model_id
stringlengths 7
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|---|---|---|
TalentoTechIA/nsandov24
|
<!-- 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. -->
# nsandov24
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: 0.0929
- Accuracy: 0.9699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1334 | 3.8462 | 500 | 0.0929 | 0.9699 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
remonemo/beans_mit_aug
|
<!-- 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. -->
# beans_mit_aug
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the nateraw/beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1334
- Accuracy: 0.9688
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- 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: 4
### Training results
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV73
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV73
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3467
- Accuracy: 0.8914
## 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: 4e-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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1143 | 1.0 | 15 | 1.0550 | 0.5886 |
| 0.9057 | 2.0 | 30 | 0.7895 | 0.7314 |
| 0.7128 | 3.0 | 45 | 0.5484 | 0.76 |
| 0.491 | 4.0 | 60 | 0.4717 | 0.7829 |
| 0.4667 | 5.0 | 75 | 0.3817 | 0.8457 |
| 0.39 | 6.0 | 90 | 0.3647 | 0.88 |
| 0.3975 | 7.0 | 105 | 0.3604 | 0.8571 |
| 0.3496 | 8.0 | 120 | 0.3397 | 0.8457 |
| 0.3546 | 9.0 | 135 | 0.3968 | 0.84 |
| 0.2798 | 10.0 | 150 | 0.3983 | 0.8571 |
| 0.2813 | 11.0 | 165 | 0.3591 | 0.8571 |
| 0.2178 | 12.0 | 180 | 0.4332 | 0.8571 |
| 0.234 | 13.0 | 195 | 0.3892 | 0.8686 |
| 0.2539 | 14.0 | 210 | 0.3944 | 0.8743 |
| 0.2147 | 15.0 | 225 | 0.5366 | 0.8286 |
| 0.2045 | 16.0 | 240 | 0.3467 | 0.8914 |
| 0.1826 | 17.0 | 255 | 0.4077 | 0.8629 |
| 0.1703 | 18.0 | 270 | 0.4043 | 0.8743 |
| 0.1928 | 19.0 | 285 | 0.4470 | 0.88 |
| 0.1853 | 20.0 | 300 | 0.4742 | 0.8857 |
| 0.1503 | 21.0 | 315 | 0.5047 | 0.8743 |
| 0.1692 | 22.0 | 330 | 0.4166 | 0.8686 |
| 0.1436 | 23.0 | 345 | 0.4770 | 0.8686 |
| 0.1715 | 24.0 | 360 | 0.4240 | 0.8514 |
| 0.1449 | 25.0 | 375 | 0.4297 | 0.8743 |
| 0.1051 | 26.0 | 390 | 0.5423 | 0.8743 |
| 0.1055 | 27.0 | 405 | 0.5705 | 0.8743 |
| 0.1407 | 28.0 | 420 | 0.5883 | 0.8743 |
| 0.1463 | 29.0 | 435 | 0.4944 | 0.8857 |
| 0.1413 | 30.0 | 450 | 0.5196 | 0.8914 |
| 0.1521 | 31.0 | 465 | 0.6213 | 0.8571 |
| 0.1318 | 32.0 | 480 | 0.7071 | 0.84 |
| 0.1357 | 33.0 | 495 | 0.5737 | 0.8743 |
| 0.1182 | 34.0 | 510 | 0.5457 | 0.88 |
| 0.1382 | 35.0 | 525 | 0.5622 | 0.8743 |
| 0.1108 | 36.0 | 540 | 0.5403 | 0.88 |
| 0.0948 | 37.0 | 555 | 0.6038 | 0.8629 |
| 0.0964 | 38.0 | 570 | 0.6299 | 0.8743 |
| 0.1035 | 39.0 | 585 | 0.5474 | 0.8857 |
| 0.0892 | 40.0 | 600 | 0.5423 | 0.8914 |
| 0.1121 | 41.0 | 615 | 0.5783 | 0.8686 |
| 0.1119 | 42.0 | 630 | 0.6223 | 0.8743 |
| 0.1103 | 43.0 | 645 | 0.6355 | 0.88 |
| 0.0934 | 44.0 | 660 | 0.5847 | 0.88 |
| 0.0861 | 45.0 | 675 | 0.6008 | 0.88 |
| 0.0933 | 46.0 | 690 | 0.6066 | 0.88 |
| 0.0909 | 46.6780 | 700 | 0.6084 | 0.88 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1
|
[
"avanzada",
"avanzada humeda",
"no dmae"
] |
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV74
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV74
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3840
- Accuracy: 0.9314
## 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: 4e-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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1566 | 1.0 | 15 | 1.1332 | 0.2286 |
| 1.0369 | 2.0 | 30 | 0.9972 | 0.6171 |
| 0.9264 | 3.0 | 45 | 0.6970 | 0.7714 |
| 0.629 | 4.0 | 60 | 0.4858 | 0.8286 |
| 0.5428 | 5.0 | 75 | 0.4445 | 0.8171 |
| 0.4508 | 6.0 | 90 | 0.3967 | 0.8457 |
| 0.4188 | 7.0 | 105 | 0.3711 | 0.8286 |
| 0.3688 | 8.0 | 120 | 0.3448 | 0.8629 |
| 0.3503 | 9.0 | 135 | 0.3299 | 0.8743 |
| 0.2885 | 10.0 | 150 | 0.2780 | 0.8971 |
| 0.2582 | 11.0 | 165 | 0.3197 | 0.8686 |
| 0.3041 | 12.0 | 180 | 0.4322 | 0.8514 |
| 0.2957 | 13.0 | 195 | 0.4167 | 0.84 |
| 0.2821 | 14.0 | 210 | 0.4550 | 0.84 |
| 0.2713 | 15.0 | 225 | 0.4461 | 0.8343 |
| 0.2402 | 16.0 | 240 | 0.3447 | 0.8971 |
| 0.1729 | 17.0 | 255 | 0.3471 | 0.8914 |
| 0.1773 | 18.0 | 270 | 0.3235 | 0.8914 |
| 0.155 | 19.0 | 285 | 0.3583 | 0.8857 |
| 0.1867 | 20.0 | 300 | 0.3917 | 0.88 |
| 0.159 | 21.0 | 315 | 0.3539 | 0.8743 |
| 0.1498 | 22.0 | 330 | 0.3438 | 0.8629 |
| 0.1585 | 23.0 | 345 | 0.3092 | 0.8914 |
| 0.1655 | 24.0 | 360 | 0.2937 | 0.9029 |
| 0.1191 | 25.0 | 375 | 0.3470 | 0.8971 |
| 0.1756 | 26.0 | 390 | 0.3520 | 0.8857 |
| 0.1467 | 27.0 | 405 | 0.3552 | 0.88 |
| 0.1788 | 28.0 | 420 | 0.3994 | 0.8914 |
| 0.108 | 29.0 | 435 | 0.3685 | 0.9143 |
| 0.1359 | 30.0 | 450 | 0.3831 | 0.9086 |
| 0.0982 | 31.0 | 465 | 0.4162 | 0.8971 |
| 0.1154 | 32.0 | 480 | 0.3634 | 0.9086 |
| 0.0959 | 33.0 | 495 | 0.3828 | 0.8914 |
| 0.1136 | 34.0 | 510 | 0.4778 | 0.88 |
| 0.1184 | 35.0 | 525 | 0.3773 | 0.8857 |
| 0.0858 | 36.0 | 540 | 0.3628 | 0.8971 |
| 0.0958 | 37.0 | 555 | 0.4379 | 0.8857 |
| 0.0891 | 38.0 | 570 | 0.3993 | 0.8857 |
| 0.1132 | 39.0 | 585 | 0.3931 | 0.8857 |
| 0.093 | 40.0 | 600 | 0.3840 | 0.9314 |
| 0.094 | 41.0 | 615 | 0.3870 | 0.9029 |
| 0.0938 | 42.0 | 630 | 0.4061 | 0.8914 |
| 0.1024 | 43.0 | 645 | 0.5408 | 0.8571 |
| 0.0835 | 44.0 | 660 | 0.3230 | 0.9086 |
| 0.0795 | 45.0 | 675 | 0.3599 | 0.9086 |
| 0.053 | 46.0 | 690 | 0.4675 | 0.8686 |
| 0.0673 | 47.0 | 705 | 0.4926 | 0.8857 |
| 0.0888 | 48.0 | 720 | 0.4276 | 0.8971 |
| 0.0539 | 49.0 | 735 | 0.5204 | 0.8686 |
| 0.0662 | 50.0 | 750 | 0.4685 | 0.9143 |
| 0.0738 | 51.0 | 765 | 0.3824 | 0.9143 |
| 0.0459 | 52.0 | 780 | 0.4200 | 0.9143 |
| 0.0651 | 53.0 | 795 | 0.4323 | 0.9086 |
| 0.0744 | 54.0 | 810 | 0.4256 | 0.9086 |
| 0.0499 | 55.0 | 825 | 0.4435 | 0.9143 |
| 0.076 | 56.0 | 840 | 0.4884 | 0.9086 |
| 0.0689 | 57.0 | 855 | 0.4900 | 0.9029 |
| 0.0899 | 58.0 | 870 | 0.5848 | 0.8971 |
| 0.054 | 59.0 | 885 | 0.5400 | 0.9143 |
| 0.0524 | 60.0 | 900 | 0.5701 | 0.8914 |
| 0.0532 | 61.0 | 915 | 0.5269 | 0.9029 |
| 0.0596 | 62.0 | 930 | 0.5223 | 0.88 |
| 0.0814 | 63.0 | 945 | 0.5199 | 0.8914 |
| 0.0899 | 64.0 | 960 | 0.4566 | 0.9143 |
| 0.0649 | 65.0 | 975 | 0.4577 | 0.92 |
| 0.0508 | 66.0 | 990 | 0.4655 | 0.92 |
| 0.0529 | 67.0 | 1005 | 0.4868 | 0.9029 |
| 0.0707 | 68.0 | 1020 | 0.4883 | 0.9029 |
| 0.0603 | 69.0 | 1035 | 0.5059 | 0.9029 |
| 0.0776 | 70.0 | 1050 | 0.5878 | 0.8857 |
| 0.0747 | 71.0 | 1065 | 0.4694 | 0.92 |
| 0.0681 | 72.0 | 1080 | 0.5228 | 0.8857 |
| 0.0357 | 73.0 | 1095 | 0.5152 | 0.8857 |
| 0.049 | 74.0 | 1110 | 0.5197 | 0.9086 |
| 0.0335 | 75.0 | 1125 | 0.5028 | 0.9143 |
| 0.0523 | 76.0 | 1140 | 0.6165 | 0.8857 |
| 0.0258 | 77.0 | 1155 | 0.5491 | 0.8971 |
| 0.0398 | 78.0 | 1170 | 0.5218 | 0.8971 |
| 0.0408 | 79.0 | 1185 | 0.6784 | 0.8743 |
| 0.0606 | 80.0 | 1200 | 0.4997 | 0.9086 |
| 0.062 | 81.0 | 1215 | 0.5853 | 0.8857 |
| 0.0485 | 82.0 | 1230 | 0.5230 | 0.9086 |
| 0.0337 | 83.0 | 1245 | 0.5579 | 0.8971 |
| 0.0474 | 84.0 | 1260 | 0.5233 | 0.8971 |
| 0.0542 | 85.0 | 1275 | 0.5082 | 0.9143 |
| 0.0512 | 86.0 | 1290 | 0.5149 | 0.9029 |
| 0.0497 | 87.0 | 1305 | 0.5065 | 0.8971 |
| 0.0317 | 88.0 | 1320 | 0.4929 | 0.9143 |
| 0.0507 | 89.0 | 1335 | 0.5252 | 0.9143 |
| 0.0229 | 90.0 | 1350 | 0.5545 | 0.8914 |
| 0.0248 | 91.0 | 1365 | 0.5472 | 0.8914 |
| 0.0499 | 92.0 | 1380 | 0.5360 | 0.8971 |
| 0.0241 | 93.0 | 1395 | 0.5291 | 0.8971 |
| 0.0458 | 94.0 | 1410 | 0.5110 | 0.9029 |
| 0.0304 | 95.0 | 1425 | 0.5070 | 0.9029 |
| 0.1043 | 96.0 | 1440 | 0.5084 | 0.9086 |
| 0.0454 | 97.0 | 1455 | 0.5128 | 0.9086 |
| 0.0474 | 98.0 | 1470 | 0.5270 | 0.8971 |
| 0.0325 | 99.0 | 1485 | 0.5307 | 0.8971 |
| 0.0273 | 100.0 | 1500 | 0.5334 | 0.8971 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1
|
[
"avanzada",
"avanzada humeda",
"no dmae"
] |
jungnerd/your_model_name
|
# 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## 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]
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<!-- 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]
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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]
|
[
"benign",
"malignant",
"normal"
] |
lautenad/vit-base-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-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: 0.0407
- 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 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1597 | 1.0 | 410 | 0.1298 | 0.9853 |
| 0.0549 | 2.0 | 820 | 0.0672 | 0.9902 |
| 0.0324 | 3.0 | 1230 | 0.0568 | 0.9902 |
| 0.0247 | 4.0 | 1640 | 0.0512 | 0.9902 |
| 0.0175 | 5.0 | 2050 | 0.0502 | 0.9902 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"72",
"84",
"70",
"51",
"48",
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"58",
"40",
"35",
"60",
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"4",
"85",
"82",
"10",
"13",
"37",
"76",
"19",
"97"
] |
huangqishan/nn
|
# 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]
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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]
|
[
"0 - zero",
"1 - one",
"2 - two",
"3 - three",
"4 - four",
"5 - five",
"6 - six",
"7 - seven",
"8 - eight",
"9 - nine"
] |
gutkia01/vit-food-classification-gutkia01
|
<!-- 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-classification-gutkia01
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0 | 1.0 | 2171 | 0.0000 | 1.0 |
| 0.0 | 2.0 | 4342 | 0.0000 | 1.0 |
| 0.0 | 3.0 | 6513 | 0.0000 | 1.0 |
| 0.0 | 4.0 | 8684 | 0.0000 | 1.0 |
| 0.0 | 5.0 | 10855 | 0.0000 | 1.0 |
| 0.0 | 6.0 | 13026 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"fruits",
"junkfood"
] |
remonemo/beans_no_aug_freeze
|
<!-- 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. -->
# beans_no_aug_freeze
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the nateraw/beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4318
- Accuracy: 0.8281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- 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: 4
### Training results
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
mbiarreta/vit-ena24-MD
|
<!-- 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-ena24-MD
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 ena24_MD dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6827
- Accuracy: 0.6826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4013 | 0.1259 | 100 | 1.8994 | 0.6631 |
| 0.4178 | 0.2519 | 200 | 2.0700 | 0.5938 |
| 0.336 | 0.3778 | 300 | 1.6827 | 0.6826 |
| 0.2547 | 0.5038 | 400 | 1.8338 | 0.6582 |
| 0.1166 | 0.6297 | 500 | 1.9549 | 0.6699 |
| 0.0555 | 0.7557 | 600 | 1.7759 | 0.7021 |
| 0.3521 | 0.8816 | 700 | 2.0155 | 0.6592 |
| 0.0144 | 1.0076 | 800 | 1.9693 | 0.6738 |
| 0.0847 | 1.1335 | 900 | 1.7930 | 0.7227 |
| 0.0041 | 1.2594 | 1000 | 1.7101 | 0.7334 |
| 0.0232 | 1.3854 | 1100 | 1.7127 | 0.7324 |
| 0.1154 | 1.5113 | 1200 | 1.8169 | 0.7236 |
| 0.0355 | 1.6373 | 1300 | 1.7981 | 0.7334 |
| 0.0042 | 1.7632 | 1400 | 1.7519 | 0.7451 |
| 0.0435 | 1.8892 | 1500 | 1.8185 | 0.7344 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"american black bear",
"american crow",
"eastern fox squirrel",
"eastern gray squirrel",
"grey fox",
"horse",
"northern raccoon",
"red fox",
"striped skunk",
"virginia opossum",
"white_tailed_deer",
"wild turkey",
"bird",
"woodchuck",
"bobcat",
"chicken",
"coyote",
"dog",
"domestic cat",
"eastern chipmunk",
"eastern cottontail"
] |
kuchikihater/vit-skin-cancer
|
<!-- 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-data-augmentation-balanced-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the HAM1000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6023
- Accuracy: 0.8527
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 64
- 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: 15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
[
"akiec",
"bcc",
"bkl",
"df",
"mel",
"nv",
"vasc"
] |
thomas970804/Medicine-Drinking-Recognition-VIT-Base-patch16-224
|
<!-- 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. -->
# Medicine-Drinking-Recognition-VIT-Base-patch16-224
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1334
- Accuracy: 0.9653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0816 | 1.0 | 7 | 0.9886 | 0.5208 |
| 0.758 | 2.0 | 14 | 0.5653 | 0.8449 |
| 0.4036 | 3.0 | 21 | 0.2762 | 0.9259 |
| 0.2487 | 4.0 | 28 | 0.2734 | 0.9051 |
| 0.1794 | 5.0 | 35 | 0.1806 | 0.9352 |
| 0.1409 | 6.0 | 42 | 0.1626 | 0.9468 |
| 0.1272 | 7.0 | 49 | 0.1662 | 0.9468 |
| 0.1082 | 8.0 | 56 | 0.1270 | 0.9630 |
| 0.1108 | 9.0 | 63 | 0.1334 | 0.9653 |
| 0.0892 | 10.0 | 70 | 0.1547 | 0.9491 |
| 0.0848 | 11.0 | 77 | 0.1408 | 0.9560 |
| 0.0811 | 12.0 | 84 | 0.1447 | 0.9514 |
| 0.0776 | 13.0 | 91 | 0.1412 | 0.9537 |
| 0.0773 | 14.0 | 98 | 0.1506 | 0.9514 |
| 0.0868 | 15.0 | 105 | 0.1693 | 0.9468 |
| 0.0723 | 16.0 | 112 | 0.1771 | 0.9421 |
| 0.0814 | 17.0 | 119 | 0.1592 | 0.9514 |
| 0.0719 | 18.0 | 126 | 0.1319 | 0.9630 |
| 0.0769 | 19.0 | 133 | 0.1283 | 0.9653 |
| 0.0709 | 20.0 | 140 | 0.1294 | 0.9653 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"other",
"drinking",
"taking_medicine"
] |
izeah01/image_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. -->
# image_classification
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: 4.5739
- Accuracy: 0.05
## 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
- 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
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.05 | 2 | 4.5851 | 0.0 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- 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"
] |
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV76
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV76
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4077
- Accuracy: 0.88
## 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: 4e-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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1017 | 0.9524 | 15 | 1.0955 | 0.3143 |
| 0.9212 | 1.9524 | 30 | 0.8475 | 0.6743 |
| 0.7117 | 2.9524 | 45 | 0.6980 | 0.6171 |
| 0.5496 | 3.9524 | 60 | 0.4957 | 0.8 |
| 0.5051 | 4.9524 | 75 | 0.4578 | 0.7714 |
| 0.4331 | 5.9524 | 90 | 0.3767 | 0.8457 |
| 0.4324 | 6.9524 | 105 | 0.4334 | 0.8229 |
| 0.3664 | 7.9524 | 120 | 0.4469 | 0.7829 |
| 0.335 | 8.9524 | 135 | 0.3407 | 0.8743 |
| 0.2977 | 9.9524 | 150 | 0.3569 | 0.84 |
| 0.2978 | 10.9524 | 165 | 0.3858 | 0.8686 |
| 0.2983 | 11.9524 | 180 | 0.3657 | 0.8571 |
| 0.2539 | 12.9524 | 195 | 0.3979 | 0.8514 |
| 0.2215 | 13.9524 | 210 | 0.3755 | 0.8514 |
| 0.2474 | 14.9524 | 225 | 0.4143 | 0.8457 |
| 0.2245 | 15.9524 | 240 | 0.3954 | 0.8629 |
| 0.2427 | 16.9524 | 255 | 0.4063 | 0.8743 |
| 0.2036 | 17.9524 | 270 | 0.4762 | 0.8343 |
| 0.2397 | 18.9524 | 285 | 0.4077 | 0.88 |
| 0.2157 | 19.9524 | 300 | 0.5519 | 0.8114 |
| 0.221 | 20.9524 | 315 | 0.5091 | 0.8114 |
| 0.1799 | 21.9524 | 330 | 0.4301 | 0.8629 |
| 0.1777 | 22.9524 | 345 | 0.4592 | 0.8743 |
| 0.1641 | 23.9524 | 360 | 0.4445 | 0.8686 |
| 0.1582 | 24.9524 | 375 | 0.4807 | 0.8571 |
| 0.1394 | 25.9524 | 390 | 0.4472 | 0.8743 |
| 0.16 | 26.9524 | 405 | 0.5020 | 0.8743 |
| 0.1826 | 27.9524 | 420 | 0.4834 | 0.8686 |
| 0.1648 | 28.9524 | 435 | 0.5368 | 0.8629 |
| 0.155 | 29.9524 | 450 | 0.5284 | 0.8514 |
| 0.1378 | 30.9524 | 465 | 0.4585 | 0.8743 |
| 0.1608 | 31.9524 | 480 | 0.4883 | 0.8686 |
| 0.1435 | 32.9524 | 495 | 0.5400 | 0.84 |
| 0.1444 | 33.9524 | 510 | 0.5379 | 0.8571 |
| 0.1504 | 34.9524 | 525 | 0.5876 | 0.8629 |
| 0.1108 | 35.9524 | 540 | 0.5414 | 0.8571 |
| 0.1392 | 36.9524 | 555 | 0.5801 | 0.8571 |
| 0.1065 | 37.9524 | 570 | 0.5940 | 0.8629 |
| 0.087 | 38.9524 | 585 | 0.6316 | 0.8571 |
| 0.127 | 39.9524 | 600 | 0.6509 | 0.8571 |
| 0.1198 | 40.9524 | 615 | 0.6311 | 0.8571 |
| 0.1255 | 41.9524 | 630 | 0.5793 | 0.8514 |
| 0.1317 | 42.9524 | 645 | 0.5860 | 0.8343 |
| 0.1016 | 43.9524 | 660 | 0.5839 | 0.8629 |
| 0.1249 | 44.9524 | 675 | 0.5763 | 0.8571 |
| 0.0762 | 45.9524 | 690 | 0.5853 | 0.8629 |
| 0.1075 | 46.9524 | 705 | 0.5967 | 0.8514 |
| 0.0792 | 47.9524 | 720 | 0.6012 | 0.8457 |
| 0.1033 | 48.9524 | 735 | 0.5989 | 0.8457 |
| 0.1115 | 49.9524 | 750 | 0.6030 | 0.8457 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1
|
[
"avanzada",
"avanzada humeda",
"no dmae"
] |
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV77
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV77
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3654
- Accuracy: 0.8914
## 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: 4e-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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1317 | 0.9524 | 15 | 1.0850 | 0.3657 |
| 1.0134 | 1.9524 | 30 | 1.0048 | 0.56 |
| 0.9444 | 2.9524 | 45 | 0.7731 | 0.7371 |
| 0.6457 | 3.9524 | 60 | 0.5598 | 0.7486 |
| 0.5593 | 4.9524 | 75 | 0.4836 | 0.7771 |
| 0.4781 | 5.9524 | 90 | 0.4486 | 0.7829 |
| 0.4769 | 6.9524 | 105 | 0.4365 | 0.8114 |
| 0.4366 | 7.9524 | 120 | 0.5434 | 0.7657 |
| 0.4248 | 8.9524 | 135 | 0.3744 | 0.8457 |
| 0.3298 | 9.9524 | 150 | 0.3618 | 0.8343 |
| 0.342 | 10.9524 | 165 | 0.3661 | 0.8629 |
| 0.3033 | 11.9524 | 180 | 0.3753 | 0.8343 |
| 0.3106 | 12.9524 | 195 | 0.4607 | 0.8229 |
| 0.264 | 13.9524 | 210 | 0.3623 | 0.8457 |
| 0.2407 | 14.9524 | 225 | 0.3982 | 0.8343 |
| 0.2758 | 15.9524 | 240 | 0.3694 | 0.8514 |
| 0.2719 | 16.9524 | 255 | 0.5112 | 0.8171 |
| 0.2311 | 17.9524 | 270 | 0.3977 | 0.8571 |
| 0.246 | 18.9524 | 285 | 0.4087 | 0.8629 |
| 0.2193 | 19.9524 | 300 | 0.4239 | 0.8343 |
| 0.2419 | 20.9524 | 315 | 0.3980 | 0.8514 |
| 0.2084 | 21.9524 | 330 | 0.4278 | 0.8686 |
| 0.1973 | 22.9524 | 345 | 0.3654 | 0.8914 |
| 0.1807 | 23.9524 | 360 | 0.4050 | 0.8629 |
| 0.1693 | 24.9524 | 375 | 0.5299 | 0.8229 |
| 0.1594 | 25.9524 | 390 | 0.4832 | 0.8514 |
| 0.1876 | 26.9524 | 405 | 0.5069 | 0.84 |
| 0.1514 | 27.9524 | 420 | 0.5056 | 0.8571 |
| 0.1818 | 28.9524 | 435 | 0.5403 | 0.8286 |
| 0.1682 | 29.9524 | 450 | 0.5058 | 0.84 |
