| 0.4172 | 9.0 | 9 | 0.4192 | 1.0 |
| 0.3853 | 10.0 | 10 | 0.3985 | 1.0 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"healthy",
"sick"
] |
Amoros/DinoAmoros_is_103_bs_32_ep_150-large-2025_05_30_52071-bs32_freeze_monolabel
|
<!-- 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. -->
# DinoAmoros_is_103_bs_32_ep_150-large-2025_05_30_52071-bs32_freeze_monolabel
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9936
- F1 Micro: 0.6615
- F1 Macro: 0.5762
- Accuracy: 0.6615
- Learning Rate: 0.0000
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Rate |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:|:--------:|:------:|
| 1.7837 | 1.0 | 977 | 1.3164 | 0.5792 | 0.4109 | 0.5792 | 0.001 |
| 1.4869 | 2.0 | 1954 | 1.2393 | 0.5998 | 0.4409 | 0.5998 | 0.001 |
| 1.411 | 3.0 | 2931 | 1.2197 | 0.6012 | 0.4388 | 0.6012 | 0.001 |
| 1.3905 | 4.0 | 3908 | 1.2161 | 0.6063 | 0.4860 | 0.6063 | 0.001 |
| 1.3669 | 5.0 | 4885 | 1.1722 | 0.6228 | 0.4934 | 0.6228 | 0.001 |
| 1.3543 | 6.0 | 5862 | 1.1832 | 0.6160 | 0.4913 | 0.6160 | 0.001 |
| 1.3467 | 7.0 | 6839 | 1.1737 | 0.6217 | 0.5017 | 0.6217 | 0.001 |
| 1.3254 | 8.0 | 7816 | 1.1449 | 0.6246 | 0.4999 | 0.6246 | 0.001 |
| 1.3188 | 9.0 | 8793 | 1.1404 | 0.6291 | 0.4972 | 0.6291 | 0.001 |
| 1.3129 | 10.0 | 9770 | 1.1476 | 0.6307 | 0.5074 | 0.6307 | 0.001 |
| 1.298 | 11.0 | 10747 | 1.1268 | 0.6347 | 0.4888 | 0.6347 | 0.001 |
| 1.3001 | 12.0 | 11724 | 1.1318 | 0.6285 | 0.5200 | 0.6285 | 0.001 |
| 1.2705 | 13.0 | 12701 | 1.1183 | 0.6372 | 0.5359 | 0.6372 | 0.001 |
| 1.2699 | 14.0 | 13678 | 1.1077 | 0.6302 | 0.5163 | 0.6302 | 0.001 |
| 1.2747 | 15.0 | 14655 | 1.1005 | 0.6417 | 0.5250 | 0.6417 | 0.001 |
| 1.2826 | 16.0 | 15632 | 1.0989 | 0.6393 | 0.5227 | 0.6393 | 0.001 |
| 1.2753 | 17.0 | 16609 | 1.1159 | 0.6340 | 0.5331 | 0.6340 | 0.001 |
| 1.2792 | 18.0 | 17586 | 1.0937 | 0.6369 | 0.5375 | 0.6369 | 0.001 |
| 1.2745 | 19.0 | 18563 | 1.0863 | 0.6441 | 0.5441 | 0.6441 | 0.001 |
| 1.2681 | 20.0 | 19540 | 1.0973 | 0.6378 | 0.5382 | 0.6378 | 0.001 |
| 1.2765 | 21.0 | 20517 | 1.1111 | 0.6344 | 0.5367 | 0.6344 | 0.001 |
| 1.2704 | 22.0 | 21494 | 1.0776 | 0.6439 | 0.5398 | 0.6439 | 0.001 |
| 1.2516 | 23.0 | 22471 | 1.0827 | 0.6429 | 0.5510 | 0.6429 | 0.001 |
| 1.2487 | 24.0 | 23448 | 1.0872 | 0.6449 | 0.5306 | 0.6449 | 0.001 |
| 1.257 | 25.0 | 24425 | 1.0920 | 0.6388 | 0.5157 | 0.6388 | 0.001 |
| 1.2474 | 26.0 | 25402 | 1.0984 | 0.6369 | 0.5073 | 0.6369 | 0.001 |
| 1.2522 | 27.0 | 26379 | 1.0984 | 0.6404 | 0.5160 | 0.6404 | 0.001 |
| 1.251 | 28.0 | 27356 | 1.0838 | 0.6435 | 0.5323 | 0.6435 | 0.001 |
| 1.1971 | 29.0 | 28333 | 1.0550 | 0.6512 | 0.5436 | 0.6512 | 0.0001 |
| 1.1875 | 30.0 | 29310 | 1.0398 | 0.6557 | 0.5506 | 0.6557 | 0.0001 |
| 1.1647 | 31.0 | 30287 | 1.0447 | 0.6567 | 0.5570 | 0.6567 | 0.0001 |
| 1.1458 | 32.0 | 31264 | 1.0314 | 0.6556 | 0.5592 | 0.6556 | 0.0001 |
| 1.1549 | 33.0 | 32241 | 1.0268 | 0.6589 | 0.5575 | 0.6589 | 0.0001 |
| 1.1499 | 34.0 | 33218 | 1.0237 | 0.6594 | 0.5593 | 0.6594 | 0.0001 |
| 1.1402 | 35.0 | 34195 | 1.0221 | 0.6587 | 0.5588 | 0.6587 | 0.0001 |