| 0.1681 | 30.9524 | 465 | 0.5187 | 0.8114 |
| 0.1394 | 31.9524 | 480 | 0.5843 | 0.8629 |
| 0.1659 | 32.9524 | 495 | 0.4707 | 0.8629 |
| 0.1753 | 33.9524 | 510 | 0.5603 | 0.8229 |
| 0.1884 | 34.9524 | 525 | 0.5372 | 0.8343 |
| 0.1399 | 35.9524 | 540 | 0.5559 | 0.8629 |
| 0.1603 | 36.9524 | 555 | 0.6177 | 0.8629 |
| 0.1353 | 37.9524 | 570 | 0.5262 | 0.8457 |
| 0.0874 | 38.9524 | 585 | 0.4945 | 0.8629 |
| 0.1054 | 39.9524 | 600 | 0.6391 | 0.8629 |
| 0.1156 | 40.9524 | 615 | 0.6080 | 0.8514 |
| 0.1247 | 41.9524 | 630 | 0.6483 | 0.8114 |
| 0.1396 | 42.9524 | 645 | 0.5377 | 0.8457 |
| 0.117 | 43.9524 | 660 | 0.5460 | 0.8629 |
| 0.1403 | 44.9524 | 675 | 0.6856 | 0.84 |
| 0.1089 | 45.9524 | 690 | 0.6401 | 0.8514 |
| 0.1022 | 46.9524 | 705 | 0.6795 | 0.8514 |
| 0.09 | 47.9524 | 720 | 0.6025 | 0.8457 |
| 0.0948 | 48.9524 | 735 | 0.6489 | 0.8514 |
| 0.1177 | 49.9524 | 750 | 0.6105 | 0.8571 |
| 0.0797 | 50.9524 | 765 | 0.7485 | 0.8229 |
| 0.0872 | 51.9524 | 780 | 0.6390 | 0.84 |
| 0.1038 | 52.9524 | 795 | 0.6190 | 0.8743 |
| 0.1361 | 53.9524 | 810 | 0.6417 | 0.8457 |
| 0.1205 | 54.9524 | 825 | 0.6161 | 0.84 |
| 0.1026 | 55.9524 | 840 | 0.5836 | 0.8514 |
| 0.1059 | 56.9524 | 855 | 0.6865 | 0.8571 |
| 0.0999 | 57.9524 | 870 | 0.7455 | 0.8629 |
| 0.1075 | 58.9524 | 885 | 0.7018 | 0.8343 |
| 0.0952 | 59.9524 | 900 | 0.6851 | 0.8286 |
| 0.0796 | 60.9524 | 915 | 0.6301 | 0.8514 |
| 0.0952 | 61.9524 | 930 | 0.6734 | 0.8343 |
| 0.1041 | 62.9524 | 945 | 0.6475 | 0.8514 |
| 0.0961 | 63.9524 | 960 | 0.7369 | 0.8229 |
| 0.0897 | 64.9524 | 975 | 0.7261 | 0.84 |
| 0.0591 | 65.9524 | 990 | 0.7303 | 0.8286 |
| 0.106 | 66.9524 | 1005 | 0.6512 | 0.84 |
| 0.0817 | 67.9524 | 1020 | 0.6835 | 0.8229 |
| 0.0653 | 68.9524 | 1035 | 0.7211 | 0.8514 |
| 0.0801 | 69.9524 | 1050 | 0.7762 | 0.8343 |
| 0.0754 | 70.9524 | 1065 | 0.7669 | 0.8571 |
| 0.067 | 71.9524 | 1080 | 0.8578 | 0.8457 |
| 0.0896 | 72.9524 | 1095 | 0.8271 | 0.84 |
| 0.0622 | 73.9524 | 1110 | 0.7458 | 0.8286 |
| 0.0741 | 74.9524 | 1125 | 0.7236 | 0.84 |
| 0.0687 | 75.9524 | 1140 | 0.7986 | 0.84 |
| 0.0877 | 76.9524 | 1155 | 0.7999 | 0.8286 |
| 0.1034 | 77.9524 | 1170 | 0.7840 | 0.8286 |
| 0.0716 | 78.9524 | 1185 | 0.7871 | 0.8343 |
| 0.0659 | 79.9524 | 1200 | 0.7860 | 0.8571 |
| 0.0844 | 80.9524 | 1215 | 0.8366 | 0.8514 |
| 0.0858 | 81.9524 | 1230 | 0.8152 | 0.8629 |
| 0.0531 | 82.9524 | 1245 | 0.7717 | 0.8286 |
| 0.075 | 83.9524 | 1260 | 0.8578 | 0.8171 |
| 0.059 | 84.9524 | 1275 | 0.8240 | 0.8229 |
| 0.0896 | 85.9524 | 1290 | 0.8907 | 0.8343 |
| 0.0741 | 86.9524 | 1305 | 0.8814 | 0.84 |
| 0.0697 | 87.9524 | 1320 | 0.9080 | 0.8286 |
| 0.0552 | 88.9524 | 1335 | 0.8345 | 0.8343 |
| 0.0576 | 89.9524 | 1350 | 0.8746 | 0.8229 |
| 0.0729 | 90.9524 | 1365 | 0.8196 | 0.8343 |
| 0.0782 | 91.9524 | 1380 | 0.8073 | 0.8343 |
| 0.0584 | 92.9524 | 1395 | 0.8011 | 0.8286 |
| 0.0471 | 93.9524 | 1410 | 0.8076 | 0.8286 |
| 0.0544 | 94.9524 | 1425 | 0.8390 | 0.8229 |
| 0.0576 | 95.9524 | 1440 | 0.8575 | 0.8286 |
| 0.0608 | 96.9524 | 1455 | 0.8392 | 0.8229 |
| 0.064 | 97.9524 | 1470 | 0.8266 | 0.8286 |
| 0.0742 | 98.9524 | 1485 | 0.8311 | 0.8286 |
| 0.0471 | 99.9524 | 1500 | 0.8333 | 0.8286 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1
|
[
"avanzada",
"avanzada humeda",
"no dmae"
] |
harsh-patel-us/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0506
- Accuracy: 0.9837
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2107 | 1.0 | 190 | 0.1038 | 0.9641 |
| 0.1565 | 2.0 | 380 | 0.0672 | 0.9774 |
| 0.1092 | 3.0 | 570 | 0.0506 | 0.9837 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"annual crop",
"forest",
"herbaceous vegetation",
"highway",
"industrial",
"pasture",
"permanent crop",
"residential",
"river",
"sea or lake"
] |
prithivMLmods/Flood-Image-Detection
|
# Flood-Image-Detection
> Flood-Image-Detection is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **binary image classification**. It is trained to detect whether an image contains a **flooded scene** or **non-flooded** environment. The model uses the `SiglipForImageClassification` architecture.
> [!note]
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features : https://arxiv.org/pdf/2502.14786
```py
Classification Report:
precision recall f1-score support
Flooded Scene 0.9172 0.9458 0.9313 609
Non Flooded 0.9744 0.9603 0.9673 1309
accuracy 0.9557 1918
macro avg 0.9458 0.9530 0.9493 1918
weighted avg 0.9562 0.9557 0.9559 1918
```
---
## Label Space: 2 Classes
```
Class 0: Flooded Scene
Class 1: Non Flooded
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/flood-image-detection" # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Flooded Scene",
"1": "Non Flooded"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Flood Detection"),
title="Flood-Image-Detection",
description="Upload an image to detect whether the scene is flooded or not."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`Flood-Image-Detection` is designed for:
* **Disaster Monitoring** – Rapid detection of flood-affected areas from imagery.
* **Environmental Analysis** – Track flooding patterns across regions using image datasets.
* **Crisis Response** – Assist emergency services in identifying critical zones.
* **Surveillance and Safety** – Monitor infrastructure or locations for flood exposure.
* **Smart Alert Systems** – Integrate with IoT or camera feeds for automated flood alerts.
|
[
"flooded scene",
"non flooded"
] |
prithivMLmods/Forest-Fire-Detection
|
# Forest-Fire-Detection
> `Forest-Fire-Detection` is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect whether an image contains **fire**, **smoke**, or a **normal** (non-fire) scene. The model uses the `SiglipForImageClassification` architecture.
> [!note]
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features : https://arxiv.org/pdf/2502.14786
```py
Classification Report:
precision recall f1-score support
Fire 0.9960 0.9896 0.9928 2020
Normal 0.9902 0.9960 0.9931 2020
Smoke 0.9995 1.0000 0.9998 2020
accuracy 0.9952 6060
macro avg 0.9952 0.9952 0.9952 6060
weighted avg 0.9952 0.9952 0.9952 6060
```
---
## Label Space: 3 Classes
```
Class 0: Fire
Class 1: Normal
Class 2: Smoke
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Forest-Fire-Detection" # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Fire",
"1": "Normal",
"2": "Smoke"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=3, label="Forest Fire Detection"),
title="Forest-Fire-Detection",
description="Upload an image to detect whether the scene contains fire, smoke, or is normal."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`Forest-Fire-Detection` is designed for:
* **Wildfire Monitoring** – Rapid identification of forest fire and smoke zones.
* **Environmental Protection** – Surveillance of forest areas for early fire warning.
* **Disaster Management** – Support in emergency response and evacuation decisions.
* **Smart Surveillance** – Integrate with drones or camera feeds for automated fire detection.
* **Research and Analysis** – Analyze visual datasets for fire-prone region identification.
|
[
"fire",
"normal",
"smoke"
] |
Sisigoks/FloraSense
|
# 🌿 Sisigoks/FloraSense
**FloraSense** is a fine-tuned Vision Transformer (ViT) model designed for accurate classification of plant species and flora-related imagery. It builds on top of the powerful `google/vit-base-patch16-224` base model and is fine-tuned on the **Planter_GARDEN_EDITION** dataset curated by [Sisigoks](https://huggingface.co/Sisigoks), which includes over 10,000 diverse plant images.
---
## 🧠 Model Description
- **Architecture**: Vision Transformer (ViT)
- **Base Model**: [`google/vit-base-patch16-224`](https://huggingface.co/google/vit-base-patch16-224)
- **Task**: Image Classification
- **Use Case**: Automated plant and flora species recognition in digital botany, garden classification systems, plant care apps, biodiversity projects, and educational tools.
---
## 📊 Model Performance
- **Evaluation Accuracy**: **35.46%**
- **Evaluation Loss**: 4.2894
- **Epochs Trained**: 10
- **Evaluation Speed**:
- 33.9 samples/sec
- 2.12 steps/sec
> ⚠️ While the accuracy may appear moderate, the model is handling over **10,000** highly similar plant species, making this a non-trivial challenge in fine-grained classification.
---
## 🧪 Training Procedure
| Hyperparameter | Value |
|-----------------------|----------------------------|
| Learning Rate | 5e-5 |
| Train Batch Size | 16 |
| Eval Batch Size | 16 |
| Gradient Accumulation | 4 |
| Total Effective Batch | 64 |
| Optimizer | Adam (β1=0.9, β2=0.999) |
| Scheduler | Linear w/ warmup (10%) |
| Epochs | 15 |
| Seed | 42 |
- **Framework**: PyTorch
- **Libraries**: Transformers 4.45.1, Datasets 3.0.1, Tokenizers 0.20.0
---
## 📚 Dataset
- **Name**: [`Sisigoks/Planter_GARDEN_EDITION`](https://huggingface.co/datasets/Sisigoks/Planter_GARDEN_EDITION)
- **Type**: Image Classification
- **Language**: English
- **Scope**: Over 10,000 unique plant and floral species
- **Format**: Real-world garden and nature photography
- **Use Case**: Realistic and diverse training scenarios for classification models
---
## ✅ Intended Use
### Use Cases
- Botanical image recognition apps
- Educational tools for students and researchers
- Smart gardening & plant care solutions
- Field-use flora identification via AR and mobile apps
### Target Users
- Botanists
- AI and ML researchers
- Gardeners and farmers
- Biology educators and students
---
## ⚠️ Limitations
- May confuse visually similar species due to fine-grained class diversity.
- Performance could degrade in poor lighting or occlusion-heavy environments.
- Biases may exist based on the geographic scope of the dataset (e.g., underrepresentation of tropical or rare plants).
---
## 🔐 Ethical Considerations
- **Accuracy**: Misclassification of medicinal/toxic plants can have real-world safety implications.
- **Bias**: Regional, lighting, or season-specific training data may skew predictions in certain environments.
- **Usage**: This is a research-grade model and should not be relied on for critical decisions without expert validation.
---
## 🚀 How to Use
``` python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
# Load model and processor
processor = AutoImageProcessor.from_pretrained("Sisigoks/FloraSense")
model = AutoModelForImageClassification.from_pretrained("Sisigoks/FloraSense")
# Load and preprocess image
image = Image.open("your_image.jpg")
inputs = processor(images=image, return_tensors="pt")
# Inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_label = logits.argmax(-1).item()
print(f"Predicted class ID: {predicted_label}")
```
## 📄 Citation
If you use this model or dataset in your work, please cite:
```
@misc{sisigoks_florasense_2025,
author = {Sisigoks},
title = {FloraSense: ViT-based Fine-Grained Plant Classifier},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Sisigoks/FloraSense}}
}
```
## 🙌 Acknowledgements
- Hugging Face 🤗 – for providing the model and dataset hosting infrastructure.
- Google Research – for the original ViT architecture that enabled scalable vision transformers.
|
[
"abelia_chinensis_r_br_",
"abelia_grandiflora__ravelli_ex_andr___rehder",
"abelia_grandifolia_villarreal",
"abelia_triflora_r_br__ex_wall_",
"abelia_x_grandiflora__rovelli_ex_andr___rehder",
"abeliophyllum_distichum_nakai",
"abelmoschus_esculentus__l___moench",
"abelmoschus_manihot__l___medik_",
"abelmoschus_moschatus_medik_",
"abies_alba_mill_",
"abies_balsamea__l___mill_",
"abies_cephalonica_j_w_loudon",
"abies_cephalonica_loudon",
"abies_concolor__gord____glend___lindl__ex_hildebr_",
"abies_concolor__gordon__lindl__ex_hildebr_",
"abies_concolor__gordon___glend___lindl__ex_hildebr_",
"abies_fraseri__pursh__poir_",
"abies_grandis__douglas_ex_d_don__lindl_",
"abies_koreana_e_h_wilson",
"abies_lasiocarpa__hook___nutt_",
"abies_nebrodensis__lojac___mattei",
"abies_nordmanniana__steven__spach",
"abies_numidica_lannoy_ex_carri_re",
"abies_pinsapo_boiss_",
"abies_procera_rehder",
"abronia_latifolia_eschsch_",
"abronia_umbellata_lam_",
"abrus_precatorius_l_",
"abutilon_grandifolium__willd___sweet",
"abutilon_hirtum__lam___sweet",
"abutilon_indicum__l___sweet",
"abutilon_mauritianum__jacq___medik_",
"abutilon_megapotamicum__a_spreng___a_st__hil____naudin",
"abutilon_pictum__gillies_ex_hook___walp_",
"abutilon_spp_",
"abutilon_theophrasti_medik_",
"acacia_auriculiformis_benth_",
"acacia_baileyana_f_muell_",
"acacia_berlandieri_benth_",
"acacia_caven__molina__molina",
"acacia_cognata_domin",
"acacia_confluens_maiden___blakeley",
"acacia_confusa_merr_",
"acacia_cultriformis_g_don",
"acacia_dealbata_link",
"acacia_farnesiana__l___willd_",
"acacia_heterophylla__lam___willd_",
"acacia_karroo_hayne",
"acacia_longifolia__andrews__willd_",
"acacia_mangium_willd_",
"acacia_mearnsii_de_wild_",
"acacia_melanoxylon_r_br_",
"acacia_muricata__l___willd_",
"acacia_nilotica__l___delile",
"acacia_paradoxa_dc_",
"acacia_podalyriifolia_g_don",
"acacia_pravissima_f_muell_",
"acacia_pycnantha_benth_",
"acacia_redolens_maslin",
"acacia_retinodes_schltdl_",
"acacia_saligna__labill___h_l_wendl_",
"acacia_saligna__labill___wendl_",
"acacia_seyal_delile",
"acacia_spirorbis_labill_",
"acacia_tortilis__forssk___hayne",
"acacia_xanthophloea_benth_",
"acaena_microphylla_hook_f_",
"acaena_novae_zelandiae_kirk",
"acalypha_aristata_kunth",
"acalypha_australis_l_",
"acalypha_hispida_burm_f_",
"acalypha_indica_l_",
"acalypha_integrifolia_willd_",
"acalypha_macrostachya_jacq_",
"acalypha_virginica_l_",
"acalypha_wilkesiana_m_ll_arg_",
"acanthocereus_tetragonus__l___humm_",
"acanthocereus_tetragonus__l___hummelinck",
"acanthophoenix_rubra__bory__h_wendl_",
"acanthospermum_australe__loefl___kuntze",
"acanthospermum_hispidum_dc_",
"acanthus_ilicifolius_l_",
"acanthus_mollis_l_",
"acanthus_spinosus_l_",
"acca_sellowiana__o_berg__burret",
"acer___freemanii_a_e_murray",
"acer_buergerianum_miq_",
"acer_campestre_l_",
"acer_cappadocicum_gled_",
"acer_circinatum_pursh",
"acer_cissifolium__siebold___zucc___k_koch",
"acer_davidii_franch_",
"acer_ginnala_maxim_",
"acer_glabrum_torr_",
"acer_griseum__franch___pax",
"acer_japonicum_thunb_",
"acer_macrophyllum_pursh",
"acer_monspessulanum_l_",
"acer_negundo_l_",
"acer_opalus_mill_",
"acer_palmatum_thunb_",
"acer_pensylvanicum_l_",
"acer_platanoides_l_",
"acer_pseudoplatanus_l_",
"acer_rubrum_l_",
"acer_saccharinum_l_",
"acer_saccharum_marshall",
"acer_sempervirens_l_",
"acer_shirasawanum_koidz_",
"acer_spicatum_lam_",
"acer_tataricum_l_",
"acer_tataricum_subsp__ginnala__maxim___wesm_",
"acer_truncatum_bunge",
"achillea_ageratum_l_",
"achillea_atrata_l_",
"achillea_clavennae_l_",
"achillea_crithmifolia_waldst____kit_",
"achillea_distans_waldst____kit__ex_willd_",
"achillea_erba_rotta_all_",
"achillea_filipendulina_lam_",
"achillea_ligustica_all_",
"achillea_macrophylla_l_",
"achillea_maritima__l___ehrend____y_p_guo",
"achillea_millefolium_l_",
"achillea_nana_l_",
"achillea_nobilis_l_",
"achillea_odorata_l_",
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"aloe_striata_haw_",
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"aloe_zebrina_baker",
"aloinopsis_schooneesii_l_bolus",
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"althaea_cannabina_l_",
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"amelanchier_alnifolia__nutt___nutt__ex_m_roem_",
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"amelanchier_utahensis_koehne",
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"ammi_visnaga__l___lam_",
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"ampelodesmos_mauritanicus__poir___t_durand___schinz",
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"anacamptis_champagneuxii__barn_oud__r_m_bateman__pridgeon___m_w_chase",
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"ayapana_triplinervis__vahl__r_m_king___h_rob_",
"azadirachta_indica_a_juss_",
"azalea_alabamensis__rehder__ashe",
"azolla_caroliniana_willd_",
"azolla_filiculoides_lam_",
"azolla_pinnata_r__br_",
"baccharis_douglasii_dc_",
"baccharis_halimifolia_l_",
"baccharis_neglecta_britton",
"baccharis_pilularis_dc_",
"baccharis_trimera__less___dc_",
"bacopa_monnieri__l___wettst_",
"bacopa_repens__sw___wettst_",
"bactris_gasipaes_kunth",
"badula_barthesia__lam___a_dc_",
"balanites_aegyptiaca__l___delile",
"balanites_maughamii_sprague",
"balantium_antarcticum__labill___c__presl",
"baldellia_ranunculoides__l___parl_",
"ballota_acetabulosa_benth_",
"ballota_hirsuta_benth_",
"ballota_nigra_l_",
"ballota_pseudodictamnus__l___benth_",
"baloghia_inophylla__g_forst___p_s_green",
"balsamorhiza_sagittata__pursh__nutt_",
"bambusa_bambos__l___voss",
"bambusa_multiplex__lour___raeusch__ex_schult_",
"bambusa_multiplex__lour___raeusch__ex_schult____schult__f_",
"bambusa_tuldoides_munro",
"bambusa_vulgaris_schrad_",
"bambusa_vulgaris_schrad__ex_j_c__wendl_",
"banara_guianensis_aubl_",
"banksia_ericifolia_l_f_",
"banksia_integrifolia_l_f_",
"banksia_praemorsa_andrews",
"banksia_serrata_l_f_",
"banksia_spinulosa_sm_",
"baptisia_alba__l___vent_",
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"baptisia_australis__l___r__br_",
"baptisia_bracteata_elliott",
"baptisia_sphaerocarpa_nutt_",
"baptisia_tinctoria__l___vent_",
"barbarea_australis_hook_f_",
"barbarea_intermedia_boreau",
"barbarea_orthoceras_ledeb_",
"barbarea_rupicola_moris",
"barbarea_verna__mill___asch_",
"barbarea_vulgaris_r_br_",
"barbarea_vulgaris_w_t__aiton",
"barleria_cristata_l_",
"barleria_lupulina_lindl_",
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"barleria_repens_nees",
"barringtonia_acutangula__l___gaertn_",
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"bartsia_alpina_l_",
"bartsia_trixago_l_",
"basella_alba_l_",
"basselinia_deplanchei__brongn____gris__vieill_",
"basselinia_pancheri__brongn____gris__vieill_",
"bassia_prostrata__l___beck",
"bassia_scoparia__l___a_j_scott",
"bassia_scoparia__l___a_j__scott",
"bassia_scoparia__l___voss",
"batis_maritima_l_",
"bauhinia_acuminata_l_",
"bauhinia_forficata_link",
"bauhinia_galpinii_n_e_br_",
"bauhinia_kockiana_korth_",
"bauhinia_monandra_kurz",
"bauhinia_purpurea_l_",
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"beaucarnea_gracilis_lem_",
"beaucarnea_guatemalensis_rose",
"beaucarnea_recurvata_lem_",
"beaumontia_grandiflora_wall_",
"beclardia_macrostachya__thouars__a_rich_",
"begonia_aconitifolia_a_dc_",
"begonia_acutifolia_jacq_",
"begonia_boliviensis_a_dc_",
"begonia_brevirimosa_irmsch_",
"begonia_coccinea_hook_",
"begonia_convolvulacea__klotzsch__a_dc_",
"begonia_cucullata_willd_",
"begonia_cucullata_cv___doublet_rose_pink_",
"begonia_evansiana_andrews",
"begonia_formosana__hayata__masam_",
"begonia_fuchsioides_hook_",
"begonia_gehrtii_irmsch_",
"begonia_grandis_dryand_",
"begonia_handelii_irmsch_",
"begonia_heracleifolia_cham____schltdl_",
"begonia_hirtella_link",
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"begonia_imperialis_lem_",
"begonia_listada_l_b_sm____wassh_",
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"begonia_maculata_raddi",
"begonia_masoniana_irmsch__ex_ziesenh_",
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"begonia_spp_",
"begonia_sutherlandii_hook_f_",
"begonia_urophylla_hook_",
"begonia_variegata_y_m_shui___w_h_chen",
"begonia_x_semperflorens_link___otto",
"belamcanda_chinensis__l___dc_",
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"bellidiastrum_michelii_cass_",
"bellis_annua_l_",
"bellis_perennis_l_",
"bellis_sylvestris_cirillo",
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"berardia_lanuginosa__lam___fiori",
"berberis_aetnensis_c_presl",
"berberis_aquifolium_pursh",
"berberis_darwinii_hook_",
"berberis_gagnepainii_c_k_schneid_",
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"berberis_julianae_c_k_schneid_",
"berberis_thunbergii_dc_",
"berberis_vulgaris_l_",
"berberis_x_stenophylla_lindl_",
"berchemia_zeyheri__sond___grubov",
"bergenia_cordifolia__haw___sternb_",
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"bergeranthus_multiceps__salm_dyck__schwantes",
"berkheya_purpurea__dc___benth____hook_f__ex_mast_",
"berlandiera_lyrata_benth_",
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"berula_erecta__huds___coville",
"beschorneria_yuccoides_k_koch",
"beta_vulgaris_l_",
"betonica_alopecuros_l_",
"betonica_hirsuta_l_",
"betonica_macrantha_k_koch",
"betonica_officinalis_l_",
"betula_alleghaniensis_britton",
"betula_ermanii_cham_",
"betula_humilis_schrank",
"betula_lenta_l_",
"betula_maximowicziana_regel",
"betula_nana_l_",
"betula_nigra_l_",
"betula_papyrifera_marshall",
"betula_pendula_roth",
"betula_pubescens_ehrh_",
"betula_utilis_d_don",
"bidens_aurea__aiton__sherff",
"bidens_bipinnata_l_",
"bidens_cernua_l_",
"bidens_ferulifolia__jacq___sweet",
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"bidens_radiata_thuill_",
"bidens_subalternans_dc_",
"bidens_tripartita_l_",
"bidens_triplinervia_humb___bonpl____kunth",
"bidens_triplinervia_kunth",
"bifora_radians_m_bieb_",
"bignonia_capreolata_l_",
"bignonia_magnifica_w_bull",
"billardiera_heterophylla__lindl___l_w_cayzer___crisp",
"billbergia_nutans_h_wendl__ex_regel",
"billbergia_pyramidalis__sims__lindl_",
"billbergia_vittata_brongn__ex_c_morel",
"biophytum_sensitivum__l___dc_",
"bischofia_javanica_blume",
"bischofia_polycarpa__h_l_v___airy_shaw",
"biscutella_auriculata_l_",
"biscutella_cichoriifolia_loisel_",
"biscutella_laevigata_l_",
"bismarckia_nobilis_hildebr____h_wendl_",
"bistorta_amplexicaulis__d_don__greene",
"bistorta_officinalis_delarbre",
"bistorta_vivipara__l___delarbre",
"bituminaria_bituminosa__l___c_h_stirt_",
"bituminaria_bituminosa__l___c_h__stirt_",
"bixa_orellana_l_",
"blackstonia_perfoliata__l___huds_",
"blechnum_brasiliense_desv_",
"blechnum_spicant__l___roth",
"blechnum_spicant__l___sm_",
"blechum_pyramidatum__lam___urb_",
"bletia_purpurea__lam___a_dc_",
"bletilla_striata__thunb___rchb__f_",
"bletilla_striata__thunb___rchb_f_",
"blighia_sapida_k_d_koenig",
"blitum_bonus_henricus__l___c_a_mey_",
"blitum_capitatum_l_",
"blitum_virgatum_l_",
"blumea_balsamifera__l___dc_",
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"bocoa_prouacensis_aubl_",
"boehmeria_cylindrica__l___sw_",
"boehmeria_macrophylla_hornem_",
"boehmeria_nivea__l___gaudich_",
"boehmeria_penduliflora_wedd__ex_d_g_long",
"boerhavia_coccinea_mill_",
"boerhavia_diffusa_l_",
"boerhavia_erecta_l_",
"boerhavia_repens_l_",
"bolbitis_auriculata__lam___alston",
"bolboschoenus_maritimus__l___palla",
"bomarea_obovata_herb_",
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"borassus_flabellifer_l_",
"boronia_crenulata_sm_",
"boronia_heterophylla_f_muell_",
"borrichia_frutescens__l___dc_",
"boscia_albitrunca__burch___gilg___benedict",
"bothriochloa_barbinodis__lag___herter",
"bothriochloa_ischaemum__l___keng",
"botrychium_dissectum_spreng_",
"botrychium_lunaria__l___sw_",
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"bougainvillea_buttiana_holttum___standl_",
"bougainvillea_glabra_choisy",
"bougainvillea_spectabilis_willd_",
"bougainvillea_spp_",
"bouteloua_curtipendula__michx___torr_",
"bouvardia_ternifolia__cav___schltdl_",
"bowiea_volubilis_harv_",
"bowlesia_incana_ruiz___pav_",
"brachiaria_lachnantha__hochst___stapf",
"brachychiton_acerifolius_f_muell_",
"brachychiton_acerifolius__a_cunn__ex_g_don__f_muell_",
"brachychiton_bidwillii_hook_",
"brachychiton_discolor_f_muell_",
"brachychiton_populneus__schott___endl___r_br_",
"brachychiton_rupestris__lindl___k_schum_",
"brachychiton_rupestris__t_mitch__ex_lindl___k_schum_",
"brachyglottis_greyi__hook_f___b_nord_",
"brachylaena_transvaalensis_hutch__ex_e_phillips___schweick_",
"brachypodium_distachyon__l___p_beauv_",
"brachypodium_phoenicoides__l___roem____schult_",
"brachypodium_pinnatum__l___p_beauv_",
"brachypodium_retusum__pers___p_beauv_",
"brachypodium_sylvaticum__huds___p_beauv_",
"brahea_armata_s_watson",
"brasiliopuntia_brasiliensis__willd___a_berger",
"brassavola_acaulis_lindl____paxton",
"brassavola_cucullata__l___r_br_",
"brassavola_perrinii_lindl_",
"brassia_arachnoidea_barb_rodr_",
"brassia_caudata__l___lindl_",
"brassica_barrelieri__l___janka",
"brassica_fruticulosa_cirillo",
"brassica_juncea__l___czern_",
"brassica_napus_l_",
"brassica_nigra__l___k_koch",
"brassica_nigra__l___w_d_j_koch",
"brassica_oleracea_l_",
"brassica_rapa_l_",
"brassica_tournefortii_gouan",
"breonadia_salicina__vahl__hepper___j_r_i_wood",
"breynia_disticha_j_r_forst____g_forst_",
"breynia_vitis_idaea__burm_f___c_e_c_fisch_",
"brickellia_eupatorioides__l___shinners",
"bridelia_micrantha__hochst___baill_",
"brighamia_insignis_a_gray",
"brillantaisia_owariensis_p_beauv_",
"brimeura_amethystina__l___chouard",
"brimeura_fastigiata__viv___chouard",
"briza_maxima_l_",
"briza_media_l_",
"briza_minor_l_",
"brocchinia_reducta_baker",
"brodiaea_elegans_hoover",
"bromelia_karatas_l_",
"bromelia_pinguin_l_",
"bromopsis_erecta__huds___fourr_",
"bromopsis_inermis__leyss___holub",
"bromopsis_ramosa__huds___holub",
"bromus_arvensis_l_",
"bromus_catharticus_vahl",
"bromus_commutatus_schrad_",
"bromus_diandrus_roth",
"bromus_erectus_huds_",
"bromus_hordeaceus_l_",
"bromus_inermis_leyss_",
"bromus_lanceolatus_roth",
"bromus_madritensis_l_",
"bromus_racemosus_l_",
"bromus_rubens_l_",
"bromus_secalinus_l_",
"bromus_squarrosus_l_",
"bromus_sterilis_l_",
"bromus_tectorum_l_",
"brosimum_alicastrum_sw_",
"brosimum_rubescens_taub_",
"broussonetia_papyrifera__l___l_h_r__ex_vent_",
"broussonetia_papyrifera__l___vent_",
"browallia_americana_l_",
"browallia_speciosa_hook_",
"brownea_grandiceps_jacq_",
"browningia_hertlingiana__backeb___buxb_",
"brugmansia_arborea__l___steud_",
"brugmansia_sanguinea_d_don",
"brugmansia_sanguinea__ruiz___pav___d_don",
"brugmansia_spp_",
"brugmansia_suaveolens__humb____bonpl__ex_willd___bercht____j_presl",
"brugmansia_versicolor_lagerh_",
"brunfelsia_americana_l_",
"brunfelsia_pauciflora__cham____schltdl___benth_",
"brunfelsia_uniflora__pohl__d_don",
"brunnera_macrophylla__adams__i_m_johnst_",
"bryonia_alba_l_",
"bryonia_cretica_l_",
"bryophyllum_daigremontianum__raym__hamet___perrier__a_berger",
"bryophyllum_delagoense__eckl____zeyh___druce",
"bryophyllum_fedtschenkoi__raym__hamet___h_perrier__lauz__march_",
"bryophyllum_manginii__raym__hamet___h_perrier__nothdurft",
"bryophyllum_pinnatum__lam___oken",
"bucida_buceras_l_",
"buddleja_alternifolia_maxim_",
"buddleja_asiatica_lour_",
"buddleja_davidii_franch_",
"buddleja_globosa_hope",
"buddleja_lindleyana_fortune",
"buddleja_madagascariensis_lam_",
"buddleja_saligna_willd_",
"buddleja_salviifolia__l___lam_",
"buddleja_x_weyeriana_weyer",
"buglossoides_arvensis__l___i_m_johnst_",
"buglossoides_purpurocaerulea__l___i_m_johnst_",
"bulbine_frutescens__l___willd_",
"bulbophyllum_fletcherianum_rolfe",
"bulbophyllum_keekee_n_hall_",
"bulbophyllum_longiflorum_thouars",
"bulbophyllum_ngoyense_schltr_",
"bulbophyllum_nutans__thouars__thouars",
"bulbophyllum_occultum_thouars",
"bunchosia_armeniaca__cav___dc_",
"bunias_erucago_l_",
"bunias_orientalis_l_",
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"buphthalmum_salicifolium_l_",
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"bupleurum_ranunculoides_l_",
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"bupleurum_rotundifolium_l_",
"bupleurum_stellatum_l_",
"bupleurum_tenuissimum_l_",
"burkea_africana_hook_",
"bursera_fagaroides__kunth__engl_",
"bursera_simaruba__l___sarg_",
"butea_monosperma__lam___taub_",
"butia_capitata__mart___becc_",
"butomus_umbellatus_l_",
"buxus_balearica_lam_",
"buxus_microphylla_siebold___zucc_",
"buxus_sempervirens_l_",
"byrsonima_altissima_dc_",
"byrsonima_crassifolia__l___kunth",
"cabomba_caroliniana_a_gray",
"cactaceae",
"caesalpinia_bonduc__l___roxb_",
"caesalpinia_decapetala__roth__alston",
"caesalpinia_echinata_lam_",
"caesalpinia_ferrea_c_mart_",
"caesalpinia_gilliesii__hook___d_dietr_",
"caesalpinia_gilliesii__wall__ex_hook___d_dietr_",
"caesalpinia_pluviosa_dc_",
"caesalpinia_pulcherrima__l___sw_",
"caesalpinia_spinosa__molina__kuntze",
"caiophora_chuquitensis__meyen__urb____gilg",
"cajanus_cajan__l___huth",
"cajanus_cajan__l___millsp_",
"cakile_edentula__bigelow__hook_",
"cakile_maritima_scop_",
"caladenia_catenata__sm___druce",
"caladium_bicolor__aiton__vent_",
"caladium_bicolor__dryand___vent_",
"caladium_lindenii__andr___madison",
"calamagrostis___acutiflora__schrad___dc_",
"calamagrostis_arundinacea__l___roth",
"calamagrostis_canescens__weber__roth",
"calamagrostis_epigejos__l___roth",
"calamagrostis_varia__schrad___host",
"calamintha_nepeta__l___savi",
"calanthe_sylvatica__thouars__lindl_",
"calanthe_triplicata__willemet__ames",
"calathea_bachemiana_e_morren",
"calathea_bella__w_bull__regel",
"calathea_concinna__w_bull__k_schum_",
"calathea_crocata_e_morren___joriss_",
"calathea_crotalifera_s_watson",
"calathea_lancifolia_boom",
"calathea_louisae_gagnep_",
"calathea_lutea__aubl___e_mey__ex_schult_",
"calathea_majestica__linden__h_a_kenn_",
"calathea_makoyana_e_morren",
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"calathea_ornata__linden__k_rn_",
"calathea_picturata_k_koch___linden",
"calathea_roseopicta__linden__regel",
"calathea_rufibarba_fenzl",
"calathea_sanderiana__sander__gentil",
"calathea_spp_",
"calathea_undulata__linden___andr___linden___andr_",
"calathea_warscewiczii__l__mathieu_ex_planch___planch____linden",
"calathea_zebrina__sims__lindl_",
"calceolaria_integrifolia_l_",
"calceolaria_uniflora_lam_",
"calendula_algeriensis_boiss____reut_",
"calendula_arvensis_l_",
"calendula_arvensis_m_bieb_",
"calendula_arvensis__vaill___l_",
"calendula_officinalis_l_",
"calendula_stellata_cav_",
"calepina_irregularis__asso__thell_",
"calibrachoa_parviflora__juss___d_arcy",
"calicotome_spinosa__l___link",
"calicotome_villosa__poir___link",
"calla_palustris_l_",
"calliandra_brevipes_benth_",
"calliandra_calothyrsus_meisn_",
"calliandra_eriophylla_benth_",
"calliandra_haematocephala_hassk_",
"calliandra_surinamensis_benth_",
"calliandra_tergemina__l___benth_",
"calliandra_tweedii_benth_",
"callicarpa_americana_l_",
"callicarpa_bodinieri_h_l_v_",
"callicarpa_dichotoma__lour___k_koch",
"callicarpa_japonica_thunb_",
"callirhoe_digitata_nutt_",
"callisia_fragrans__lindl___woodson",
"callisia_gentlei_matuda",
"callisia_navicularis__ortgies__d_r_hunt",
"callisia_repens__jacq___l_",
"callistemon_citrinus__curtis__skeels",
"callistemon_viminalis__sol__ex_gaertn___g_don",
"callistephus_chinensis__l___nees",
"callitriche_obtusangula_le_gall",
"callitriche_palustris_l_",
"callitriche_stagnalis_scop_",
"callitris_sulcata__parl___schltr_",
"calluna_vulgaris__l___hull",
"calocedrus_decurrens__torr___florin",
"calochortus_gunnisonii_s__watson",
"calochortus_kennedyi_porter",
"calochortus_macrocarpus_douglas",
"calochortus_splendens_douglas_ex_benth_",
"calochortus_tolmiei_hook____arn_",
"calochortus_venustus_douglas_ex_benth_",
"calodendrum_capense__l_f___thunb_",
"calophyllum_calaba_l_",
"calophyllum_inophyllum_l_",
"calophyllum_tacamahaca_willd_",
"calothamnus_quadrifidus_r_br__ex_w_t_aiton",
"calotropis_gigantea__l___dryand_",
"calotropis_procera__aiton__dryand_",
"calotropis_procera__aiton__w_t_aiton",
"calotropis_procera__aiton__w_t__aiton",
"calpurnia_aurea__aiton__benth_",
"caltha_leptosepala_dc_",
"caltha_palustris_l_",
"calycanthus_floridus_l_",
"calypso_bulbosa__l___oakes",
"calyptocarpus_vialis_less_",
"calystegia_macrostegia__greene__brummitt",
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"chamaecrista_nictitans__l___moench",
"chamaecyparis_lawsoniana__a_murray__parl_",
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"chamaecyparis_obtusa__siebold___zucc___endl_",
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"chamaecytisus_proliferus__l_f___link",
"chamaedaphne_calyculata__l___moench",
"chamaedorea_costaricana_oerst_",
"chamaedorea_elegans_mart_",
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"chambeyronia_macrocarpa__brongn___vieill__ex_becc_",
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"chasmanthium_latifolium__michx___h_o_yates",
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"chassalia_corallioides__cordem___verdc_",
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"chelidonium_majus_l_",
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"chenopodiastrum_hybridum__l___s_fuentes__uotila___borsch",
"chenopodiastrum_murale__l___s_fuentes__uotila___borsch",
"chenopodiastrum_murale__l___s__fuentes",
"chenopodium_album_l_",
"chenopodium_ambrosioides_l_",
"chenopodium_berlandieri_moq_",
"chenopodium_bonus_henricus_l_",
"chenopodium_capitatum__l___asch_",
"chenopodium_ficifolium_sm_",
"chenopodium_giganteum_d_don",
"chenopodium_glaucum_l_",
"chenopodium_hybridum_l_",
"chenopodium_murale_l_",
"chenopodium_polyspermum_l_",
"chenopodium_quinoa_willd_",
"chenopodium_rubrum_l_",
"chenopodium_strictum_roth",
"chenopodium_vulvaria_l_",
"chiliadenus_glutinosus_fourr_",
"chiliadenus_glutinosus__l___fourr_",
"chilopsis_linearis__cav___sweet",
"chimaphila_maculata__l___pursh",
"chimaphila_umbellata__l___nutt_",
"chimaphila_umbellata__l___w_p_c_barton",
"chimonanthus_fragrans_lindl_",
"chimonanthus_praecox__l___link",
"chiococca_alba__l___hitchc_",
"chionanthus_foveolatus__e_mey___stearn",
"chionanthus_virginicus_l_",
"chionodoxa_luciliae_boiss_",
"chloris_barbata_sw_",
"chloris_gayana_kunth",
"chloris_pycnothrix_trin_",
"chloris_roxburghiana_schult_",
"chloris_verticillata_nutt_",
"chloris_virgata_sw_",
"chlorogalum_pomeridianum__dc___kunth",
"chlorophytum_capense__l___voss",
"chlorophytum_comosum__thunb___jacq_",
"chlorophytum_comosum__thunb___jacques",
"chlorophytum_orchidastrum_lindl_",
"chlorophytum_zavattarii__cufod___nordal",
"choisya_ternata_kunth",
"chondrilla_juncea_l_",
"chorispora_tenella__pall___dc_",
"christella_dentata__forssk___brownsey___jermy",
"christia_obcordata__poir___bakh_f_",
"chromolaena_odorata__l___r_m_king___h_rob_",
"chromolaena_odorata__l___r_m__king___h__rob_",
"chrozophora_tinctoria__l___a_juss_",
"chrysanthemoides_monilifera__l___norl_",
"chrysanthemum___grandiflorum_ramat_",
"chrysanthemum_coronarium_l_",
"chrysanthemum_indicum_l_",
"chrysanthemum_morifolium_ramat_",
"chrysanthemum_x_grandiflorum_ramat_",
"chrysanthemum_zawadskii_herbich",
"chrysobalanus_icaco_l_",
"chrysocephalum_apiculatum__labill___steetz",
"chrysogonum_virginianum_l_",
"chrysojasminum_fruticans__l___banfi",
"chrysophyllum_cainito_l_",
"chrysophyllum_oliviforme_l_",
"chrysophyllum_sanguinolentum__pierre__baehni",
"chrysopogon_gryllus__l___trin_",
"chrysopogon_plumulosus_hochst_",
"chrysopsis_mariana__l___elliott",
"chrysosplenium_alternifolium_l_",
"chrysosplenium_oppositifolium_l_",
"chrysothamnus_viscidiflorus__hook___nutt_",
"chrysothemis_pulchella__donn_ex_sims__decne_",
"chukrasia_tabularis_a_juss_",
"cicendia_filiformis__l___delarbre",
"cicer_arietinum_l_",
"cichorium_endivia_l_",
"cichorium_intybus_l_",
"cichorium_spinosum_l_",
"cicuta_maculata_l_",
"cicuta_virosa_l_",
"cinnamomum_camphora__l___j_presl",
"cinnamomum_camphora__l___j__presl",
"cinnamomum_verum_j_presl",
"circaea_alpina_l_",
"circaea_lutetiana_l_",
"cirsium_acaulon__l___scop_",
"cirsium_alsophilum__pollini__greuter",
"cirsium_alsophilum__pollini__soldano",
"cirsium_altissimum__l___hill",
"cirsium_arvense__l___scop_",
"cirsium_discolor__muhl__ex_willd___spreng_",
"cirsium_dissectum__l___hill",
"cirsium_eriophorum__l___scop_",
"cirsium_erisithales__jacq___scop_",
"cirsium_ferox__l___dc_",
"cirsium_heterophyllum__l___hill",
"cirsium_horridulum_michx_",
"cirsium_monspessulanum__l___hill",
"cirsium_oleraceum__l___scop_",
"cirsium_palustre__l___coss__ex_scop_",
"cirsium_palustre__l___scop_",
"cirsium_rivulare__jacq___all_",
"cirsium_spinosissimum__l___scop_",
"cirsium_texanum_buckley",
"cirsium_tuberosum__l___all_",
"cirsium_undulatum__nutt___spreng_",
"cirsium_vulgare__savi__ten_",
"cissampelos_pareira_l_",
"cissus_alata_jacq_",
"cissus_antarctica_vent_",
"cissus_javana_dc_",
"cissus_quadrangularis_l_",
"cissus_repens_lam_",
"cissus_rotundifolia_vahl",
"cissus_verticillata__l___nicolson___c_e_jarvis",
"cistanche_phelypaea__l___cout_",