| 1.1519 | 36.0 | 35172 | 1.0216 | 0.6591 | 0.5641 | 0.6591 | 0.0001 |
| 1.1303 | 37.0 | 36149 | 1.0180 | 0.6619 | 0.5635 | 0.6619 | 0.0001 |
| 1.1317 | 38.0 | 37126 | 1.0174 | 0.6609 | 0.5601 | 0.6609 | 0.0001 |
| 1.1298 | 39.0 | 38103 | 1.0162 | 0.6631 | 0.5738 | 0.6631 | 0.0001 |
| 1.131 | 40.0 | 39080 | 1.0139 | 0.6610 | 0.5636 | 0.6610 | 0.0001 |
| 1.1206 | 41.0 | 40057 | 1.0147 | 0.6632 | 0.5665 | 0.6632 | 0.0001 |
| 1.1119 | 42.0 | 41034 | 1.0114 | 0.6621 | 0.5719 | 0.6621 | 0.0001 |
| 1.1051 | 43.0 | 42011 | 1.0120 | 0.6635 | 0.5741 | 0.6635 | 0.0001 |
| 1.1067 | 44.0 | 42988 | 1.0062 | 0.6632 | 0.5743 | 0.6632 | 0.0001 |
| 1.1096 | 45.0 | 43965 | 1.0066 | 0.6629 | 0.5715 | 0.6629 | 0.0001 |
| 1.106 | 46.0 | 44942 | 1.0081 | 0.6641 | 0.5788 | 0.6641 | 0.0001 |
| 1.1004 | 47.0 | 45919 | 1.0084 | 0.6621 | 0.5664 | 0.6621 | 0.0001 |
| 1.0996 | 48.0 | 46896 | 1.0034 | 0.6640 | 0.5789 | 0.6640 | 0.0001 |
| 1.1055 | 49.0 | 47873 | 1.0056 | 0.6644 | 0.5710 | 0.6644 | 0.0001 |
| 1.0928 | 50.0 | 48850 | 1.0027 | 0.6637 | 0.5762 | 0.6637 | 0.0001 |
| 1.0957 | 51.0 | 49827 | 1.0016 | 0.6640 | 0.5755 | 0.6640 | 0.0001 |
| 1.094 | 52.0 | 50804 | 1.0034 | 0.6660 | 0.5828 | 0.6660 | 0.0001 |
| 1.0851 | 53.0 | 51781 | 1.0005 | 0.6657 | 0.5774 | 0.6657 | 0.0001 |
| 1.0858 | 54.0 | 52758 | 1.0015 | 0.6665 | 0.5853 | 0.6665 | 0.0001 |
| 1.0887 | 55.0 | 53735 | 1.0008 | 0.6641 | 0.5816 | 0.6641 | 0.0001 |
| 1.0894 | 56.0 | 54712 | 1.0011 | 0.6633 | 0.5740 | 0.6633 | 0.0001 |
| 1.0721 | 57.0 | 55689 | 0.9987 | 0.6675 | 0.5810 | 0.6675 | 0.0001 |
| 1.0847 | 58.0 | 56666 | 0.9987 | 0.6647 | 0.5739 | 0.6647 | 0.0001 |
| 1.087 | 59.0 | 57643 | 0.9975 | 0.6680 | 0.5868 | 0.6680 | 0.0001 |
| 1.0957 | 60.0 | 58620 | 0.9997 | 0.6668 | 0.5825 | 0.6668 | 0.0001 |
| 1.0562 | 61.0 | 59597 | 0.9961 | 0.6655 | 0.5857 | 0.6655 | 0.0001 |
| 1.0792 | 62.0 | 60574 | 0.9957 | 0.6669 | 0.5864 | 0.6669 | 0.0001 |
| 1.0654 | 63.0 | 61551 | 0.9942 | 0.6646 | 0.5848 | 0.6646 | 0.0001 |
| 1.0794 | 64.0 | 62528 | 0.9977 | 0.6631 | 0.5768 | 0.6631 | 0.0001 |
| 1.0688 | 65.0 | 63505 | 0.9981 | 0.6675 | 0.5840 | 0.6675 | 0.0001 |
| 1.0702 | 66.0 | 64482 | 0.9941 | 0.6678 | 0.5827 | 0.6678 | 0.0001 |
| 1.0719 | 67.0 | 65459 | 0.9940 | 0.6673 | 0.5887 | 0.6673 | 0.0001 |
| 1.0606 | 68.0 | 66436 | 0.9937 | 0.6665 | 0.5906 | 0.6665 | 0.0001 |
| 1.073 | 69.0 | 67413 | 0.9925 | 0.6684 | 0.5864 | 0.6684 | 0.0001 |
| 1.0729 | 70.0 | 68390 | 0.9926 | 0.6666 | 0.5869 | 0.6666 | 0.0001 |
| 1.0579 | 71.0 | 69367 | 0.9945 | 0.6668 | 0.5925 | 0.6668 | 0.0001 |
| 1.0619 | 72.0 | 70344 | 0.9900 | 0.6654 | 0.5865 | 0.6654 | 0.0001 |
| 1.0603 | 73.0 | 71321 | 0.9959 | 0.6679 | 0.5885 | 0.6679 | 0.0001 |
| 1.0596 | 74.0 | 72298 | 0.9902 | 0.6652 | 0.5860 | 0.6652 | 0.0001 |
| 1.0488 | 75.0 | 73275 | 0.9896 | 0.6664 | 0.5904 | 0.6664 | 0.0001 |
| 1.0494 | 76.0 | 74252 | 0.9927 | 0.6650 | 0.5839 | 0.6650 | 0.0001 |
| 1.0637 | 77.0 | 75229 | 0.9886 | 0.6675 | 0.5902 | 0.6675 | 0.0001 |
| 1.0594 | 78.0 | 76206 | 0.9865 | 0.6675 | 0.5811 | 0.6675 | 0.0001 |
| 1.0476 | 79.0 | 77183 | 0.9862 | 0.6647 | 0.5847 | 0.6647 | 0.0001 |