"cistanche_violacea__desf___hoffmanns____link",
"cistus_albidus_l_",
"cistus_clusii_dunal",
"cistus_creticus_l_",
"cistus_crispus_l_",
"cistus_halimifolius_l_",
"cistus_ladanifer_l_",
"cistus_lasianthus_lam_",
"cistus_laurifolius_l_",
"cistus_monspeliensis_l_",
"cistus_parviflorus_lam_",
"cistus_populifolius_l_",
"cistus_salviifolius_l_",
"cistus_x_purpureus_lam_",
"citharexylum_spinosum_l_",
"citronella_costaricensis__donn_sm___r_a_howard",
"citrullus_colocynthis__l___schrad_",
"citrullus_lanatus__thunb___matsum____nakai",
"citrus___aurantium_l_",
"citrus_aurantiifolia__christm___swingle",
"citrus_aurantium_l_",
"citrus_australasica_f_muell_",
"citrus_hystrix_dc_",
"citrus_japonica_thunb_",
"citrus_limon__l___burm__f_",
"citrus_limon__l___burm_f_",
"citrus_limon__l___osbeck",
"citrus_maxima__burm___merr_",
"citrus_medica_l_",
"citrus_reticulata_blanco",
"citrus_sinensis__l___osbeck",
"citrus_trifoliata_l_",
"citrus_x_paradisi_macfad_",
"cladanthus_mixtus__l___chevall_",
"cladium_mariscus__l___pohl",
"cladrastis_kentukea__dum_cours___rudd",
"cladrastis_kentukea__dum__cours___rudd",
"claoxylon_glandulosum_boivin_ex_baill_",
"clarkia_amoena__lehm___a_nelson___j_f_macbr_",
"clarkia_amoena__lehm___a__nelson___j_f__macbr_",
"clarkia_pulchella_pursh",
"clarkia_unguiculata_lindl_",
"clavija_costaricana_pittier",
"claytonia_caroliniana_michx_",
"claytonia_perfoliata_donn_ex_willd_",
"claytonia_rubra__howell__tidestr_",
"claytonia_sibirica_l_",
"claytonia_virginica_l_",
"cleistocactus_baumannii__lem___lem_",
"cleistocactus_straussii__heese__backeb_",
"cleistocactus_winteri_d_r_hunt",
"clematis_alpina__l___mill_",
"clematis_armandii_franch_",
"clematis_cirrhosa_l_",
"clematis_crispa_l_",
"clematis_flammula_l_",
"clematis_heracleifolia_dc_",
"clematis_integrifolia_l_",
"clematis_lanuginosa_lindl_",
"clematis_lasiantha_nutt_",
"clematis_ligusticifolia_nutt_",
"clematis_mauritiana_lam_",
"clematis_montana_buch__ham__ex_dc_",
"clematis_occidentalis__hornem___dc_",
"clematis_orientalis_l_",
"clematis_patens_morren___decne_",
"clematis_recta_l_",
"clematis_tangutica__maxim___korsh_",
"clematis_terniflora_dc_",
"clematis_texensis_buckley",
"clematis_viorna_l_",
"clematis_virginiana_l_",
"clematis_vitalba_l_",
"clematis_viticella_l_",
"clematis_x_jackmanii_moore",
"cleome_gynandra_l_",
"cleome_hassleriana_chodat",
"cleome_houtteana_schltdl_",
"cleome_rutidosperma_dc_",
"cleome_serrulata_pursh",
"cleome_spinosa_jacq_",
"cleome_viscosa_l_",
"cleonia_lusitanica__l___l_",
"cleoserrata_speciosa__raf___iltis",
"clerodendron_trichotomum_thunb_",
"clerodendrum___speciosum_dombrain",
"clerodendrum_bungei_steud_",
"clerodendrum_chinense__osbeck__mabb_",
"clerodendrum_indicum__l___kuntze",
"clerodendrum_infortunatum_l_",
"clerodendrum_japonicum__thunb___sweet",
"clerodendrum_paniculatum_l_",
"clerodendrum_quadriloculare__blanco__merr_",
"clerodendrum_speciosissimum_drapiez",
"clerodendrum_splendens_g_don",
"clerodendrum_thomsoniae_balf_",
"clerodendrum_thomsoniae_balf_f_",
"clerodendrum_trichotomum_thunb_",
"clerodendrum_umbellatum_poir_",
"clethra_alnifolia_l_",
"clibadium_surinamense_l_",
"clidemia_capitellata__bonpl___d__don",
"clidemia_hirta__l___d_don",
"clidemia_hirta__l___d__don",
"clinacanthus_nutans__burm_f___lindau",
"clinopodium_acinos__l___kuntze",
"clinopodium_alpinum__l___kuntze",
"clinopodium_grandiflorum__l___kuntze",
"clinopodium_nepeta__l___kuntze",
"clinopodium_vulgare_l_",
"clintonia_andrewsiana_torr_",
"clintonia_borealis__aiton__raf_",
"clitoria_mariana_l_",
"clitoria_ternatea_l_",
"clivia_miniata_reg_",
"clivia_miniata__lindl___bosse",
"clivia_nobilis_lindl_",
"clusia_gracilis_standl_",
"clusia_grandiflora_splitg_",
"clusia_rosea_jacq_",
"clypeola_jonthlaspi_l_",
"cneorum_tricoccon_l_",
"cnidoscolus_aconitifolius__mill___i_m_johnst_",
"cnidoscolus_urens__l___arthur",
"cobaea_scandens_cav_",
"coccineorchis_cristata_szlach___rutk____mytnik",
"coccinia_grandis__l___voigt",
"coccocypselum_hirsutum_bartl__ex_dc_",
"coccoloba_pubescens_l_",
"coccoloba_uvifera__l___l_",
"cocculus_carolinus__l___dc_",
"cocculus_laurifolius_dc_",
"cochlearia_anglica_l_",
"cochlearia_danica_l_",
"cochlearia_officinalis_l_",
"cochliasanthus_caracalla__l___trew",
"cochliostema_odoratissimum_lem_",
"cochlospermum_vitifolium__willd___spreng_",
"cocos_nucifera_l_",
"codiaeum_spp_",
"codiaeum_variegatum__l___a_juss_",
"codiaeum_variegatum__l___a__juss_",
"codiaeum_variegatum__l___rumph__ex_a_juss_",
"coelogyne_asperata_lindl_",
"coelogyne_cristata_lindl_",
"coelogyne_flaccida_lindl_",
"coelogyne_pandurata_lindl_",
"coelogyne_viscosa_rchb_f_",
"coffea_abbayesii_j__f_leroy",
"coffea_arabica_l_",
"coffea_mauritiana_lam_",
"coincya_monensis__l___greuter___burdet",
"coincya_richeri__vill___greuter___burdet",
"coix_lacryma_jobi_l_",
"cojoba_arborea__l___britton___rose",
"cola_cordifolia__cav___r_br_",
"colchicum_alpinum_dc_",
"colchicum_autumnale_l_",
"colchicum_bulbocodium_ker_gawl_",
"colchicum_cupanii_guss_",
"colchicum_filifolium__cambess___stef_",
"colchicum_longifolium_castagne",
"colchicum_lusitanum_brot_",
"colchicum_montanum_l_",
"colchicum_multiflorum_brot_",
"colchicum_neapolitanum__ten___ten_",
"colchicum_variegatum_l_",
"coleocephalocereus_aureus_f_ritter",
"coleonema_pulchrum_hook_",
"coleostephus_myconis__l___cass_",
"coleostephus_myconis__l___cass__ex_rchb_f_",
"coleostephus_myconis__l___rchb_f_",
"coleus_spp_",
"colletia_cruciata_gillies___hook_",
"colletia_paradoxa__spreng___escal_",
"collinsonia_canadensis_l_",
"collomia_grandiflora_douglas_ex_lindl_",
"colocasia_esculenta__l___schott",
"colocasia_esculenta__l___schott_",
"colubrina_asiatica__l___brongn_",
"columnea_hirta_klotzsch___hanst_",
"columnea_medicinalis__wiehler__l_e_skog___l_p_kvist",
"columnea_microphylla_klotzsch___hanst__ex_oerst_",
"colutea_arborescens_l_",
"colutea_orientalis_mill_",
"comandra_umbellata__l___nutt_",
"comarum_palustre_l_",
"combretum_apiculatum_sond_",
"combretum_collinum_fresen_",
"combretum_constrictum__benth___m_a_lawson",
"combretum_erythrophyllum__burch___sond_",
"combretum_hereroense_schinz",
"combretum_imberbe_wawra",
"combretum_indicum__l___defilipps",
"combretum_kraussii_hochst_",
"combretum_molle_r_br__ex_g_don",
"combretum_rotundifolium_rich_",
"combretum_woodii_d_mmer",
"combretum_zeyheri_sond_",
"commelina_benghalensis_l_",
"commelina_coelestis_willd_",
"commelina_communis_l_",
"commelina_diffusa_burm__f_",
"commelina_diffusa_burm_f_",
"commelina_erecta_l_",
"commersonia_bartramia__l___merr_",
"commiphora_africana__a_rich___endl_",
"commiphora_glandulosa_schinz",
"commiphora_madagascariensis_jacq_",
"commiphora_mollis__oliv___engl_",
"comparettia_falcata_poepp____endl_",
"comptonia_peregrina__l___coult_",
"comptonia_peregrina__l___j_m__coult_",
"conceveiba_guianensis_aubl_",
"congea_tomentosa_roxb_",
"coniogramme_emeiensis_ching___k_h__shing",
"conium_maculatum_l_",
"conocarpus_erectus_l_",
"conoclinium_coelestinum__l___dc_",
"conopholis_alpina_liebm_",
"conopholis_americana__l___wallr_",
"conophytum_hians_n_e_br_",
"conopodium_majus__gouan__loret",
"conostegia_xalapensis__bonpl___d__don_ex_dc_",
"consolida_ajacis__l___schur",
"consolida_orientalis__j_gay__schr_dinger",
"consolida_regalis_gray",
"convallaria_majalis_l_",
"convolvulus_althaeoides_l_",
"convolvulus_arvensis_l_",
"convolvulus_betonicifolius_mill_",
"convolvulus_cantabrica_l_",
"convolvulus_cneorum_l_",
"convolvulus_equitans_benth_",
"convolvulus_lanuginosus_desr_",
"convolvulus_lineatus_l_",
"convolvulus_sabatius_viv_",
"convolvulus_sepium_l_",
"convolvulus_silvaticus_kit_",
"convolvulus_soldanella_l_",
"convolvulus_tricolor_l_",
"conyza_bonariensis__l___cronquist",
"conyza_canadensis__l___cronquist",
"conyza_floribunda_kunth",
"conyza_ramosissima_cronquist",
"conyza_sumatrensis__retz___e_walker",
"copiapoa_hypogaea_f_ritter",
"coprosma_granadensis__mutis_ex_l_f___heads",
"coprosma_repens_a_rich_",
"coptis_trifolia__l___salisb_",
"coptosperma_graveolens__s_moore__degreef",
"corallorhiza_maculata__raf___raf_",
"corallorhiza_mertensiana_bong_",
"corallorhiza_striata_lindl_",
"corallorhiza_trifida_ch_tel_",
"corchorus_aestuans_l_",
"corchorus_olitorius_l_",
"corchorus_trilocularis_l_",
"cordia_africana_lam_",
"cordia_alliodora__ruiz___pav___oken",
"cordia_caffra_sond_",
"cordia_collococca_l_",
"cordia_crenata_delile",
"cordia_curassavica__jacq___roem____schult_",
"cordia_dichotoma_g_forst_",
"cordia_monoica_roxb_",
"cordia_myxa_l_",
"cordia_ovalis_r__br_",
"cordia_sebestena_l_",
"cordia_subcordata_lam_",
"cordia_superba_cham_",
"cordyla_africana_lour_",
"cordyline_australis__g_forst___endl_",
"cordyline_australis__g__forst___endl_",
"cordyline_fruticosa__l___a_chev_",
"cordyline_fruticosa__l___a__chev",
"cordyline_fruticosa__l___a__chev_",
"cordyline_fruticosa__l__ex_stickm___a_chev_",
"cordyline_indivisa__g_forst___endl_",
"cordyline_mauritiana__lam___j_f_macbr_",
"cordyline_stricta__sims__endl_",
"corema_alba__l___d_don",
"coreopsis_auriculata_l_",
"coreopsis_grandiflora_hogg_ex_sweet",
"coreopsis_lanceolata_l_",
"coreopsis_major_walter",
"coreopsis_palmata_nutt_",
"coreopsis_tinctoria_nutt_",
"coreopsis_tripteris_l_",
"coreopsis_verticillata_l_",
"coriandrum_sativum_l_",
"coriaria_myrtifolia_l_",
"coris_monspeliensis_l_",
"corispermum_pallasii_steven",
"cornus_alba_l_",
"cornus_amomum_mill_",
"cornus_canadensis_l_",
"cornus_capitata_wall_",
"cornus_controversa_hemsl_",
"cornus_florida_l_",
"cornus_kousa_b_rger_ex_miq_",
"cornus_kousa_f_buerger_ex_hance",
"cornus_kousa_hance",
"cornus_mas_l_",
"cornus_racemosa_lam_",
"cornus_rugosa_lam_",
"cornus_sanguinea_l_",
"cornus_sericea_l_",
"cornus_suecica_l_",
"corokia___virgata_turrill",
"corokia_cotoneaster_raoul",
"coronilla_coronata_l_",
"coronilla_glauca_l_",
"coronilla_juncea_l_",
"coronilla_minima_l_",
"coronilla_scorpioides__l___koch",
"coronilla_scorpioides__l___w_d_j_koch",
"coronilla_valentina_l_",
"coronilla_varia_l_",
"coronilla_viminalis_salisb_",
"coronopus_didymus__l___sm_",
"corpuscularia_lehmannii__eckl____zeyh___schwantes",
"correa_alba_andrews",
"correa_bauerlenii_f_muell_",
"corrigiola_littoralis_l_",
"cortaderia_selloana__schult____schult__f___asch____graebn_",
"cortaderia_selloana__schult____schult_f___asch____graebn_",
"corydalis_aurea_willd_",
"corydalis_cava__l___schweigg____k_rte",
"corydalis_flavula__raf___dc_",
"corydalis_sempervirens__l___pers_",
"corydalis_solida__l___clairv_",
"corylopsis_pauciflora_siebold___zucc_",
"corylopsis_sinensis_hemsl_",
"corylus_avellana_l_",
"corylus_colurna_l_",
"corylus_cornuta_marshall",
"corylus_maxima_mill_",
"corymbia_citriodora__hook___k_d_hill___l_a_s_johnson",
"corymbia_ficifolia__f_muell___k_d_hill___l_a_s_johnson",
"corynephorus_canescens__l___p_beauv_",
"corynocarpus_laevigatus_j_r_forst____g_forst_",
"corypha_umbraculifera_l_",
"coryphantha_cornifera__dc___lem_",
"coryphantha_elephantidens__lem___lem_",
"coryphantha_ramillosa_cutak",
"cosmos_atrosanguineus__hook___voss",
"cosmos_bipinnatus_cav_",
"cosmos_caudatus_kunth",
"cosmos_sulphureus_cav_",
"cossinia_pinnata_comm__ex_lam_",
"costus_afer_ker_gawl_",
"costus_arabicus_l_",
"costus_barbatus_suess_",
"costus_guanaiensis_rusby",
"costus_malortieanus_h_wendl_",
"costus_spicatus__jacq___sw_",
"costus_spiralis__jacq___roscoe",
"costus_woodsonii_maas",
"cota_altissima__l___j_gay_ex_guss_",
"cota_tinctoria__l___j_gay",
"cota_tinctoria__l___j_gay_ex_guss_",
"cotinus_coggygria_scop_",
"cotinus_obovatus_raf_",
"cotoneaster_acutifolius_turcz_",
"cotoneaster_bullatus_bois",
"cotoneaster_cochleatus__franch___g_klotz",
"cotoneaster_coriaceus_franch_",
"cotoneaster_dammeri_c_k_schneid_",
"cotoneaster_divaricatus_rehder___e_h_wilson",
"cotoneaster_franchetii_bois",
"cotoneaster_frigidus_wall__ex_lindl_",
"cotoneaster_horizontalis_decne_",
"cotoneaster_integerrimus_medik_",
"cotoneaster_integrifolius__roxb___g_klotz",
"cotoneaster_lacteus_w_w_sm_",
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"dichondra_occidentalis_house",
"dichondra_repens_j_r__forst____g__forst_",
"dichorisandra_thyrsiflora_j_c_mikan",
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"dieffenbachia_nitidipetiolata_croat___grayum",
"dieffenbachia_oerstedii_schott",
"dieffenbachia_parlatorei_linden___andr_",
"dieffenbachia_seguine__jacq___schott",
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"dierama_pulcherrimum__hook_f___baker",
"diervilla_lonicera_mill_",
"diervilla_sessilifolia_buckley",
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"digera_muricata__l___mart_",
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"digitalis_lutea_l_",
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"digitalis_purpurea_l_",
"digitalis_thapsi_l_",
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"dillenia_indica_l_",
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"dillwynia_retorta__wendl___druce",
"dimerandra_emarginata__g_mey___hoehne",
"dimocarpus_longan_lour_",
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"dioclea_malacocarpa_ducke",
"diodia_virginiana_l_",
"dionaea_muscipula_ellis",
"dionaea_muscipula_j_ellis",
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"dioon_spinulosum_dyer_ex_eichl_",
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"dioscorea_esculenta__lour___burkill",
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"dioscorea_oppositifolia_l_",
"dioscorea_villosa_l_",
"diosma_hirsuta_l_",
"diospyros_digyna_jacq_",
"diospyros_fasciculosa__f_muell___f_muell_",
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"diospyros_olen_hiern",
"diospyros_texana_scheele",
"diospyros_vieillardii__hiern__kosterm_",
"diospyros_virginiana_l_",
"diospyros_whyteana__hiern__p_white",
"dipcadi_serotinum__l___medik_",
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"diphylleia_cymosa_michx_",
"diplacus_aurantiacus__curtis__jeps_",
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"diplazium_esculentum__retz___sw_",
"diplocyclos_palmatus__l___c_jeffrey",
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"dipsacus_fullonum_l_",
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"dirca_palustris_l_",
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"dissotis_rotundifolia__sm___triana",
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"dittrichia_graveolens__l___greuter",
"dittrichia_viscosa__l___greuter",
"dodecatheon_meadia_l_",
"dodonaea_viscosa_jacq_",
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"doellingeria_umbellata__mill___nees",
"dolichandra_unguis_cati__l___l_g_lohmann",
"dombeya_acutangula_cav_",
"dombeya_burgessiae_gerrard_ex_harv_",
"dombeya_ciliata_cordem_",
"dombeya_ficulnea_baill_",
"dombeya_pilosa_cordem_",
"dombeya_punctata_cav_",
"dombeya_reclinata_cordem_",
"dombeya_rotundifolia__hochst___planch_",
"dombeya_wallichii__lindl___baill_",
"dombeya_wallichii__lindl___k_schum_",
"doratoxylon_apetalum__poir___radlk_",
"doronicum_austriacum_jacq_",
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"doronicum_columnae_ten_",
"doronicum_grandiflorum_lam_",
"doronicum_orientale_hoffm_",
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"dorstenia_elata_gardner",
"doryanthes_palmeri_w_hill_ex_benth_",
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"draba_aizoides_l_",
"draba_muralis_l_",
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"dracaena_angustifolia__medik___roxb_",
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"dracaena_concinna_kunth",
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"dracaena_fragrans_ker_gawl_",
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"dracaena_marginata_lam_",
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"dracaena_surculosa_lindl_",
"dracocephalum_moldavica_l_",
"dracocephalum_ruyschiana_l_",
"dracunculus_vulgaris_schott",
"drimia_maritima__l___stearn",
"drimia_pancration__steinh___j_c_manning___goldblatt",
"drimiopsis_maculata_lindl____paxton",
"drimys_winteri_j_r_forst____g_forst_",
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"drosera_aliciae_raym__hamet",
"drosera_anglica_huds_",
"drosera_brevifolia_pursh",
"drosera_capensis_l_",
"drosera_capillaris_poir_",
"drosera_filiformis_raf_",
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"drosera_longifolia_l_",
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"drosophyllum_lusitanicum__l___link",
"dryas_octopetala_l_",
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"drymocallis rupestris (l.) soják_7423",
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"dryopteris_aemula__aiton__kuntze",
"dryopteris_affinis__lowe__fraser_jenk_",
"dryopteris_carthusiana__vill___h_p_fuchs",
"dryopteris_carthusiana__vill___h_p__fuchs",
"dryopteris_cristata__l___a_gray",
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"dryopteris_cycadina__franch____sav___c__chr_",
"dryopteris_dilatata__hoffm___a_gray",
"dryopteris_dilatata__hoffm___a__gray",
"dryopteris_erythrosora__d_c__eaton__kuntze",
"dryopteris_erythrosora__eaton__kuntze",
"dryopteris_expansa__c_presl__fraser_jenk____jermy",
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"dryopteris_intermedia__muhl__ex_willd___a__gray",
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"dryopteris_wallichiana__spreng___hyl_",
"drypetes_variabilis_uittien",
"duabanga_grandiflora__dc___walp_",
"duchesnea_indica__andrews__teschem_",
"duchesnea_indica__jacks___focke",
"dudleya_abramsii_rose",
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"dulichium_arundinaceum__l___britton",
"duranta_erecta_l_",
"durio_zibethinus_l_",
"duroia_aquatica__aubl___bremek_",
"dyckia_brevifolia_baker",
"dyckia_encholirioides__gaudich___mez",
"dypsis_decaryi__jum___beentje___j_dransf_",
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"dysphania_ambrosioides__l___mosyakin___clemants",
"dysphania_botrys__l___mosyakin___clemants",
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"dysphania_schraderiana__schult___mosyakin___clemants",
"ebenus_pinnata_aiton",
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"ecclinusa_ramiflora_mart_",
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"echeveria_affinis_e_walther",
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"echeveria_carnicolor__baker__e_morren",
"echeveria_colorata_e_walther",
"echeveria_derenbergii_j_a_purpus",
"echeveria_elegans_rose",
"echeveria_gibbiflora_dc_",
"echeveria_gigantea_rose___purpus",
"echeveria_harmsii_j_f_macbr_",
"echeveria_laui_moran___j_meyr_n",
"echeveria_lilacina_kimnach___moran",
"echeveria_nodulosa__baker__otto",
"echeveria_pallida_e_walther",
"echeveria_prolifica_moran___j_meyran",
"echeveria_pulidonis_e_walther",
"echeveria_pulvinata_rose",
"echeveria_purpusorum__rose__a_berger",
"echeveria_runyonii_rose",
"echeveria_secunda_booth_ex_lindl_",
"echeveria_setosa_rose___purpus",
"echeveria_shaviana_e_walther",
"echeveria_spp_",
"echinacea_angustifolia_dc_",
"echinacea_pallida__nutt___nutt_",
"echinacea_purpurea__l___moench",
"echinacea_tennesseensis__beadle__small",
"echinaria_capitata__l___desf_",
"echinocactus_grusonii_hildm_",
"echinocactus_horizonthalonius_lem_",
"echinocactus_texensis_hopffer",
"echinocereus_coccineus_engelm_",
"echinocereus_dasyacanthus_engelm_",
"echinocereus_engelmannii__parry_ex_engelm___lem_",
"echinocereus_enneacanthus_engelm_",
"echinocereus_pectinatus__scheidw___engelm_",
"echinocereus_pentalophus__dc___lem_",
"echinocereus_poselgeri_lem_",
"echinocereus_rigidissimus__engelm___f_haage",
"echinocereus_stramineus__engelm___f_seitz",
"echinocereus_triglochidiatus_engelm_",
"echinocereus_viereckii_werderm_",
"echinocereus_viridiflorus_engelm_",
"echinochloa_colona__l___link",
"echinochloa_crus_galli__l___p_beauv_",
"echinochloa_crus_galli__l___p__beauv_",
"echinocystis_lobata__michx___torr____a_gray",
"echinocystis_lobata__michx___torr____a__gray",
"echinodorus_cordifolius__l___griseb_",
"echinodorus_grandiflorus__cham____schltdl___micheli",
"echinophora_spinosa_l_",
"echinops_bannaticus_rochel_ex_schrad_",
"echinops_exaltatus_schrad_",
"echinops_ritro_l_",
"echinops_sphaerocephalus_l_",
"echinops_spinosissimus_turra",
"echinopsis_angelesiae__r_kiesling__g_d_rowley",
"echinopsis_atacamensis__phil___friedrich___g_d_rowley",
"echinopsis_bridgesii_salm_dyck",
"echinopsis_candicans__gillies_ex_salm_dyck__d_r_hunt",
"echinopsis_chamaecereus_h_friedrich___glaetzle",
"echinopsis_eyriesii__turpin__pfeiff____otto",
"echinopsis_huascha__web___friedrich___g_d_rowley",
"echinopsis_oxygona__link__zucc__ex_pfeiff____otto",
"echinopsis_pachanoi__britton___rose__friedrich___g_d_rowley",
"echinopsis_schickendantzii_f_a_c_weber",
"echinopsis_spachiana__lem___friedrich___g_d_rowley",
"echinopsis_subdenudata_c_rdenas",
"echium_angustifolium_mill_",
"echium_arenarium_guss_",
"echium_asperrimum_lam_",
"echium_candicans_l_f_",
"echium_creticum_l_",
"echium_italicum_l_",
"echium_pininana",
"echium_pininana_webb___berthel_",
"echium_plantagineum_l_",
"echium_sabulicola_pomel",
"echium_strictum_l_f_",
"echium_virescens_dc_",
"echium_vulgare_l_",
"echium_wildpretii_h_pearson_ex_hook_f_",
"echium_wildpretii_pearson_ex_hook_f_",
"eclipta_prostrata__l___l_",
"edgeworthia_chrysantha_lindl_",
"edgeworthia_tomentosa__thunb___nakai",
"edithcolea_grandis_n_e_br_",
"egeria_densa_planch_",
"ehretia_acuminata_r_br_",
"ehretia_amoena_klotzsch",
"ehretia_dicksonii_hance",
"ehretia_latifolia_loisel__ex_a_dc_",
"ehretia_microphylla_lam_",
"ehretia_rigida__thunb___druce",
"ehrharta_erecta_lam_",
"eichhornia_azurea__sw___kunth",
"eichhornia_crassipes__mart___solms",
"ekebergia_capensis_sparrm_",
"elaeagnus_angustifolia_l_",
"elaeagnus_commutata_bernh__ex_rydb_",
"elaeagnus_macrophylla_thunb_",
"elaeagnus_multiflora_thunb_",
"elaeagnus_pungens_thunb_",
"elaeagnus_rhamnoides__l___a_nelson",
"elaeagnus_umbellata_thunb_",
"elaeagnus_x_ebbingei_door_",
"elaeis_guineensis_jacq_",
"elaeocarpus_angustifolius_blume",
"elaeocarpus_reticulatus_sm_",
"elatine_hexandra__lapierre__dc_",
"elatine_macropoda_guss_",
"elatine_triandra_schkuhr",
"elattostachys_apetala__labill___radlk_",
"eleocharis_obtusa__willd___schult_",
"eleocharis_palustris__l___roem____schult_",
"eleocharis_parvula__roem____schult___link_ex_bluff__nees___schauer",
"elephantopus_elatus_bertol_",
"elephantopus_mollis_kunth",
"elettaria_cardamomum__l___maton",
"eleusine_coracana__l___gaertn_",
"eleusine_indica__l___gaertn_",
"eleusine_tristachya__lam___lam_",
"elodea_canadensis_michx_",
"elodea_nuttallii__planch___h_st_john",
"elymus_canadensis_l_",
"elymus_caninus__l___l_",
"elymus_virginicus_l_",
"elytrigia_campestris__godr____gren___kergu_len_ex_carreras",
"elytrigia_juncea__l___nevski",
"elytrigia_repens__l___desv__ex_nevski",
"embothrium_coccineum_j_r_forst____g_forst_",
"emex_spinosa__l___campd_",
"emilia_fosbergii_nicolson",
"emilia_sonchifolia__l___dc_",
"emilia_sonchifolia__l___dc__ex_dc_",
"empetrum_nigrum_l_",
"encelia_californica_nutt_",
"encelia_farinosa_a__gray_ex_torr_",
"encephalartos_altensteinii_lehm_",
"encephalartos_ferox_g_bertol_",
"encephalartos_horridus__jacq___lehm_",
"encephalartos_kisambo_faden___beentje",
"encephalartos_laurentianus_de_wild_",
"encephalartos_lehmannii_lehm_",
"encephalartos_natalensis_r_a_dyer___verdoorn",
"enchylaena_tomentosa_r_br_",
"encyclia_tampensis__lindl___small",
"englerophytum_magalismontanum__sond___t_d_penn_",
"enkianthus_campanulatus__miq___g_nicholson",
"ensete_lasiocarpum__franch___cheesman",
"ensete_ventricosum__welw___cheesman",
"entada_gigas__l___fawc____rendle",
"entada_phaseoloides__l___merr_",
"enterolobium_contortisiliquum__vell___morong",
"enterolobium_cyclocarpum__jacq___griseb_",
"eperua_falcata_aubl_",
"eperua_grandiflora__aubl___baill_",
"ephedra_altissima_desf_",
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"ephedra_major_host",
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"epidendrum_atacazoicum_schltr_",
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"epidendrum_ciliare_l_",
"epidendrum_ibaguense_kunth",
"epidendrum_nocturnum_jacq_",
"epidendrum_porphyreum_lindl_",
"epidendrum_radicans_pav__ex_lindl_",
"epidendrum_rigidum_jacq_",
"epidendrum_stamfordianum_bateman",
"epifagus_virginiana__l___w_p_c__barton",
"epigaea_repens_l_",
"epilobium_alpestre__jacq___krock_",
"epilobium_alsinifolium_vill_",
"epilobium_anagallidifolium_lam_",
"epilobium_angustifolium_l_",
"epilobium_brachycarpum_c_presl",
"epilobium_canum__greene__p_h__raven",
"epilobium_ciliatum_raf_",
"epilobium_collinum_c_c_gmel_",
"epilobium_dodonaei_vill_",
"epilobium_hirsutum_l_",
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"epilobium_montanum_l_",
"epilobium_palustre_l_",
"epilobium_parviflorum_schreb_",
"epilobium_roseum_schreb_",
"epilobium_tetragonum_l_",
"epimedium_alpinum_l_",
"epipactis_atrorubens__hoffm___besser",
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"epipactis_helleborine__l___crantz",
"epipactis_microphylla__ehrh___sw_",
"epipactis_muelleri_godfery",
"epipactis_palustris__l___crantz",
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"epiphyllum_hookeri_haw_",
"epiphyllum_oxypetalum__dc___haw_",
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"epipogium_aphyllum_sw_",
"epipremnum_aureum__linden___andr___g_s_bunting",
"epipremnum_pinnatum__l___engl_",
"episcia_cupreata__hook___hanst_",
"episcia_dianthiflora_h_e_moore___r_g_wilson",
"episcia_lilacina_hanst_",
"equisetum_arvense_l_",
"equisetum_fluviatile_l_",
"equisetum_giganteum_l_",
"equisetum_hyemale_l_",
"equisetum_palustre_l_",
"equisetum_pratense_ehrh_",
"equisetum_ramosissimum_desf_",
"equisetum_scirpoides_michx_",
"equisetum_sylvaticum_l_",
"equisetum_telmateia_ehrh_",
"equisetum_variegatum_schleich_",
"equisetum_variegatum_schleich__ex_f__weber___d__mohr",
"eragrostis_barrelieri_daveau",
"eragrostis_cilianensis__all___janch_",
"eragrostis_cilianensis__all___vignolo_ex_janch_",
"eragrostis_curvula__schrad___nees",
"eragrostis_mexicana__hornem___link",
"eragrostis_minor_host",
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"eragrostis_pilosa__l___p_beauv_",
"eragrostis_racemosa__thunb___steud_",
"eragrostis_spectabilis__pursh__steud_",
"eragrostis_superba_peyr_",
"eragrostis_unioloides__retz___nees_ex_steud_",
"eranthemum_pulchellum_andrews",
"eranthis_hyemalis__l___salisb_",
"erechtites_hieraciifolius__l___raf__ex_dc_",
"erechtites_minima__poir___dc_",
"erechtites_valerianifolius__wolf__dc_",
"eremophila_nivea_chinnock",
"eremurus_stenophyllus__boiss____buhse__baker",
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"hebe_andersonii__lindl____j__paxton__cockayne",
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"hedysarum_boveanum_bunge_ex_basiner",
"hedysarum_coronarium_l_",
"hedysarum_hedysaroides__l___schinz___thell_",
"hedysarum_spinosissimum_l_",
"helenium_amarum__raf___h__rock",
"helenium_autumnale_l_",
"helenium_flexuosum_raf_",
"helenium_puberulum_dc_",
"helianthemum_apenninum__l___mill_",
"helianthemum_canum__l___baumg_",
"helianthemum_hirtum__l___mill_",
"helianthemum_ledifolium__l___mill_",
"helianthemum_lippii__l___dum_cours_",
"helianthemum_marifolium_mill_",
"helianthemum_marifolium__l___mill_",
"helianthemum_nummularium__l___mill_",
"helianthemum_oelandicum__l___dc_",
"helianthemum_oelandicum__l___dum_cours_",
"helianthemum_salicifolium__l___mill_",
"helianthemum_syriacum__jacq___dum_cours_",
"helianthemum_violaceum__cav___pers_",
"helianthus___laetiflorus_pers_",
"helianthus_angustifolius_l_",
"helianthus_annuus_l_",
"helianthus_debilis_nutt_",
"helianthus_decapetalus_l_",
"helianthus_divaricatus_l_",
"helianthus_giganteus_l_",
"helianthus_hirsutus_raf_",
"helianthus_maximiliani_schrad_",
"helianthus_mollis_lam_",
"helianthus_niveus__benth___brandegee",
"helianthus_pauciflorus_nutt_",
"helianthus_petiolaris_nutt_",
"helianthus_salicifolius_a_dietr_",
"helianthus_strumosus_l_",
"helianthus_tuberosus_l_",
"helianthus_x_laetiflorus_pers_",
"helichrysum_ambiguum__pers___c_presl",
"helichrysum_arenarium__l___moench",
"helichrysum_bracteatum__venten___willd_",
"helichrysum_foetidum__l___moench",
"helichrysum_fontanesii_cambess_",
"helichrysum_glumaceum_dc_",
"helichrysum_italicum__roth__g_don",
"helichrysum_odoratissimum__l___sweet",
"helichrysum_pendulum__c_presl__c_presl",
"helichrysum_petiolare_hilliard___b_l_burtt",
"helichrysum_petiolare_hilliard___b_l__burtt",
"helichrysum_petiolare_hilliard___burtt",
"helichrysum_rupestre__raf___dc_",
"helichrysum_stoechas__l___moench",
"helicodiceros_muscivorus__l_f___engl_",
"heliconia_bihai__l___l_",
"heliconia_caribaea_lam_",
"heliconia_latispatha_benth_",
"heliconia_psittacorum_l_f_",
"heliconia_rostrata_ruiz___pav_",
"heliconia_stricta_huber",
"heliconia_wagneriana_petersen",
"helictotrichon_sempervirens__vill___pilg_",
"heliopsis_helianthoides__l___sweet",
"heliosperma_pusillum__waldst____kit___rchb_",
"heliotropium_amplexicaule_vahl",
"heliotropium_angiospermum_murray",
"heliotropium_arborescens_l_",
"heliotropium_curassavicum_l_",
"heliotropium_europaeum_l_",
"heliotropium_foertherianum_diane___hilger",
"heliotropium_indicum_l_",
"heliotropium_steudneri_vatke",
"heliotropium_supinum_l_",
"heliotropium_tenellum__nutt___torr_",
"helleborus_argutifolius_viv_",
"helleborus_foetidus_l_",
"helleborus_lividus_aiton",
"helleborus_lividus_aiton_ex_curtis",
"helleborus_niger_l_",
"helleborus_orientalis_lam_",
"helleborus_viridis_l_",
"helminthotheca_echioides__l___holub",
"helonias_bullata_l_",
"helosciadium_nodiflorum__l___w_d_j_koch",
"hemerocallis_fulva__l___l_",
"hemerocallis_lilioasphodelus_l_",
"hemerocallis_minor_mill_",
"hemidesmus_indicus__l___r__br__ex_schult_",
"hemigraphis_alternata__burm__f___t__anderson",
"hemigraphis_alternata__burm_f___t_anderson",
"hemigraphis_repanda__l___hallier_f_",
"hemionitis_arifolia__burm__f___t__moore",
"hemitomes_congestum_a__gray",
"hemizonia_congesta_dc_",
"hepatica_nobilis_mill_",
"hepatica_nobilis_schreb_",
"heptacodium_miconioides_rehder",
"heracleum_lanatum_michx_",
"heracleum_mantegazzianum_sommier___levier",
"heracleum_maximum_w__bartram",
"heracleum_pyrenaicum_lam_",
"heracleum_sibiricum_l_",
"heracleum_sosnowskyi_manden_",
"heracleum_sphondylium_l_",
"herbertia_lahue__molina__goldblatt",
"heritiera_littoralis_aiton",
"hernandia_nymphaeifolia__j_presl__kubitzki",
"herniaria_glabra_l_",
"herniaria_hirsuta_l_",
"hertia_cheirifolia__l___kuntze",
"hesperaloe_parviflora__torr___j_m_coult_",
"hesperaloe_parviflora__torr___j_m__coult_",
"hesperantha_coccinea__backh____harv___goldblatt___j_c_manning",
"hesperis_matronalis_l_",
"hesperoyucca_whipplei__torr___trel_",
"heteranthera_dubia__jacq___macmill_",
"heteranthera_limosa__sw___willd_",
"heteranthera_reniformis_ruiz___pav_",
"heteranthera_zosterifolia_mart_",
"heteromorpha_arborescens__spreng___cham____schltdl_",
"heteropogon_contortus__l___p_beauv__ex_roem____schult_",
"heteropterys_brachiata__l___dc_",
"heteropyxis_natalensis_harv_",
"heterotheca_grandiflora_nutt_",
"heterotheca_subaxillaris__lam___britton___rusby",
"heterotheca_villosa__pursh__shinners",
"heterotis_rotundifolia__sm___jacq__f_l_",
"heuchera_americana_l_",
"heuchera_micrantha_douglas_ex_lindl_",
"heuchera_richardsonii_r__br_",
"heuchera_rubescens_torr_",
"heuchera_sanguinea_engelm_",
"heuchera_villosa_michx_",
"hevea_brasiliensis__willd__ex_a_juss___m_ll_arg_",
"hexalobus_monopetalus__a_rich___engl____diels",
"hexastylis_arifolia__michx___small",
"hibbertia_scandens__willd___dryand_",
"hibiscus_acetosella_welw__ex_hiern",
"hibiscus_arnottianus_a__gray",
"hibiscus_boryanus_dc_",
"hibiscus_calyphyllus_cav_",
"hibiscus_cannabinus_l_",
"hibiscus_coccineus_walter",
"hibiscus_elatus_sw_",
"hibiscus_laevis_all_",
"hibiscus_martianus_zucc_",
"hibiscus_moscheutos_l_",
"hibiscus_mutabilis_l_",
"hibiscus_palustris_l_",
"hibiscus_rosa_sinensis_l_",
"hibiscus_sabdariffa_l_",
"hibiscus_schizopetalus__dyer__hook_f_",
"hibiscus_syriacus_l_",
"hibiscus_tiliaceus_l_",
"hibiscus_tilliaceus_l_",
"hibiscus_trionum_l_",
"hieracium_albiflorum_hook_",
"hieracium_amplexicaule_l_",
"hieracium_aurantiacum_l_",
"hieracium_cantalicum_arv__touv_",
"hieracium_cerdanum_arv__touv_",
"hieracium_glaucinum_jord_",
"hieracium_intybaceum_all_",
"hieracium_jaubertianum_timb__lagr____loret",
"hieracium_lachenalii_suter",
"hieracium_maculatum_schrank",
"hieracium_murorum_c_b_clarke",
"hieracium_murorum_l_",
"hieracium_nobile_gren____godr_",
"hieracium_pilosella_l_",
"hieracium_prenanthoides_vill_",
"hieracium_sabaudum_l_",
"hieracium_tomentosum_l_",
"hieracium_umbellatum_l_",
"hieracium_venosum_l_",
"hieracium_villosum_jacq_",
"hieracium_vulgatum_fr_",
"hierochloe_odorata__l___p_beauv_",
"himantoglossum_hircinum__l___spreng_",
"himantoglossum_robertianum__loisel___p_delforge",
"himatanthus_drasticus__mart___plumel",
"hippeastrum_correiense__bury__worsley",
"hippeastrum_papilio__ravenna__van_scheepen",
"hippeastrum_puniceum__lam___kuntze",
"hippeastrum_puniceum__lam___voss",
"hippeastrum_reginae__l___herb_",
"hippeastrum_reticulatum__l_h_r___herb_",
"hippeastrum_striatum__lam___h_e_moore",
"hippeastrum_vittatum__l_h_r___herb_",
"hippobroma_longiflora__l___g_don",
"hippobroma_longiflora__l___g__don",
"hippocrepis_ciliata_willd_",
"hippocrepis_comosa_l_",
"hippocrepis_emerus__l___lassen",
"hippomane_mancinella_l_",
"hippophae_rhamnoides_l_",
"hippuris_vulgaris_l_",
"hiptage_benghalensis__l___kurz",
"hirschfeldia_incana__l___lagr__foss_",
"hirtella_bicornis_mart____zucc_",
"hoffmannseggia_glauca__ortega__eifert",
"hoheria_sexstylosa_colenso",
"holarrhena_pubescens_wall__ex_g_don",
"holcus_lanatus_l_",
"holcus_mollis_l_",
"holmskioldia_sanguinea_retz_",
"holodiscus_discolor__pursh__maxim_",
"holosteum_umbellatum_l_",
"homalanthus_populifolius_graham",
"homalium_paniculatum__lam___benth_",
"homalomena_rubescens__roxb___kunth",
"homogyne_alpina__l___cass_",
"honckenya_peploides__l___ehrh_",
"honorius_nutans__sm___gray",
"hordeum_bulbosum_l_",
"hordeum_jubatum_l_",
"hordeum_marinum_huds_",
"hordeum_murinum_l_",
"hordeum_pusillum_nutt_",
"hordeum_secalinum_schreb_",
"hordeum_vulgare_l_",
"hormathophylla_spinosa__l___p_k_pfer",
"horminum_pyrenaicum_l_",
"hornungia_alpina__l___o_appel",
"hornungia_petraea__l___rchb_",
"hornungia_procumbens__l___hayek",
"hosta_lancifolia__thunb___engl_",
"hosta_plantaginea__lam___asch_",
"hosta_sieboldiana__hook___engl_",
"hottonia_palustris_l_",
"houseplant",
"houseplant_aloe",
"houseplant_amaryllis",
"houseplant_anthurium",
"houseplant_cactaceae",
"houseplant_calathea",
"houseplant_codiaeum",
"houseplant_cyclamen",
"houseplant_eremospatha",
"houseplant_hoya",
"houseplant_kalanchoe",
"houseplant_orchidaceae",
"houseplant_pelargonium",
"houseplant_pothos",
"houseplant_sansevieria",
"houseplant_schefflera",
"houseplant_schlumbergera",
"houseplant_spathiphyllum",
"houseplant_crassulaceae",
"houstonia_caerulea_l_",
"houstonia_purpurea_l_",
"houstonia_pusilla_schoepf",
"houttuynia_cordata_thunb_",
"hovenia_dulcis_thunb_",
"howea_forsteriana__f_muell___becc_",
"hoya_australis_r_br__ex_traill",
"hoya_carnosa__l__f___r_br_",
"hoya_carnosa__l_f___r_br_",
"hoya_kerrii_craib",
"hoya_linearis_wall__ex_d_don",
"hoya_spp_",
"hubertia_tomentosa_bory",
"huernia_confusa_phillips",
"huernia_penzigii_n_e__br_",
"huernia_schneideriana_a__berger",
"humiriastrum_subcrenatum__benth___cuatrec_",
"humulus_lupulus_l_",
"huperzia_phlegmaria__l___rothm_",
"huperzia_selago__l___bernh__ex_schrank___mart_",
"huperzia_squarrosa__g__forst___trevis_",
"hura_crepitans_l_",
"hyacinthoides_hispanica__mill___rothm_",
"hyacinthoides_italica__l___rothm_",
"hyacinthoides_non_scripta__l___chouard_ex_rothm_",
"hyacinthoides_x_massartiana_geerinck",
"hyacinthus_orientalis_l_",
"hydnora_abyssinica_a_br_",
"hydrangea_arborescens_l_",
"hydrangea_aspera_d__don",
"hydrangea_macrophylla__thunb___ser_",
"hydrangea_paniculata_siebold",
"hydrangea_petiolaris_siebold___zucc_",
"hydrangea_quercifolia_w_bartram",
"hydrangea_quercifolia_w__bartram",
"hydrangea_sargentiana_rehder",
"hydrangea_spp_",
"hydrastis_canadensis_l_",
"hydrocharis_morsus_ranae_l_",
"hydrocotyle_americana_l_",
"hydrocotyle_bonariensis_comm__ex_lam_",
"hydrocotyle_leucocephala_cham____schltdl_",
"hydrocotyle_ranunculoides_l__f_",