| 1.0523 | 80.0 | 78160 | 0.9879 | 0.6674 | 0.5823 | 0.6674 | 0.0001 |
| 1.0437 | 81.0 | 79137 | 0.9858 | 0.6670 | 0.5925 | 0.6670 | 0.0001 |
| 1.0553 | 82.0 | 80114 | 0.9868 | 0.6675 | 0.5928 | 0.6675 | 0.0001 |
| 1.0477 | 83.0 | 81091 | 0.9887 | 0.6656 | 0.5865 | 0.6656 | 0.0001 |
| 1.0489 | 84.0 | 82068 | 0.9865 | 0.6701 | 0.5972 | 0.6701 | 0.0001 |
| 1.0378 | 85.0 | 83045 | 0.9858 | 0.6691 | 0.5932 | 0.6691 | 0.0001 |
| 1.0495 | 86.0 | 84022 | 0.9848 | 0.6687 | 0.5900 | 0.6687 | 0.0001 |
| 1.0323 | 87.0 | 84999 | 0.9842 | 0.6686 | 0.5908 | 0.6686 | 0.0001 |
| 1.0337 | 88.0 | 85976 | 0.9886 | 0.6682 | 0.5853 | 0.6682 | 0.0001 |
| 1.043 | 89.0 | 86953 | 0.9871 | 0.6668 | 0.5868 | 0.6668 | 0.0001 |
| 1.0443 | 90.0 | 87930 | 0.9833 | 0.6677 | 0.5838 | 0.6677 | 0.0001 |
| 1.0418 | 91.0 | 88907 | 0.9833 | 0.6710 | 0.5983 | 0.6710 | 0.0001 |
| 1.0503 | 92.0 | 89884 | 0.9863 | 0.6719 | 0.5904 | 0.6719 | 0.0001 |
| 1.0341 | 93.0 | 90861 | 0.9845 | 0.6701 | 0.5913 | 0.6701 | 0.0001 |
| 1.0428 | 94.0 | 91838 | 0.9834 | 0.6702 | 0.5900 | 0.6702 | 0.0001 |
| 1.0216 | 95.0 | 92815 | 0.9854 | 0.6696 | 0.5900 | 0.6696 | 0.0001 |
| 1.0367 | 96.0 | 93792 | 0.9854 | 0.6712 | 0.5888 | 0.6712 | 0.0001 |
| 1.0198 | 97.0 | 94769 | 0.9830 | 0.6709 | 0.5922 | 0.6709 | 1e-05 |
| 1.0232 | 98.0 | 95746 | 0.9819 | 0.6728 | 0.5942 | 0.6728 | 1e-05 |
| 1.0187 | 99.0 | 96723 | 0.9812 | 0.6729 | 0.5958 | 0.6729 | 1e-05 |
| 1.0152 | 100.0 | 97700 | 0.9811 | 0.6703 | 0.5958 | 0.6703 | 1e-05 |
| 1.0116 | 101.0 | 98677 | 0.9795 | 0.6743 | 0.5970 | 0.6743 | 1e-05 |
| 1.0083 | 102.0 | 99654 | 0.9782 | 0.6736 | 0.5923 | 0.6736 | 1e-05 |
| 1.0046 | 103.0 | 100631 | 0.9786 | 0.6704 | 0.5916 | 0.6704 | 1e-05 |
| 0.9976 | 104.0 | 101608 | 0.9803 | 0.6727 | 0.5977 | 0.6727 | 1e-05 |
| 1.0142 | 105.0 | 102585 | 0.9823 | 0.6704 | 0.5954 | 0.6704 | 1e-05 |
| 1.0203 | 106.0 | 103562 | 0.9786 | 0.6755 | 0.6003 | 0.6755 | 1e-05 |
| 0.997 | 107.0 | 104539 | 0.9784 | 0.6719 | 0.5920 | 0.6719 | 1e-05 |
| 1.0156 | 108.0 | 105516 | 0.9776 | 0.6752 | 0.5974 | 0.6752 | 1e-05 |
| 0.9917 | 109.0 | 106493 | 0.9783 | 0.6720 | 0.5916 | 0.6720 | 1e-05 |
| 1.005 | 110.0 | 107470 | 0.9785 | 0.6704 | 0.5914 | 0.6704 | 1e-05 |
| 1.0112 | 111.0 | 108447 | 0.9781 | 0.6718 | 0.5987 | 0.6718 | 1e-05 |
| 1.0072 | 112.0 | 109424 | 0.9769 | 0.6710 | 0.5923 | 0.6710 | 1e-05 |
| 0.9939 | 113.0 | 110401 | 0.9785 | 0.6724 | 0.5976 | 0.6724 | 1e-05 |
| 1.0092 | 114.0 | 111378 | 0.9780 | 0.6716 | 0.5908 | 0.6716 | 1e-05 |
| 1.0052 | 115.0 | 112355 | 0.9774 | 0.6742 | 0.5972 | 0.6742 | 1e-05 |
| 0.9947 | 116.0 | 113332 | 0.9798 | 0.6726 | 0.5890 | 0.6726 | 1e-05 |
| 1.011 | 117.0 | 114309 | 0.9785 | 0.6727 | 0.5930 | 0.6727 | 1e-05 |
| 0.9985 | 118.0 | 115286 | 0.9778 | 0.6735 | 0.5972 | 0.6735 | 1e-05 |
| 1.008 | 119.0 | 116263 | 0.9776 | 0.6722 | 0.5910 | 0.6722 | 0.0000 |
| 1.0061 | 120.0 | 117240 | 0.9797 | 0.6720 | 0.5945 | 0.6720 | 0.0000 |
| 1.0014 | 121.0 | 118217 | 0.9790 | 0.6732 | 0.5938 | 0.6732 | 0.0000 |
| 0.9879 | 122.0 | 119194 | 0.9772 | 0.6739 | 0.5951 | 0.6739 | 0.0000 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu118