"hydrocotyle_ranunculoides_l_f_",
"hydrocotyle_sibthorpioides_lam_",
"hydrocotyle_umbellata_l_",
"hydrocotyle_verticillata_thunb_",
"hydrocotyle_vulgaris_l_",
"hydrophyllum_appendiculatum_michx_",
"hydrophyllum_capitatum_douglas_ex_benth_",
"hydrophyllum_virginianum_l_",
"hygrophila_corymbosa__blume__lindau",
"hylocereus_megalanthus__k__schum__ex_vaupel__ralf_bauer",
"hylocereus_trigonus__haw___saff_",
"hylocereus_undatus__haw___britton___rose",
"hylotelephium_anacampseros__l___h_ohba",
"hylotelephium_maximum__l___holub",
"hylotelephium_sieboldii__regel__ohba",
"hylotelephium_spectabile__boreau__h_ohba",
"hylotelephium_spectabile__boreau__h__ohba",
"hylotelephium_telephioides__michx___h__ohba",
"hylotelephium_telephium__l___h_ohba",
"hylotelephium_telephium__l___h__ohba",
"hymenaea_courbaril_l_",
"hymenocallis_liriosme__raf___shinners",
"hymenocallis_littoralis__jacq___salisb_",
"hymenolobium_flavum_kleinhoonte",
"hymenophyllum_tunbrigense__l___sm_",
"hyophorbe_indica_gaertn_",
"hyophorbe_lagenicaulis__l_h_bailey__h_e_moore",
"hyophorbe_verschaffeltii_h_wendl_",
"hyoscyamus_albus_l_",
"hyoscyamus_niger_l_",
"hyoseris_radiata_l_",
"hyospathe_elegans_mart_",
"hyparrhenia_hirta__l___stapf",
"hypecoum_imberbe_sm_",
"hypecoum_procumbens_l_",
"hypericum_androsaemum_l_",
"hypericum_annulatum_moris",
"hypericum_balearicum_l_",
"hypericum_calycinum_l_",
"hypericum_canariense_l_",
"hypericum_elodes_l_",
"hypericum_frondosum_michx_",
"hypericum_hircinum_l_",
"hypericum_hirsutum_l_",
"hypericum_humifusum_l_",
"hypericum_hypericoides__l___crantz",
"hypericum_kalmianum_l_",
"hypericum_lanceolatum_lam_",
"hypericum_linariifolium_vahl",
"hypericum_maculatum_crantz",
"hypericum_montanum_l_",
"hypericum_mutilum_l_",
"hypericum_nummularium_l_",
"hypericum_olympicum_l_",
"hypericum_patulum_thunb_",
"hypericum_perfoliatum_l_",
"hypericum_perforatum_l_",
"hypericum_prolificum_l_",
"hypericum_pulchrum_l_",
"hypericum_punctatum_lam_",
"hypericum_richeri_vill_",
"hypericum_tetrapterum_fr_",
"hypericum_tomentosum_l_",
"hypericum_triquetrifolium_turra",
"hypericum_x_hidcoteense_hilling_ex_geerinck",
"hypericum_x_inodorum_mill_",
"hyphaene_coriacea_gaertn_",
"hypnum_cupressiforme_hedw_",
"hypochaeris_achyrophorus_l_",
"hypochaeris_glabra_l_",
"hypochaeris_maculata_l_",
"hypochaeris_radicata_l_",
"hypochaeris_uniflora_vill_",
"hypoestes_aristata__vahl__roem____schult_",
"hypoestes_phyllostachya_baker",
"hypoestes_sanguinolenta__van_houtte__hook__f_",
"hypoxis_hirsuta__l___coville",
"hyptis_capitata_jacq_",
"hyptis_emoryi_torr_",
"hyptis_suaveolens__l___poit_",
"hyssopus_officinalis_l_",
"iberis_amara_l_",
"iberis_ciliata_all_",
"iberis_linifolia_l_",
"iberis_pinnata_l_",
"iberis_saxatilis_l_",
"iberis_semperflorens_l_",
"iberis_sempervirens_l_",
"iberis_umbellata_l_",
"ibicella_lutea__lindl___van_eselt_",
"idesia_polycarpa_maxim_",
"ilex_aquifolium_l_",
"ilex_canariensis_poir_",
"ilex_cassine_l_",
"ilex_cornuta_lindl____paxton",
"ilex_crenata_thunb_",
"ilex_decidua_walter",
"ilex_glabra__l___a__gray",
"ilex_mitis__l___radlk_",
"ilex_opaca_aiton",
"ilex_rotunda_thunb_",
"ilex_verticillata__l___a__gray",
"ilex_vomitoria_aiton",
"illecebrum_verticillatum_l_",
"illicium_floridanum_j__ellis",
"illicium_verum_hook_f_",
"impatiens_auricoma_baill_",
"impatiens_balfouri_hook_f_",
"impatiens_balfourii_hook_f_",
"impatiens_balsamina_l_",
"impatiens_capensis_meerb_",
"impatiens_flaccida_arn_",
"impatiens_glandulifera_royle",
"impatiens_hawkeri_w_bull",
"impatiens_niamniamensis_gilg",
"impatiens_noli_tangere_l_",
"impatiens_pallida_nutt_",
"impatiens_parviflora_dc_",
"impatiens_sodenii_engl____warb_",
"impatiens_walleriana_hook__f_",
"impatiens_walleriana_hook_f_",
"imperata_cylindrica__l___r_usch_",
"imperata_cylindrica__l___raeusch_",
"imperatoria_ostruthium_l_",
"incarvillea_delavayi_bureau___franch_",
"indigofera_ammoxylum__dc___polhill",
"indigofera_binderi_kotschy",
"indigofera_brevicalyx_baker_f_",
"indigofera_decora_lindl_",
"indigofera_heterantha_brandis",
"indigofera_heterantha_wall__ex_brandis",
"indigofera_hirsuta_l_",
"indigofera_miniata_ortega",
"indigofera_spicata_forssk_",
"indigofera_tinctoria_l_",
"indigofera_volkensii_taub_",
"inga_alba__sw___willd_",
"inga_edulis_mart_",
"inga_laurina__sw___willd_",
"inga_melinonis_sagot",
"inga_vera_willd_",
"inula_britannica_l_",
"inula_conyza_dc_",
"inula_conyza__griess___dc_",
"inula_ensifolia_l_",
"inula_helenium_l_",
"inula_hirta_l_",
"inula_montana_l_",
"inula_salicina_l_",
"inula_spiraeifolia_l_",
"inula_verbascifolia__willd___hausskn_",
"iochroma_australe_griseb_",
"iochroma_cyaneum__lindl___g_h_m__lawr____j_m__tucker",
"ionopsis_utricularioides__sw___lindl_",
"ipomea_spp_",
"ipomoea_alba_l_",
"ipomoea_aquatica_forssk_",
"ipomoea_asarifolia__desr___roem____schult_",
"ipomoea_batatas__l___lam_",
"ipomoea_blepharophylla_hallier_f_",
"ipomoea_cairica__l___sweet",
"ipomoea_carnea_jacq_",
"ipomoea_cordatotriloba_dennst_",
"ipomoea_hederacea_jacq_",
"ipomoea_hederifolia_l_",
"ipomoea_imperati__vahl__griseb_",
"ipomoea_indica__burm___merr_",
"ipomoea_indica__burm__f___merr_",
"ipomoea_lacunosa_l_",
"ipomoea_lobata__cerv___thell_",
"ipomoea_nil__l___roth",
"ipomoea_obscura__l___ker_gawl_",
"ipomoea_pandurata__l___g__mey_",
"ipomoea_pes_caprae__l___r_br_",
"ipomoea_pes_caprae__l___r__br_",
"ipomoea_pes_tigridis_l_",
"ipomoea_purpurea__l___roth",
"ipomoea_quamoclit_l_",
"ipomoea_sagittata_poir_",
"ipomoea_tiliacea__willd___choisy",
"ipomoea_tricolor_cav_",
"ipomoea_triloba_l_",
"ipomoea_wrightii_a__gray",
"ipomopsis_aggregata__pursh__v_e__grant",
"ipomopsis_rubra__l___wherry",
"iresine_diffusa_humb____bonpl__ex_willd_",
"iresine_herbstii_hook_",
"iresine_herbstii_hook__ex_lindl_",
"iris___germanica_l_",
"iris_albicans_lange",
"iris_barbatula_noltie___k_y_guan",
"iris_chrysographes_dykes",
"iris_cristata_aiton",
"iris_domestica__l___goldblatt___mabb_",
"iris_douglasiana_herb_",
"iris_ensata_thunb_",
"iris_foetidissima_l_",
"iris_germanica_l_",
"iris_graminea_l_",
"iris_japonica_thunb_",
"iris_latifolia__mill___voss",
"iris_lutescens_lam_",
"iris_orientalis_mill_",
"iris_pallida_lam_",
"iris_perrieri_simonet_ex_n_service",
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"malva_alcea_l_",
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"malva_canariensis_m_f_ray",
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"nerine_bowdenii_w_watson",
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"nipponanthemum_nipponicum__franch__ex_maxim___kitam_",
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"nolina_recurvata__lem___hemsl_",
"nolina_texana_s_watson",
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"nonliving_flower",
"nonliving_fruit",
"nonliving_object",
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"nonliving_scene",
"nopalea_cochenillifera__l___salm_dyck",
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"nothofagus_alpina__poepp____endl___oerst_",
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"ovni",
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"omphalodes_linifolia__l___moench",
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"oncidium_spp_",
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"onychium_japonicum__thunb___kunze",
"ophioglossum_californicum_prantl",
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"ophiopogon_planiscapus_nakai",
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"petasites_frigidus__l___fr_",
"petasites_hybridus__l___g_gaertn___b_mey____scherb_",
"petasites_hybridus__l___p_gaertn___b_mey____scherb_",
"petasites_hybridus__l____g_gaertn___b_mey____scherb__",
"petasites_japonicus__siebold___zucc___maxim_",
"petasites_paradoxus__retz___baumg_",
"petasites_pyrenaicus__l___g_l_pez",
"petiveria_alliacea_l_",
"petrea_volubilis_l_",
"petrocallis_pyrenaica__l___r_br_",
"petrorhagia_nanteuilii__burnat__p_w_ball___heywood",
"petrorhagia_prolifera__l___p_w_ball___heywood",
"petrorhagia_saxifraga__l___link",
"petroselinum_crispum__mill___fuss",
"petroselinum_crispum__mill___nyman_ex_a_w_hill",
"petroselinum_crispum__mill___nyman_ex_a_w__hill",
"petunia___atkinsiana_d__don_ex_w_h__baxter",
"petunia_axillaris__lam___britton__sterns___poggenb_",
"petunia_hybrida_vilm_",
"petunia_spp_",
"petunia_violacea_lindl_",
"petunia_x_atkinsiana_d_don",
"petunia_x_hybrida__hook_f___vilm_",
"peucedanum_gallicum_latourr_",
"peucedanum_officinale_l_",
"peucedanum_ostruthium__l___w_d_j_koch",
"peumus_boldus_molina",
"phacelia_bipinnatifida_michx_",
"phacelia_californica_cham_",
"phacelia_campanularia_a_gray",
"phacelia_campanularia_a__gray",
"phacelia_cicutaria_greene",
"phacelia_congesta_hook_",
"phacelia_crenulata_torr__ex_s__watson",
"phacelia_distans_benth_",
"phacelia_hastata_douglas_ex_lehm_",
"phacelia_purshii_buckley",
"phacelia_ramosissima_douglas_ex_lehm_",
"phacelia_tanacetifolia_benth_",
"phagnalon_rupestre__l___dc_",
"phagnalon_saxatile__l___cass_",
"phagnalon_sordidum__l___rchb_",
"phaius_tankervilleae__banks__blume",
"phalaenopsis___singuliflora_j_j_sm_",
"phalaenopsis_amabilis__l___blume",
"phalaenopsis_spp_",
"phalaris_aquatica_l_",
"phalaris_arundinacea_l_",
"phalaris_canariensis_l_",
"phalaris_coerulescens_desf_",
"phalaris_minor_retz_",
"phalaris_paradoxa_l_",
"phaleria_macrocarpa__scheff___boerl_",
"phaseolus_coccineus_l_",
"phaseolus_lunatus_l_",
"phaseolus_vulgaris_l_",
"phedimus_aizoon__l____t_hart",
"phedimus_hybridus__l____t_hart",
"phedimus_spurius__m_bieb___t_hart",
"phedimus_spurius__m__bieb____t_hart",
"phegopteris_connectilis__michx___watt",
"phelipanche_nana__reut___soj_k",
"phelipanche_purpurea__jacq___soj_k",
"phelipanche_ramosa__l___pomel",
"phellodendron_amurense_rupr_",
"philadelphus_coronarius_l_",
"philadelphus_inodorus_l_",
"philadelphus_lewisii_pursh",
"philenoptera_violacea__klotzsch__schrire",
"phillyrea_angustifolia_l_",
"phillyrea_latifolia_l_",
"phillyrea_media_l_",
"philodendron_bipennifolium_schott",
"philodendron_bipinnatifidum_schott_ex_endl_",
"philodendron_burle_marxii_g_m_barroso",
"philodendron_cordatum_kunth_ex_schott",
"philodendron_davidsonii_croat",
"philodendron_elegans_k_krause",
"philodendron_erubescens_k_koch___augustin",
"philodendron_giganteum_schott",
"philodendron_gloriosum_andr_",
"philodendron_goeldii_g_m_barroso",
"philodendron_hastatum_k_koch___sello",
"philodendron_hederaceum__jacq___schott",
"philodendron_hederaceum__var_kirkbridei_schott",
"philodendron_martianum_engl_",
"philodendron_melanochrysum_linden___andr_",
"philodendron_melinonii_brongn__ex_regel",
"philodendron_ornatum_schott",
"philodendron_panduriforme__kunth__kunth",
"philodendron_radiatum_schott",
"philodendron_spp_",
"philodendron_squamiferum_poepp_",
"philodendron_tripartitum__jacq___schott",
"philodendron_verrucosum_l_mathieu_ex_schott",
"philodendron_wendlandii_schott",
"philodendron_xanadu_croat__mayo___j_boos",
"phlebodium_aureum__l___j_sm_",
"phlebodium_aureum__l___j__sm_",
"phlebodium_aureum__l___sm_",
"phlebodium_pseudoaureum__cav___lellinger",
"phleum_alpinum_l_",
"phleum_arenarium_l_",
"phleum_phleoides__l___h_karst_",
"phleum_pratense_l_",
"phlomis_fruticosa_l_",
"phlomis_herba_venti_l_",
"phlomis_lychnitis_l_",
"phlomis_purpurea_l_",
"phlomis_russeliana_lag__ex_benth_",
"phlomis_russeliana__sims__lag__ex_benth_",
"phlomis_tuberosa_l_",
"phlox_carolina_l_",
"phlox_diffusa_benth_",
"phlox_divaricata_l_",
"phlox_drummondii",
"phlox_drummondii_hook_",
"phlox_hoodii_richardson",
"phlox_maculata_l_",
"phlox_paniculata_l_",
"phlox_pilosa_l_",
"phlox_speciosa_pursh",
"phlox_stolonifera_sims",
"phlox_subulata_l_",
"phoenix_canariensis_chabaud",
"phoenix_canariensis_hort__ex_chabaud",
"phoenix_dactylifera_l_",
"phoenix_reclinata_jacq_",
"phoenix_roebelenii_o_brien",
"pholidota_pallida_lindl_",
"pholisma_arenarium_nutt__ex_hook_",
"pholisma_sonorae__torr__ex_a__gray__yatsk_",
"phoradendron_californicum_nutt_",
"phormium_tenax_j_r_forst____g_forst_",
"photinia_glabra__thunb___maxim_",
"photinia_serratifolia__desf___kalkm_",
"photinia_serratifolia__desf___kalkman",
"photinia_serrulata_lindl_",
"photinia_villosa__thunb___dc_",
"phragmites_australis__cav___trin__ex_steud_",
"phragmites_mauritianus_kunth",
"phryma_leptostachya_l_",
"phrynium_pubinerve_blume",
"phuopsis_stylosa__trin___benth____hook_f_",
"phuopsis_stylosa__trin___hook_f__ex_b_d_jacks_",
"phygelius_aequalis_harv__ex_hiern",
"phygelius_capensis_e__mey__ex_benth_",
"phyla_lanceolata__michx___greene",
"phyla_nodiflora__l___greene",
"phyla_scaberrima__juss__ex_pers___moldenke",
"phylica_nitida_lam_",
"phyllanthus_acidus__l___skeels",
"phyllanthus_amarus_schumach____thonn_",
"phyllanthus_billardierei__baill___m_ll_arg_",
"phyllanthus_bourgeoisii_baill_",
"phyllanthus_emblica_l_",
"phyllanthus_epiphyllanthus_l_",
"phyllanthus_fischeri_pax",
"phyllanthus_niruri_l_",
"phyllanthus_niruroides_m_ll_arg_",
"phyllanthus_phillyreifolius_poir_",
"phyllanthus_polygonoides_nutt__ex_spreng_",
"phyllanthus_reticulatus_poir_",
"phyllanthus_tenellus_roxb_",
"phyllanthus_urinaria_l_",
"phyllodoce_caerulea__l___bab_",
"phyllodoce_empetriformis__sm___d__don",
"phyllospadix_scouleri_hook_",
"phyllostachys_aurea_carri_re_ex_a__rivi_re___c__rivi_re",
"phyllostachys_aurea_carri_re_ex_rivi_re___c_rivi_re",
"phyllostachys_aurea_rivi_re___c_rivi_re",
"phyllostachys_aureosulcata_mcclure",
"phyllostachys_bambusoides_siebold___zucc_",
"phyllostachys_nigra__lodd__ex_lindl___munro",
"phyllostachys_viridis__r_a_young__mcclure",
"phymatosorus_scolopendria__burm__f___pic__serm_",
"phymosia_umbellata__cav___kearney",
"physalis_alkekengi_l_",
"physalis_angulata_l_",
"physalis_crassifolia_benth_",
"physalis_heterophylla_nees",
"physalis_longifolia_nutt_",
"physalis_peruviana_l_",
"physalis_philadelphica_lam_",
"physalis_pubescens_l_",
"physalis_viscosa_l_",
"physocarpus_capitatus__pursh__kuntze",
"physocarpus_opulifolius__l___maxim_",
"physocarpus_opulifolius__l___raf_",
"physoplexis_comosa__l___schur",
"physostegia_virginiana__l___benth_",
"phyteuma_betonicifolium_vill_",
"phyteuma_globulariifolium_sternb____hoppe",
"phyteuma_hemisphaericum_l_",
"phyteuma_nigrum_f_w_schmidt",
"phyteuma_orbiculare_l_",
"phyteuma_ovatum_honck_",
"phyteuma_scheuchzeri_all_",
"phyteuma_spicatum_l_",
"phytolacca_acinosa_roxb_",
"phytolacca_americana_l_",
"phytolacca_bogotensis_kunth",
"phytolacca_dioica_l_",
"phytolacca_esculenta_houtt_",
"phytolacca_esculenta_van_houtte",
"phytolacca_octandra_l_",
"picea_abies__l___h_karst_",
"picea_abies__l___karst_",
"picea_engelmannii_parry_ex_engelm_",
"picea_glauca__moench__voss",
"picea_omorika__pancic__purk_",
"picea_orientalis__l___link",
"picea_orientalis__l___peterm_",
"picea_pungens_engelm_",
"picea_rubens_sarg_",
"picea_sitchensis__bong___carri_re",
"picea_wilsonii_mast_",
"picnomon_acarna__l___cass_",
"picris_echioides_l_",
"picris_hieracioides_l_",
"picris_hieracioides_sibth____sm_",
"pieris_japonica__thunb___d_don_ex_g_don",
"pieris_japonica__thunb___d__don_ex_g__don",
"pilea_cadierei_gagnep____guillaumin",
"pilea_depressa__sw___blume",
"pilea_involucrata__sims__c_h_wright___dewar",
"pilea_involucrata__sims__urb_",
"pilea_microphylla__l___liebm_",
"pilea_mollis_wedd_",
"pilea_nummulariifolia__sw___wedd_",
"pilea_peperomioides_diels",
"pilea_pumila__l___a__gray",
"pilea_spruceana_wedd_",
"pilosella_aurantiaca__l___f_w_schultz___sch_bip_",
"pilosella_caespitosa__dumort___p_d_sell___c_west",
"pilosella_cymosa__l___f_w_schultz___sch_bip_",
"pilosella_lactucella__wallr___p_d_sell___c_west",
"pilosella_officinarum_f_w_schultz___sch_bip_",
"pilosella_officinarum_vaill_",
"pilosella_piloselloides__vill___soj_k",
"pilosocereus_chrysostele__vaupel__byles___g_d_rowley",
"pilosocereus_pachycladus_f__ritter",
"pimelea_rosea_r__br_",
"pimenta_dioica__l___merr_",
"pimenta_racemosa__mill___j_w_moore",
"pimpinella_anisum_l_",
"pimpinella_major__l___huds_",
"pimpinella_peregrina_l_",
"pimpinella_saxifraga_l_",
"pimpinella_tragium_vill_",
"pinguicula_alpina_l_",
"pinguicula_esseriana_b__kirchn_",
"pinguicula_grandiflora_lam_",
"pinguicula_jaumavensis_debbert",
"pinguicula_leptoceras_rchb_",
"pinguicula_longifolia_ramond_ex_dc_",
"pinguicula_lusitanica_l_",
"pinguicula_moranensis_kunth",
"pinguicula_vulgaris_l_",
"pinus_albicaulis_engelm_",
"pinus_aristata_engelm_",
"pinus_armandii_franch_",
"pinus_attenuata_lemmon",
"pinus_balfouriana_balf_",
"pinus_banksiana_lamb_",
"pinus_brutia_ten_",
"pinus_bungeana_zucc__ex_endl_",
"pinus_canariensis_c_sm_",
"pinus_canariensis_sweet_ex_spreng_",
"pinus_cembra_l_",
"pinus_cembroides_zucc_",
"pinus_contorta_douglas_ex_loudon",
"pinus_coulteri_d_don",
"pinus_densiflora_siebold___zucc_",
"pinus_echinata_mill_",
"pinus_edulis_engelm_",
"pinus_elliottii_engelm_",
"pinus_halepensis_mill_",
"pinus_lambertiana_douglas",
"pinus_mugo_turra",
"pinus_nigra_arnold",
"pinus_nigra_j_f_arnold",
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"pinus_patula_schiede_ex_schltdl____cham_",
"pinus_patula_schltdl____cham_",
"pinus_peuce_griseb_",
"pinus_pinaster_aiton",
"pinus_pinea_l_",
"pinus_ponderosa_douglas",
"pinus_ponderosa_douglas_ex_c_lawson",
"pinus_ponderosa_lawson___c__lawson",
"pinus_radiata_d_don",
"pinus_resinosa_aiton",
"pinus_rigida_mill_",
"pinus_sibirica_du_tour",
"pinus_strobiformis_engelm_",
"pinus_strobus_l_",
"pinus_sylvestris_l_",
"pinus_tabuliformis_carri_re",
"pinus_taeda_l_",
"pinus_thunbergii_parl_",
"pinus_virginiana_mill_",
"pinus_wallichiana_a_b_jacks_",
"piper_aduncum_l_",
"piper_auritum_kunth",
"piper_betle_l_",
"piper_dilatatum_rich_",
"piper_hispidum_sw_",
"piper_marginatum_jacq_",
"piper_methysticum_g_forst_",
"piper_nigrum_l_",
"piper_peltatum_l_",
"piper_sarmentosum_roxb_",
"piper_umbellatum_l_",
"piptatherum_miliaceum__l___coss_",
"piscidia_piscipula__l___sarg_",
"pisonia_aculeata_l_",
"pisonia_grandis_r__br_",
"pisonia_subcordata_sw_",
"pistacia_atlantica_desf_",
"pistacia_chinensis_bunge",
"pistacia_lentiscus_l_",
"pistacia_terebinthus_l_",
"pistacia_vera_l_",
"pistacia_x_saportae_burnat",
"pistia_stratiotes_l_",
"pisum_sativum_l_",
"pithecellobium_dulce__roxb___benth_",