- Datasets 3.0.2
- Tokenizers 0.21.1
|
[
"algae",
"acr",
"anem",
"cca",
"ech",
"fts",
"gal",
"gon",
"mtp",
"p",
"poc",
"por",
"r",
"rdc",
"s",
"sg",
"ser",
"slt",
"sp",
"unk"
] |
Jedrzej-Smok/2025-05-30_12-32-11
|
<!-- 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. -->
# 2025-05-30_12-32-11
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 generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4394
- Accuracy: 0.9583
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7634 | 1.0 | 1 | 0.7635 | 0.5 |
| 0.7627 | 2.0 | 2 | 0.6879 | 0.5417 |
| 0.679 | 3.0 | 3 | 0.6253 | 0.6667 |
| 0.6123 | 4.0 | 4 | 0.5741 | 0.9583 |
| 0.5609 | 5.0 | 5 | 0.5507 | 0.9583 |
| 0.5149 | 6.0 | 6 | 0.4901 | 0.9583 |
| 0.4774 | 7.0 | 7 | 0.4753 | 0.9583 |
| 0.4421 | 8.0 | 8 | 0.4502 | 0.9583 |
| 0.4263 | 9.0 | 9 | 0.4482 | 0.9583 |
| 0.4111 | 10.0 | 10 | 0.4394 | 0.9583 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"healthy",
"sick"
] |
Fuscosucof/fusco_alzheimerMRI_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. -->
# fusco_alzheimerMRI_model
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0434
- Accuracy: 0.9902
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8393 | 1.0 | 72 | 0.9099 | 0.5703 |
| 0.6828 | 2.0 | 144 | 0.7748 | 0.6914 |
| 0.5156 | 3.0 | 216 | 0.5721 | 0.7637 |
| 0.3141 | 4.0 | 288 | 0.2799 | 0.9082 |
| 0.2024 | 5.0 | 360 | 0.5322 | 0.8066 |
| 0.0934 | 6.0 | 432 | 0.6212 | 0.8555 |
| 0.0307 | 7.0 | 504 | 0.1234 | 0.9551 |
| 0.0383 | 8.0 | 576 | 0.1279 | 0.9570 |
| 0.0195 | 9.0 | 648 | 0.1022 | 0.9746 |
| 0.0102 | 10.0 | 720 | 0.1057 | 0.9629 |
| 0.0081 | 11.0 | 792 | 0.0785 | 0.9766 |
| 0.0009 | 12.0 | 864 | 0.0641 | 0.9805 |
| 0.0023 | 13.0 | 936 | 0.0612 | 0.9824 |
| 0.0089 | 14.0 | 1008 | 0.0571 | 0.9746 |
| 0.0008 | 15.0 | 1080 | 0.0437 | 0.9902 |
| 0.0014 | 16.0 | 1152 | 0.0375 | 0.9883 |
| 0.0002 | 17.0 | 1224 | 0.0485 | 0.9824 |
| 0.0002 | 18.0 | 1296 | 0.0450 | 0.9863 |
| 0.0002 | 19.0 | 1368 | 0.0451 | 0.9863 |
| 0.0002 | 20.0 | 1440 | 0.0434 | 0.9902 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
Marc-Hagenbusch/vit-base-caltech-ucsd-birds-200-2011
|
<!-- 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-caltech-ucsd-birds-200-2011
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the bentrevett/caltech-ucsd-birds-200-2011 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0943
- Accuracy: 0.7608
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0059 | 1.0 | 590 | 4.7309 | 0.1094 |
| 3.5528 | 2.0 | 1180 | 3.2157 | 0.5199 |
| 2.3284 | 3.0 | 1770 | 2.2254 | 0.6455 |
| 1.7561 | 4.0 | 2360 | 1.7277 | 0.6819 |
| 1.5242 | 5.0 | 2950 | 1.5194 | 0.6955 |
| 1.2769 | 6.0 | 3540 | 1.3285 | 0.7396 |
| 1.1813 | 7.0 | 4130 | 1.3044 | 0.7193 |
| 1.097 | 8.0 | 4720 | 1.1878 | 0.7506 |
| 1.1306 | 9.0 | 5310 | 1.1145 | 0.7566 |
| 0.9988 | 10.0 | 5900 | 1.0868 | 0.7506 |
| 0.9887 | 11.0 | 6490 | 1.0760 | 0.7659 |
| 0.9826 | 12.0 | 7080 | 1.0849 | 0.7634 |
| 0.9612 | 13.0 | 7670 | 1.0659 | 0.7642 |
| 0.9679 | 14.0 | 8260 | 1.0939 | 0.7455 |
| 1.0242 | 15.0 | 8850 | 1.0941 | 0.7472 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
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"56",
"57",
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"59",
"60",
"61",
"62",
"63",
"64",
"65",
"66",
"67",
"68",
"69",
"70",
"71",
"72",
"73",
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"75",
"76",
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"78",
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"80",
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"83",
"84",
"85",
"86",
"87",
"88",
"89",
"90",
"91",
"92",
"93",
"94",
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"96",
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"100",
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"125",
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"133",
"134",
"135",
"136",
"137",
"138",
"139",
"140",
"141",
"142",
"143",
"144",
"145",
"146",
"147",
"148",
"149",
"150",
"151",
"152",
"153",
"154",
"155",
"156",
"157",
"158",
"159",
"160",
"161",
"162",
"163",
"164",
"165",
"166",
"167",
"168",
"169",
"170",
"171",
"172",
"173",
"174",
"175",
"176",
"177",
"178",
"179",
"180",
"181",
"182",
"183",
"184",
"185",
"186",
"187",
"188",
"189",
"190",
"191",
"192",
"193",
"194",
"195",
"196",
"197",
"198",
"199"
] |
viazzana/vit-fruits-classifier
|
<!-- 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-fruits-classifier
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Custom fruit image dataset (uploaded from GitHub) without augmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1299
- Accuracy: 0.9663
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1341 | 1.0 | 520 | 0.1599 | 0.9538 |
| 0.0929 | 2.0 | 1040 | 0.1430 | 0.9577 |
| 0.0834 | 3.0 | 1560 | 0.1416 | 0.9606 |
| 0.072 | 4.0 | 2080 | 0.1385 | 0.9596 |
| 0.0536 | 5.0 | 2600 | 0.1386 | 0.9606 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
[
"acai",
"apple",
"apricot",
"avocado",
"banana",
"black_berry",
"coconut",
"corn_kernel",
"cranberry",
"dragonfruit",
"durian",
"eggplant",
"elderberry",
"fig",
"grape",
"grapefruit",
"hard_kiwi",
"indian_strawberry",
"jalapeno",
"mandarine",
"mango",
"papaya",
"passion_fruit",
"pineapple",
"pomegranate",
"raspberry"
] |
natix-miner1v2/streetvision
|
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natix-miner2/streetvision
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natix-miner3/streetvision
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natix-miner4/streetvision
|
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natix-miner5/streetvision
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natix-miner6/streetvision
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natix-miner7/streetvision
|
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natix-miner12v2/streetvision
|
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natix-miner13v2/streetvision
|
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natix-miner14v2/streetvision
|
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kalemlhub/sn72-roadwork-gF4W23
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kalemlhub/sn72-roadwork-GYmPmL