"pithecellobium_unguis_cati__l___benth_",
"pittosporum_crassifolium_banks___sol__ex_a_cunn_",
"pittosporum_heterophyllum_franch_",
"pittosporum_senacia_putt_",
"pittosporum_tanianum_veillon___tirel",
"pittosporum_tenuifolium_banks___sol__ex_gaertn_",
"pittosporum_tenuifolium_gaertn_",
"pittosporum_tobira__thunb___w_t_aiton",
"pittosporum_tobira__thunb___w_t__aiton",
"pittosporum_undulatum_vent_",
"pittosporum_viridiflorum_sims",
"pityrogramma_calomelanos__l___link",
"planchonella_dothioensis__aubr_v___swenson__bartish___munzinger",
"planchonella_latihila_munzinger___swenson",
"plantago_afra_l_",
"plantago_albicans_l_",
"plantago_alpina_l_",
"plantago_arenaria_waldst____kit_",
"plantago_argentea_chaix",
"plantago_atrata_hoppe",
"plantago_australis_lam_",
"plantago_coronopus_l_",
"plantago_crassifolia_forssk_",
"plantago_lagopus_l_",
"plantago_lanceolata_l_",
"plantago_major_l_",
"plantago_maritima_l_",
"plantago_media_l_",
"plantago_monosperma_pourr_",
"plantago_nivalis_boiss_",
"plantago_ovata_forssk_",
"plantago_rugelii_decne_",
"plantago_sempervirens_crantz",
"plantago_serraria_l_",
"plantago_subulata_l_",
"plantago_virginica_l_",
"plantago_weldenii_rchb_",
"platanthera_bifolia__l___rich_",
"platanthera_chlorantha__custer__rchb_",
"platanthera_ciliaris__l___lindl_",
"platanthera_leucophaea__nutt___lindl_",
"platanthera_psycodes__l___lindl_",
"platanus_occidentalis_l_",
"platanus_orientalis_l_",
"platanus_racemosa_nutt_",
"platanus_x_hispanica_mill__ex_m_nchh_",
"platycapnos_spicata__l___bernh_",
"platycerium_alcicorne_desv_",
"platycerium_bifurcatum__cav___c_chr_",
"platycerium_bifurcatum__cav___c__chr_",
"platycerium_stemaria__p__beauv___desv_",
"platycladus_orientalis__l___franco",
"platycodon_grandiflorus_a_dc_",
"platycodon_grandiflorus__jacq___a_dc_",
"platycodon_grandiflorus__jacq___a__dc_",
"platystemon_californicus_benth_",
"plectranthus_amboinicus__lour___spreng_",
"plectranthus_argentatus_s_t_blake",
"plectranthus_barbatus_andrews",
"plectranthus_caninus_roth",
"plectranthus_ciliatus_e_mey_",
"plectranthus_ernstii_codd",
"plectranthus_forsteri_benth_",
"plectranthus_hadiensis__forssk___schweinf__ex_sprenger",
"plectranthus_madagascariensis__pers___benth_",
"plectranthus_neochilus_schltr_",
"plectranthus_ornatus_codd",
"plectranthus_parviflorus_willd_",
"plectranthus_prostratus_g_rke",
"plectranthus_purpuratus_harv_",
"plectranthus_scutellarioides__l___r_br_",
"plectranthus_spp_",
"plectranthus_verticillatus__l_f___druce",
"plectranthus_zuluensis_t_cooke",
"pleioblastus_fortunei__van_houtte__nakai",
"pleioblastus_viridistriatus__regel__makino",
"pleiospilos_nelii_schwantes",
"pleopeltis_polypodioides__l___e_g__andrews___windham",
"plerandra_elegantissima__veitch_ex_mast___lowry__g_m_plunkett___frodin",
"pleurothallis_teaguei_luer",
"plinia_cauliflora__mart___kausel",
"plinia_phitrantha__kiaersk___sobral",
"pluchea_carolinensis__jacq___d_don",
"pluchea_indica__l___less_",
"pluchea_odorata__l___cass_",
"pluchea_sericea__nutt___coville",
"plukenetia_volubilis_l_",
"plumbago_auriculata_lam_",
"plumbago_europaea_l_",
"plumbago_indica_l_",
"plumbago_zeylanica_l_",
"plumeria_alba_l_",
"plumeria_obtusa_l_",
"plumeria_pudica_jacq_",
"plumeria_rubra_l_",
"poa_alpina_l_",
"poa_annua_l_",
"poa_bulbosa_l_",
"poa_chaixii_vill_",
"poa_compressa_l_",
"poa_nemoralis_l_",
"poa_palustris_l_",
"poa_pratensis_l_",
"poa_trivialis_l_",
"podocarpus_elatus_r_br__ex_endl_",
"podocarpus_falcatus__thunb___endl_",
"podocarpus_henkelii_stapf_ex_dallim____b_d_jacks_",
"podocarpus_latifolius__thunb___r_br__ex_mirb_",
"podocarpus_macrophyllus__thunb___sweet",
"podocarpus_neriifolius_d_don",
"podocarpus_salignus_d_don",
"podophyllum_peltatum_l_",
"podospermum_laciniatum__l___dc_",
"podospermum_purpureum__l___w_d_j_koch___ziz",
"podranea_ricasoliana__tanfani__sprague",
"pogonia_ophioglossoides__l___ker_gawl_",
"pogostemon_heyneanus_benth_",
"polanisia_dodecandra__l___dc_",
"polaskia_chichipe__gosselin__backeb_",
"polemonium_caeruleum_l_",
"polemonium_occidentale_greene",
"polemonium_pulcherrimum_hook_",
"polemonium_reptans_l_",
"polianthes_tuberosa_l_",
"polyalthia_longifolia__sonn___thwaites",
"polyalthia_longifolia__sonnerat__thwait_",
"polycarpon_polycarpoides__biv___zodda",
"polycarpon_tetraphyllum__l___l_",
"polycnemum_majus_a_braun",
"polygala_alba_nutt_",
"polygala_alpestris_rchb_",
"polygala_alpina__dc___steud_",
"polygala_amara_l_",
"polygala_calcarea_f_w_schultz",
"polygala_californica_nutt_",
"polygala_chamaebuxus_l_",
"polygala_comosa_schkuhr",
"polygala_lutea_l_",
"polygala_major_jacq_",
"polygala_monspeliaca_l_",
"polygala_myrtifolia_l_",
"polygala_paniculata_l_",
"polygala_paucifolia_willd_",
"polygala_rupestris_pourr_",
"polygala_vulgaris_l_",
"polygonatum_biflorum__walter__elliott",
"polygonatum_multiflorum__l___all_",
"polygonatum_odoratum__mill___druce",
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"rhipsalis_mesembryanthemoides_haw_",
"rhipsalis_micrantha__kunth__dc_",
"rhipsalis_pilocarpa_loefgr_",
"rhizophora_mangle_l_",
"rhodanthe_chlorocephala__turcz___paul_g_wilson",
"rhodanthemum_hosmariense__ball___b_h_wilcox__k_bremer___humphries_",
"rhodiola_integrifolia_raf_",
"rhodiola_rosea_l_",
"rhodochiton_atrosanguineum__zucc___rothm_",
"rhododendron_arborescens__pursh__torr_",
"rhododendron_calendulaceum__michx___torr_",
"rhododendron_canadense__l___torr_",
"rhododendron_canescens__michx___sweet",
"rhododendron_catawbiense_michx_",
"rhododendron_columbianum__piper__harmaja",
"rhododendron_ferrugineum_l_",
"rhododendron_hirsutum_l_",
"rhododendron_impeditum_balf__f____w_w__sm_",
"rhododendron_impeditum_franch_",
"rhododendron_indicum_sweet",
"rhododendron_indicum__l___sweet",
"rhododendron_lapponicum__l___wahlenb_",
"rhododendron_luteum_sweet",
"rhododendron_luteum__l___sweet",
"rhododendron_maximum_l_",
"rhododendron_molle__blume__g_don",
"rhododendron_periclymenoides__michx___shinners",
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"rhododendron_simsii_planch_",
"rhododendron_spp_",
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"rhododendron_viscosum__l___torr_",
"rhododendron_x_obtusum_planch_",
"rhodohypoxis_baurii__baker__nel",
"rhodomyrtus_tomentosa__aiton__hassk_",
"rhodostemonodaphne_elephantopus_madri__n",
"rhodothamnus_chamaecistus__l___rchb_",
"rhodotypos_scandens__thunb___makino",
"rhoeo_spathacea__sw___stearn",
"rhopalostylis_sapida__sol__ex_g_forst___h_wendl____drude",
"rhus_aromatica_aiton",
"rhus_copallinum_l_",
"rhus_coriaria_l_",
"rhus_glabra_l_",
"rhus_ovata_s__watson",
"rhus_typhina_l_",
"rhus_virens_lindh__ex_a__gray",
"rhynchosia_minima__l___dc_",
"rhynchospora_alba__l___vahl",
"rhynchospora_colorata__l___h_pfeiff_",
"ribes_alpinum_l_",
"ribes_americanum_mill_",
"ribes_aureum_pursh",
"ribes_cereum_douglas",
"ribes_cynosbati_l_",
"ribes_lacustre__pers___poir_",
"ribes_missouriense_nutt_",
"ribes_nigrum_l_",
"ribes_petraeum_wulfen",
"ribes_rubrum_l_",
"ribes_sanguineum_pursh",
"ribes_speciosum_pursh",
"ribes_uva_crispa_l_",
"richardia_brasiliensis_gomes",
"richardia_scabra_l_",
"ricinus_communis_l_",
"ridolfia_segetum_moris",
"rivina_humilis_l_",
"robinia_hispida_l_",
"robinia_pseudoacacia_l_",
"robinia_viscosa_vent_",
"rodgersia_aesculifolia_batalin",
"rodgersia_podophylla_a_gray",
"rodriguezia_venusta__lindl___rchb_f_",
"roemeria_hybrida__l___dc_",
"roldana_petasitis__sims__h_rob____brettell",
"rollinia_mucosa__jacq___baill_",
"romneya_coulteri_harv_",
"romulea_arnaudii_moret",
"romulea_bulbocodium__l___sebast____mauri",
"romulea_columnae_sebast____mauri",
"romulea_rosea__l___eckl_",
"rorippa_amphibia__l___besser",
"rorippa_austriaca__crantz__besser",
"rorippa_nasturtium_aquaticum__l___hayek",
"rorippa_palustris__l___besser",
"rorippa_pyrenaica__all___rchb_",
"rorippa_sylvestris__l___besser",
"rosa___damascena_herrm_",
"rosa_abietina_gren__ex_h_christ",
"rosa_agrestis_savi",
"rosa_arvensis_huds_",
"rosa_banksiae_r_br_",
"rosa_banksiae_r_br__ex_w_t_aiton",
"rosa_canina_l_",
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"rosa_corymbifera_borkh_",
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"rosa_ferruginea_vill_",
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"rosa_gallica_l_",
"rosa_glauca_pourr_",
"rosa_luciae_franch____rochebr_",
"rosa_micrantha_borrer_ex_sm_",
"rosa_mollis_sm_",
"rosa_moschata_herrm_",
"rosa_moyesii_hemsl____e_h_wilson",
"rosa_multiflora_thunb_",
"rosa_pendulina_l_",
"rosa_pouzinii_tratt_",
"rosa_roxburghii_tratt_",
"rosa_rubiginosa_l_",
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"rosa_sempervirens_l_",
"rosa_setigera_michx_",
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"rosa_spinosissima_l_",
"rosa_spp_",
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"rosa_tomentosa_sm_",
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"rosa_villosa_l_",
"rosa_virginiana_mill_",
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"rostraria_cristata__l___tzvelev",
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"rouya_polygama__desf___coincy",
"roystonea_oleracea__jacq___o_f_cook",
"roystonea_regia__kunth__o_f_cook",
"roystonea_regia__kunth__o_f__cook",
"rubia_peregrina_l_",
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"rubrivena_polystachya__c_f_w_meissn___m_kr_l",
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"rubus_caesius_l_",
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"rubus_canescens_dc_",
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"rubus_deliciosus_torr_",
"rubus_discolor_weihe___nees",
"rubus_elegantispinosus__a_schumach___h_e_weber",
"rubus_ellipticus_sm_",
"rubus_ferocior_h_e_weber",
"rubus_foliosus_weihe",
"rubus_fraxinifolius_poir_",
"rubus_fruticosus_l_",
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"rubus_hispidus_l_",
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"rubus_laciniatus_willd_",
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"rubus_niveus_thunb_",
"rubus_occidentalis_l_",
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"rubus_praecox_bertol_",
"rubus_pruinosus_arrh_",
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"rubus_rosifolius_sm_",
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"rubus_scaber_weihe",
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"rubus_spectabilis_pursh",
"rubus_sulcatus_vest",
"rubus_tricolor_focke",
"rubus_tricolor_focke_ex_prain",
"rubus_ulmifolius_schott",
"rubus_vestitus_weihe",
"rubus_x_uncinellus_p_j_m_ll____lef_vre",
"rudbeckia_fulgida_aiton",
"rudbeckia_hirta_l_",
"rudbeckia_laciniata_l_",
"rudbeckia_maxima_nutt_",
"rudbeckia_nitida_nutt_",
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"rudbeckia_subtomentosa_pursh",
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"ruellia_brevifolia__pohl__c_ezcurra",
"ruellia_geminiflora_kunth",
"ruellia_humilis_nutt_",
"ruellia_makoyana_closon",
"ruellia_nudiflora__engelm____a__gray__urb_",
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"ruellia_strepens_l_",
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"rumex_acetosa_l_",
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"rumex_vesicarius_l_",
"rumex_x_pratensis_mert____w_d_j_koch",
"rumohra_adiantiformis__g__forst___ching",
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"ruppia_maritima_l_",
"ruscus_aculeatus_l_",
"ruscus_hypoglossum_l_",
"ruscus_hypophyllum_l_",
"russelia_equisetiformis_cham____schltdl_",
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"ruta_angustifolia_pers_",
"ruta_chalepensis_l_",
"ruta_corsica_dc_",
"ruta_graveolens_l_",
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"sabal_palmetto__walter__lodd__ex_schult____schult__f_",
"sabal_palmetto__walter__lodd__ex_schult____schult_f_",
"sabatia_angularis__l___pursh",
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"sacoglottis_guianensis_benth_",
"sacoila_lanceolata__aubl___garay",
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"sagina_nodosa__l___fenzl",
"sagina_pilifera__dc___fenzl",
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"saintpaulia_ionantha_h_wendl_",
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"salix_alba_l_",
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"salix_integra_thunb_",
"salix_laggeri_wimm_",
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"salix_lasiolepis_benth_",
"salix_myrsinifolia_salisb_",
"salix_pentandra_l_",
"salix_purpurea_l_",
"salix_pyrenaica_gouan",
"salix_repens_l_",
"salix_reticulata_l_",
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"salix_serpyllifolia_scop_",
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"salpichroa_origanifolia__lam___baill_",
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"salvia_canariensis_l_",
"salvia_coccinea_buc_hoz_ex_etl_",
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"salvia_discolor_kunth",
"salvia_divinorum_epling___j_tiva",
"salvia_dorisiana_standl_",
"salvia_elegans_vahl",
"salvia_farinacea_benth_",
"salvia_fruticosa_mill_",
"salvia_glutinosa_l_",
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"salvia_guaranitica_a_st__hil__ex_benth_",
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"salvia_involucrata_cav_",
"salvia_leucantha_cav_",
"salvia_leucophylla_greene",
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"salvia_microphylla_kunth",
"salvia_nemorosa_l_",
"salvia_officinalis_l_",
"salvia_patens_cav_",
"salvia_pratensis_l_",
"salvia_purpurea_cav_",
"salvia_reflexa_hornem_",
"salvia_roemeriana_scheele",
"salvia_sclarea_l_",
"salvia_spathacea_greene",
"salvia_splendens_nees",
"salvia_splendens_sellow_ex_nees",
"salvia_splendens_sellow_ex_roem____schult_",
"salvia_splendens_sellow_ex_schult_",
"salvia_sylvestris_l_",
"salvia_tiliifolia_vahl",
"salvia_uliginosa_benth_",
"salvia_verbenaca_l_",
"salvia_verticillata_l_",
"salvia_viridis_l_",
"salvia_x_sylvestris_l_",
"salvinia_auriculata_aubl_",
"salvinia_minima_baker",
"salvinia_natans__l___all_",
"samanea_saman__jacq___merr_",
"sambucus_canadensis_l_",
"sambucus_ebulus_l_",
"sambucus_nigra_l_",
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"samolus_valerandi_l_",
"sanchezia_nobilis_hook_f_",
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"sandoricum_koetjape__burm_f___merr_",
"sandwithia_guyanensis_lanj_",
"sanguinaria_canadensis_l_",
"sanguisorba_canadensis_l_",
"sanguisorba_hakusanensis_makino",
"sanguisorba_minor_scop_",
"sanguisorba_officinalis_l_",
"sanicula_arctopoides_hook____arn_",
"sanicula_bipinnatifida_douglas_ex_hook_",
"sanicula_canadensis_l_",
"sanicula_crassicaulis_poepp__ex_dc_",
"sanicula_epipactis__scop___e_h_krause",
"sanicula_europaea_l_",
"sansevieria_bacularis_pfennig_ex_a_butler___jankalski",
"sansevieria_cylindrica_bojer_ex_hook_",
"sansevieria_ehrenbergii_schweinf__ex_baker",
"sansevieria_francisii_chahin_",
"sansevieria_hyacinthoides__l___druce",
"sansevieria_masoniana_chahin_",
"sansevieria_metallica_g_r_me___labroy",
"sansevieria_pearsonii_n_e_br_",
"sansevieria_spp_",
"sansevieria_stuckyi_god__leb",
"sansevieria_stuckyi_god__leb_",
"sansevieria_trifasciata_prain",
"sansevieria_trifasciata_hort__ex_prain",
"santalum_album_l_",
"santolina_africana_jord____fourr_",
"santolina_chamaecyparissus_l_",
"santolina_decumbens_mill_",
"santolina_pectinata_lag_",
"santolina_rosmarinifolia_l_",
"sanvitalia_procumbens_lam_",
"sapindus_saponaria_l_",
"sapium_glandulosum__l___morong",
"saponaria_ocymoides_l_",
"saponaria_officinalis_l_",
"saponaria_pumila__st__lag___janch_",
"saraca_indica_l_",
"sarcocapnos_enneaphylla__l___dc_",
"sarcocornia_fruticosa__l___a_j_scott",
"sarcocornia_perennis__mill___a_j_scott",
"sarcodes_sanguinea_torr_",
"sarcopoterium_spinosum__l___spach",
"saritaea_magnifica__sprague_ex_steenis__dugand",
"sarracenia_flava_l_",
"sarracenia_leucophylla_raf_",
"sarracenia_minor_walter",
"sarracenia_psittacina_michx_",
"sarracenia_purpurea_l_",
"sarracenia_rubra_walter",
"sasa_palmata__burb___camus",
"sasa_palmata__burb___e_g_camus",
"sassafras_albidum__nutt___nees",
"satureja_hortensis_l_",
"satureja_montana_l_",
"satureja_thymbra_l_",
"sauromatum_venosum__dryand__ex_aiton__kunth",
"sauropus_androgynus__l___merr_",
"saururus_cernuus_l_",
"saussurea_alpina__l___dc_",
"saxifraga_aizoides_l_",
"saxifraga_androsacea_l_",
"saxifraga_aquatica_lapeyr_",
"saxifraga_aretioides_lapeyr_",
"saxifraga_aspera_l_",
"saxifraga_biflora_all_",
"saxifraga_bronchialis_l_",
"saxifraga_bryoides_l_",
"saxifraga_caesia_l_",
"saxifraga_callosa_sm_",
"saxifraga_cebennensis_rouy___e_g_camus",
"saxifraga_cespitosa_l_",
"saxifraga_cotyledon_l_",
"saxifraga_cuneifolia_l_",
"saxifraga_exarata_vill_",
"saxifraga_fragosoi_sennen",
"saxifraga_geranioides_l_",
"saxifraga_granulata_l_",
"saxifraga_hirculus_l_",
"saxifraga_hirsuta_l_",
"saxifraga_hostii_tausch",
"saxifraga_hypnoides_l_",
"saxifraga_intricata_lapeyr_",
"saxifraga_longifolia_lapeyr_",
"saxifraga_media_gouan",
"saxifraga_moschata_wulfen",
"saxifraga_mutata_l_",
"saxifraga_oppositifolia_l_",
"saxifraga_paniculata_mill_",
"saxifraga_praetermissa_d_a_webb",
"saxifraga_pubescens_pourr_",
"saxifraga_rosacea_moench",
"saxifraga_rotundifolia_l_",
"saxifraga_squarrosa_sieber",
"saxifraga_stolonifera_curtis",
"saxifraga_stolonifera_meerb_",
"saxifraga_tridactylites_l_",
"saxifraga_umbrosa_l_",
"scabiosa_atropurpurea_l_",
"scabiosa_caucasica_m_bieb_",
"scabiosa_cinerea_lapeyr__ex_lam_",
"scabiosa_columbaria_l_",
"scabiosa_lucida_vill_",
"scabiosa_ochroleuca_l_",
"scabiosa_stellata_l_",
"scabiosa_triandra_l_",
"scadoxus_multiflorus__martyn__raf_",
"scaevola_aemula_r_br_",
"scaevola_nitida_r_br_",
"scaevola_plumieri__l___vahl",
"scaevola_taccada__gaertn___roxb_",
"scandix_pecten_veneris_l_",
"schedonorus_arundinaceus__schreb___dumort_",
"schedonorus_giganteus__l___holub",
"schedonorus_pratensis__huds___p_beauv_",
"schefflera_actinophylla_harms",
"schefflera_actinophylla__endl___harms",
"schefflera_arboricola__hayata__merr_",
"schefflera_elegantissima__veitch_ex_mast___lowry___frodin",
"schefflera_heptaphylla__l___frodin",
"schefflera_spp_",
"scheuchzeria_palustris_l_",