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kalemlhub/sn72-roadwork-m8uBHd
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kalemlhub/sn72-roadwork-aNqSWr
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kalemlhub/sn72-roadwork-b1WxDL
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kalemlhub/sn72-roadwork-SUCbdK
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kalemlhub/sn72-roadwork-vUZHox
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kalemlhub/sn72-roadwork-YF6FC9
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kalemlhub/sn72-roadwork-e5GDwo
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kalemlhub/sn72-roadwork-rpC1at
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kalemlhub/sn72-roadwork-83cbHp
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kalemlhub/sn72-roadwork-11QLxe
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kalemlhub/sn72-roadwork-BkHr1B
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kalemlhub/sn72-roadwork-DnzfN2
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natix-miner15v2/streetvision
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kalemlhub/sn72-roadwork-5LzDmC
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kalemlhub/sn72-roadwork-hdxPSu
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kalemlhub/sn72-roadwork-E3JhdV
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kalemlhub/sn72-roadwork-u6aqT7
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kalemlhub/sn72-roadwork-JZqvWw
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kalemlhub/sn72-roadwork-HGPAUX
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kalemlhub/sn72-roadwork-84DyzJ
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kalemlhub/sn72-roadwork-QkDwo5
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kalemlhub/sn72-roadwork-pFaGcF
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kalemlhub/sn72-roadwork-PuPLUW
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kalemlhub/sn72-roadwork-dynC17
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kalemlhub/sn72-roadwork-me2kzv
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kalemlhub/sn72-roadwork-g3jDKg
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kalemlhub/sn72-roadwork-LYHz1s
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kalemlhub/sn72-roadwork-ztRoz1
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kalemlhub/sn72-roadwork-weEwKz
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kalemlhub/sn72-roadwork-MAtQUb
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"roadwork"
] |
kalemlhub/sn72-roadwork-TXHhuC
|
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[
"none",
"roadwork"
] |
kalemlhub/sn72-roadwork-bke3Hv
|
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|
[
"none",
"roadwork"
] |
kalemlhub/sn72-roadwork-1u441s
|
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|
[
"none",
"roadwork"
] |
kalemlhub/sn72-roadwork-ebLfHr
|
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
[
"none",
"roadwork"
] |
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