"schinus_molle_l_",
"schinus_terebinthifolia_raddi",
"schinus_terebinthifolius_raddi",
"schisandra_chinensis__turcz___baill_",
"schizachyrium_scoparium__michx___nash",
"schizanthus_pinnatus_ruiz___pav_",
"schizogyne_sericea_dc_",
"schizolobium_parahyba__vell___s_f_blake",
"schizophragma_hydrangeoides_siebold___zucc_",
"schkuhria_pinnata__lam___kuntze_ex_thell_",
"schlumbergera_bridgesii__lem___loefgr_",
"schlumbergera_gaertneri__regel__e__britton___a__rose",
"schlumbergera_spp_",
"schlumbergera_truncata__haw___moran",
"schoenoplectus_lacustris__l___palla",
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"smallanthus_uvedalia__l___mack__ex_mack_",
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"soleirolia_soleirolii__req___dandy",
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"solidago_altissima_l_",
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"sorbaria_sorbifolia__l___a_braun",
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"sorbus___thuringiaca__ilse_ex_nyman__sch_nach",
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"sparganium_angustifolium_michx_",
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"spathiphyllum_spp_",
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"tilia_tomentosa_moench",
"tilia_x_europaea_l_",
"tillandsia_aeranthos__loisel___l_b_sm_",
"tillandsia_baileyi_rose_ex_small",
"tillandsia_bartramii_elliott",
"tillandsia_bergeri_mez",
"tillandsia_brachycaulos_schltdl_",
"tillandsia_bulbosa_hook_",
"tillandsia_butzii_mez",
"tillandsia_caput_medusae_e_morren",
"tillandsia_cyanea_linden_ex_k_koch",
"tillandsia_duratii_vis_",
"tillandsia_fasciculata_sw_",
"tillandsia_festucoides_brongn__ex_mez",
"tillandsia_harrisii_ehlers",
"tillandsia_ionantha_planch_",
"tillandsia_juncea__ruiz___pav___poir_",
"tillandsia_leiboldiana_schltdl_",
"tillandsia_lindenii_regel",
"tillandsia_paucifolia_baker",
"tillandsia_pohliana_mez",
"tillandsia_pruinosa_sw_",
"tillandsia_recurvata__l___l_",
"tillandsia_stricta_sol__ex_ker_gawl_",
"tillandsia_tricholepis_baker",
"tillandsia_usneoides__l___l_",
"tillandsia_utriculata_l_",
"tillandsia_xerographica_rohweder",
"tipuana_tipu__benth___kuntze",
"tipularia_discolor__pursh__nutt_",
"titanopsis_calcarea__marloth__schwantes",
"tithonia_diversifolia_a_gray",
"tithonia_diversifolia__hemsl___a_gray",
"tithonia_diversifolia__hemsl___a__gray",
"tithonia_rotundifolia__mill___s_f_blake",
"tithonia_rotundifolia__mill___s_f__blake",
"toddalia_asiatica__l___lam_",
"tofieldia_calyculata__l___wahlenb_",
"tolmiea_menziesii__pursh__torr____a_gray",
"tolpis_barbata__l___gaertn_",
"tolpis_staticifolia__all___sch_bip_",
"toona_ciliata_m_roem_",
"toona_sinensis__juss___m_roem_",
"tordylium_apulum_l_",
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"torilis_africana_spreng_",
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"torilis_japonica__houtt___dc_",
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"torreya_californica_torr_",
"tournefortia_argentea_l__f_",
"toxicodendron_diversilobum__torr____a_gray__greene",
"toxicodendron_diversilobum__torr____a__gray__greene",
"toxicodendron_radicans__l___kuntze",
"toxicodendron_rydbergii__small_ex_rydb___greene",
"toxicodendron_vernix__l___kuntze",
"toxicoscordion_fremontii__torr___rydb_",
"tozzia_alpina_l_",
"trachelium_caeruleum_l_",
"trachelospermum_asiaticum__siebold___zucc___nakai",
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"trachycarpus_fortunei__hook___h_wendl_",
"trachystemon_orientalis__l___d_don",
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"tractema_lilio_hyacinthus__l___speta",
"tractema_verna__huds___speta",
"tradescantia___andersoniana_w_ludw____rohweder",
"tradescantia_cerinthoides_kunth",
"tradescantia_crassifolia_cav_",
"tradescantia_fluminensis_vell_",
"tradescantia_ohiensis_raf_",
"tradescantia_pallida__rose__d_r_hunt",
"tradescantia_pallida__rose__d_r__hunt",
"tradescantia_sillamontana_matuda",
"tradescantia_spathacea_sw_",
"tradescantia_spp_",
"tradescantia_virginiana_l_",
"tradescantia_x_andersoniana_f_ludw____rohweder",
"tradescantia_zebrina_bosse",
"tradescantia_zebrina_heynh__ex_bosse",
"tradescantia_zebrina_hort__ex_bosse",
"tragia_ramosa_torr_",
"tragopogon_crocifolius_l_",
"tragopogon_dubius_scop_",
"tragopogon_porrifolius_l_",
"tragopogon_pratensis_l_",
"tragus_racemosus__l___all_",
"trapa_natans_l_",
"trattinnickia_burserifolia_mart_",
"traunsteinera_globosa__l___rchb_",
"trema_micrantha__l___blume",
"trema_orientalis__l___blume",
"triadica_sebifera__l___small",
"trianthema_portulacastrum_l_",
"tribulus_cistoides_l_",
"tribulus_terrestris_l_",
"trichilia_dregeana_sond_",
"trichilia_emetica_vahl",
"trichodiadema_densum_schwantes",
"trichophorum_alpinum__l___pers_",
"trichophorum_cespitosum__l___hartm_",
"trichosanthes_cucumerina_l_",
"trichostema_dichotomum_l_",
"trichostema_lanatum_benth_",
"trichostema_lanceolatum_benth_",
"tricyrtis_formosana_baker",
"tricyrtis_hirta__thunb___hook_",
"tridax_procumbens_l_",
"tridax_procumbens__l___l_",
"tridens_flavus__l___hitchc_",
"trientalis_borealis_raf_",
"trifolium_alexandrinum_l_",
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"trifolium_alpinum_l_",
"trifolium_angustifolium_l_",
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"trifolium_badium_schreb_",
"trifolium_campestre_schreb_",
"trifolium_cherleri_l_",
"trifolium_dubium_sibth_",
"trifolium_fragiferum_l_",
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"trifolium_hirtum_all_",
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"trifolium_micranthum_viv_",
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"trifolium_nigrescens_viv_",
"trifolium_ochroleucon_huds_",
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"trifolium_pratense_l_",
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"triglochin_maritima_l_",
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"trigonella_caerulea__l___ser_",
"trigonella_esculenta_willd_",
"trigonella_foenum_graecum_l_",
"trigonidium_egertonianum_bateman_ex_lindl_",
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"trimezia_martinicensis__jacq___herb_",
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"tripleurospermum_inodorum_sch_bip_",
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"tripleurospermum_maritimum__l___w_d_j_koch",
"tripodion_tetraphyllum__l___fourr_",
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"triteleia_hyacinthina__lindl___greene",
"triteleia_laxa_benth_",
"triticum_aestivum_l_",
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"tsuga_canadensis__l___carri_re",
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"tuberaria_guttata__l___fourr_",
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"tulipa_agenensis_dc_",
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"turnera_diffusa_willd__ex_schult_",
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"turraea_mombassana_c__dc_",
"turritis_brassica_leers",
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"tylecodon_paniculatus__l_f___toelken",
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"typha_domingensis__pers___steud_",
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"tyrimnus_leucographus__l___cass_",
"ugni_molinae_turcz_",
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"ulex_gallii_planch_",
"ulex_minor_roth",
"ulex_parviflorus_pourr_",
"ulmus___hollandica_mill_",
"ulmus_alata_michx_",
"ulmus_americana_l_",
"ulmus_carpinifolia_gled_",
"ulmus_crassifolia_nutt_",
"ulmus_glabra_huds_",
"ulmus_laevis_pall_",
"ulmus_minor_mill_",
"ulmus_parvifolia_jacq_",
"ulmus_pumila_l_",
"ulmus_rubra_muhl_",
"ulmus_thomasii_sarg_",
"ulmus_x_hollandica_mill_",
"umbellularia_californica__hook____arn___nutt_",
"umbilicus_horizontalis__guss___dc_",
"umbilicus_rupestris__salisb___dandy",
"uncaria_macrophylla_wall_",
"uncarina_grandidieri__baill___stapf",
"uncinia_rubra_colenso_ex_boott",
"ungnadia_speciosa_endl_",
"uniola_paniculata_l_",
"unonopsis_stipitata_diels",
"urena_lobata_l_",
"urera_baccifera__l___gaudich__ex_wedd_",
"urochloa_panicoides_p__beauv_",
"urospermum_dalechampii__l___scop__ex_f_w_schmidt",
"urospermum_picroides__l___scop__ex_f_w_schmidt",
"urtica_chamaedryoides_pursh",
"urtica_dioica_l_",
"urtica_membranacea_poir_",
"urtica_membranacea_poir__ex_savigny",
"urtica_pilulifera_l_",
"urtica_urens_l_",
"utricularia_australis_r_br_",
"utricularia_gibba_l_",
"utricularia_intermedia_hayne",
"utricularia_minor_l_",
"utricularia_subulata_l_",
"utricularia_vulgaris_l_",
"uvularia_grandiflora_sm_",
"uvularia_perfoliata_l_",
"uvularia_sessilifolia_l_",
"vaccaria_hispanica__mill___rauschert",
"vaccinium_angustifolium_aiton",
"vaccinium_arboreum_marshall",
"vaccinium_corymbosum_l_",
"vaccinium_macrocarpon_aiton",
"vaccinium_membranaceum_douglas_ex_torr_",
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"vaccinium_ovalifolium_sm_",
"vaccinium_ovatum_pursh",
"vaccinium_oxycoccos_l_",
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"vaccinium_parvifolium_sm_",
"vaccinium_stamineum_l_",
"vaccinium_uliginosum_l_",
"vaccinium_vitis_idaea_l_",
"vachellia_erioloba__e__mey___p_j_h__hurter",
"vachellia_farnesiana__l___wight___arn_",
"vachellia_gerrardii__benth___p_j_h__hurter",
"vachellia_tortilis__forssk___galasso___banfi",
"vachellia_xanthophloea__benth___p_j_h__hurter",
"valantia_muralis_l_",
"valeriana_apula_pourr_",
"valeriana_dioica_l_",
"valeriana_montana_l_",
"valeriana_officinalis_l_",
"valeriana_pyrenaica_l_",
"valeriana_saxatilis_l_",
"valeriana_sitchensis_bong_",
"valeriana_tripteris_l_",
"valeriana_tuberosa_l_",
"valerianella_dentata__l___pollich",
"valerianella_discoidea__l___loisel_",
"valerianella_eriocarpa_desv_",
"valerianella_locusta__l___lat_",
"valerianella_locusta__l___laterr_",
"valerianella_radiata__l___dufr_",
"vallisneria_spiralis_l_",
"vanda_coerulea_griff__ex_lindl_",
"vanda_coerulescens_griff_",
"vangueria_infausta_burch_",
"vangueria_madagascariensis_j_f_gmel_",
"vanilla_odorata_c_presl",
"vanilla_planifolia_andrews",
"vanilla_planifolia_jacks_",
"vanilla_planifolia_jacks__ex_andrews",
"vanilla_pompona_schiede",
"vantanea_guianensis_aubl_",
"veitchia_merrillii__becc___h_e_moore",
"veltheimia_bracteata_harv__ex_baker",
"vepris_lanceolata_g__don",
"veratrum_album_l_",
"veratrum_californicum_durand",
"veratrum_nigrum_l_",
"veratrum_viride_aiton",
"verbascum___candelabrum_kar____kir_",
"verbascum_blattaria_l_",
"verbascum_boerhavii_l_",
"verbascum_chaixii_vill_",
"verbascum_densiflorum_bertol_",
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"verbascum_nevadense_boiss_",
"verbascum_nigrum_l_",
"verbascum_phlomoides_l_",
"verbascum_phoeniceum_l_",
"verbascum_pulverulentum_vill_",
"verbascum_sinaiticum_benth_",
"verbascum_sinuatum_l_",
"verbascum_speciosum_schrad_",
"verbascum_thapsus_l_",
"verbascum_virgatum_stokes",
"verbena_bonariensis_l_",
"verbena_brasiliensis_vell_",
"verbena_halei_small",
"verbena_hastata_l_",
"verbena_lasiostachys_link",
"verbena_litoralis_kunth",
"verbena_officinalis_l_",
"verbena_rigida_spreng_",
"verbena_stricta_vent_",
"verbena_urticifolia_l_",
"verbena_x_hybrida_voss",
"verbesina_alternifolia__l___britton_ex_kearney",
"verbesina_encelioides__cav___benth____hook__f__ex_a__gray",
"verbesina_encelioides__cav___benth____hook_f__ex_a_gray",
"verbesina_occidentalis__l___walter",
"verbesina_virginica_l_",
"vernonia_amygdalina_delile",
"vernonia_baldwinii_torr_",
"vernonia_brachycalyx_o_hoffm_",
"vernonia_fasciculata_michx_",
"vernonia_noveboracensis__l___michx_",
"veronica_agrestis_l_",
"veronica_allionii_vill_",
"veronica_alpina_l_",
"veronica_americana_schwein__ex_benth_",
"veronica_anagallis_aquatica_l_",
"veronica_aphylla_l_",
"veronica_arvensis_l_",
"veronica_austriaca_l_",
"veronica_beccabunga_l_",
"veronica_bellidioides_l_",
"veronica_catenata_pennell",
"veronica_chamaedrys_l_",
"veronica_cymbalaria_bodard",
"veronica_filiformis_sm_",
"veronica_fruticans_jacq_",
"veronica_gentianoides_vahl",
"veronica_hederifolia_l_",
"veronica_longifolia_l_",
"veronica_montana_l_",
"veronica_officinalis_l_",
"veronica_orsiniana_ten_",
"veronica_paniculata_l_",
"veronica_peregrina_l_",
"veronica_persica_poir_",
"veronica_polita_fr_",
"veronica_ponae_gouan",
"veronica_prostrata_l_",
"veronica_scutellata_l_",
"veronica_serpyllifolia_l_",
"veronica_spicata_l_",
"veronica_sublobata_m_fisch_",
"veronica_teucrium_l_",
"veronica_triphyllos_l_",
"veronica_urticifolia_jacq_",
"veronica_verna_l_",
"veronicastrum_sibiricum__l___pennell",
"veronicastrum_virginicum__l___farw_",
"viburnum_acerifolium_l_",
"viburnum_carlesii_hemsl_",
"viburnum_davidii_franch_",
"viburnum_dentatum_l_",
"viburnum_dilatatum_thunb_",
"viburnum_farreri_stearn",
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"viburnum_lentago_l_",
"viburnum_macrocephalum_fortune",
"viburnum_nudum_l_",
"viburnum_odoratissimum_ker_gawl_",
"viburnum_opulus_l_",
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"viburnum_prunifolium_l_",
"viburnum_recognitum_fernald",
"viburnum_rhytidophyllum_hemsl_",
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"viburnum_rufidulum_raf_",
"viburnum_sieboldii_miq_",
"viburnum_tinus_l_",
"vicia_angustifolia_l_",
"vicia_benghalensis_l_",
"vicia_bithynica__l___l_",
"vicia_cassubica_l_",
"vicia_cracca_l_",
"vicia_dasycarpa_ten_",
"vicia_disperma_dc_",
"vicia_faba_l_",
"vicia_grandiflora_scop_",
"vicia_hirsuta__l___gray",
"vicia_hybrida_l_",
"vicia_lathyroides_l_",
"vicia_lutea_l_",
"vicia_monantha_retz_",
"vicia_narbonensis_l_",
"vicia_onobrychioides_l_",
"vicia_orobus_dc_",
"vicia_pannonica_crantz",
"vicia_parviflora_cav_",
"vicia_peregrina_l_",
"vicia_pyrenaica_pourr_",
"vicia_sativa_l_",
"vicia_segetalis_thuill_",
"vicia_sepium_l_",
"vicia_serratifolia_jacq_",
"vicia_sylvatica_l_",
"vicia_tenuifolia_roth",
"vicia_tetrasperma__l___schreb_",
"vicia_villosa_roth",
"victoria_amazonica__poepp___j_c__sowerby",
"victoria_cruziana_a_d__orb_",
"vigna_frutescens_a_rich_",
"vigna_luteola__jacq___benth_",
"vigna_radiata__l___r_wilczek",
"vigna_speciosa__kunth__verdc_",
"vigna_unguiculata__l___walp_",
"vigna_vexillata__l___a_rich_",
"viguiera_dentata__cav___spreng_",
"vinca_difformis_pourr_",
"vinca_herbacea_waldst____kit_",
"vinca_major_l_",
"vinca_minor_l_",
"vincetoxicum_hirundinaria_medik_",
"vincetoxicum_nigrum__l___moench",
"viola_adunca_sm_",
"viola_alba_besser",
"viola_arborescens_l_",
"viola_arvensis_murray",
"viola_bicolor_pursh",
"viola_biflora_l_",
"viola_blanda_willd_",
"viola_bubanii_timb__lagr_",
"viola_calcarata_l_",
"viola_canadensis_l_",
"viola_canina_l_",
"viola_cenisia_l_",
"viola_cornuta_l_",
"viola_elatior_fr_",
"viola_glabella_nutt_",
"viola_hederacea_labill_",
"viola_hirta_l_",
"viola_kitaibeliana_schult_",
"viola_labradorica_schrank",
"viola_lutea_huds_",
"viola_melissifolia_greene",
"viola_mirabilis_l_",
"viola_odorata_l_",
"viola_palustris_l_",
"viola_pedata_l_",
"viola_pubescens_aiton",
"viola_purpurea_kellogg",
"viola_pyrenaica_ramond_ex_dc_",
"viola_reichenbachiana_jord__ex_boreau",
"viola_riviniana_rchb_",
"viola_rostrata_pursh",
"viola_sagittata_aiton",
"viola_sempervirens_greene",
"viola_sororia_willd_",
"viola_striata_aiton",
"viola_suavis_m_bieb_",
"viola_tricolor_l_",
"viola_x_wittrockiana_gams_ex_kappert",
"virola_sebifera_aubl_",
"viscaria_alpina__l___g_don",
"viscaria_vulgaris_bernh_",
"viscum_album_l_",
"vismia_cayennensis__jacq___pers_",
"vismia_sessilifolia__aubl___choisy",
"visnaga_daucoides_gaertn_",
"vitex_agnus_castus_l_",
"vitex_negundo_l_",
"vitex_pooara_corbishley",
"vitex_trifolia_l_",
"vitex_zeyheri_sond__ex_schauer",
"vitis_aestivalis_michx_",
"vitis_californica_benth_",
"vitis_labrusca_l_",
"vitis_mustangensis_buckley",
"vitis_riparia_michx_",
"vitis_rotundifolia_michx_",
"vitis_vinifera_l_",
"vitis_vulpina_l_",
"volkameria_inermis_l_",
"vouacapoua_americana_aubl_",
"vriesea_carinata_wawra",
"vriesea_gigantea_gaudich_",
"vriesea_heliconioides__kunth__hook__ex_walp_",
"vriesea_imperialis_carri_re",
"vriesea_pardalina_mez",
"vriesea_splendens__brongn___lem_",
"vulpia_bromoides__l___gray",
"vulpia_ciliata_dumort_",
"vulpia_ligustica__all___link",
"vulpia_myuros__l___c_c_gmel_",
"wahlenbergia_capillacea__l_f___a_dc_",
"wahlenbergia_hederacea__l___rchb_",
"waldsteinia_geoides_willd_",
"waldsteinia_ternata_fritsch",
"waldsteinia_ternata__stephan__fritsch",
"waltheria_indica_l_",
"warburgia_salutaris__g_bertol___chiov_",
"washingtonia_filifera__linden_ex_andr___h_wendl__ex_de_bary",
"washingtonia_robusta_h_wendl_",
"washingtonia_robusta_h__wendl_",
"watsonia_borbonica__pourr___goldblatt",
"watsonia_meriana__l___mill_",
"weberocereus_tunilla__f_a_c__weber__britton___rose",
"weigela_florida__bunge__a_dc_",
"weigela_florida__bunge__a__dc_",
"welwitschia_mirabilis_hook_f_",
"westringia_fruticosa__willd___druce",
"westringia_longifolia_r_br_",
"wigandia_caracasana_kunth",
"wigandia_urens__ruiz___pav___kunth",
"wisteria_floribunda__willd___dc_",
"wisteria_frutescens__l___poir_",
"wisteria_sinensis__sims__sweet",
"withania_frutescens__l___pauquy",
"withania_somnifera__l___dunal",
"wodyetia_bifurcata_a_k_irvine",
"wolffia_arrhiza__l___horkel_ex_wimm_",
"wollemia_nobilis_w_g_jones__k_d_hill___j_m_allen",
"woodfordia_fruticosa__l___kurz",
"woodsia_alpina__bolton__gray",
"woodsia_ilvensis__l___r_br_",
"woodsia_ilvensis__l___r__br_",
"woodwardia_areolata__l___t__moore",
"woodwardia_fimbriata_sm_",
"woodwardia_radicans__l___sm_",
"worsleya_procera__lem___traub",
"wrightia_antidysenterica__l___r_br_",
"wrightia_religiosa__teijsm____binn___benth__ex_kurz",
"wyethia_angustifolia__dc___nutt_",
"xanthisma_texanum_dc_",
"xanthium_orientale_l_",
"xanthium_spinosum_l_",
"xanthium_strumarium_l_",
"xanthocercis_zambesiaca__baker__dumaz_le_grand",
"xanthorhiza_simplicissima_marshall",
"xanthorrhoea_glauca_d_j_bedford",
"xanthoselinum_alsaticum__l___schur",
"xanthosoma_sagittifolium__l___schott",
"xanthosoma_taioba_e_g_gon__",
"xanthosoma_violaceum_schott",
"xanthostemon_chrysanthus__f_muell___benth_",
"xanthostemon_pubescens__brongn____gris__sebert___pancher",
"xeranthemum_annuum_l_",
"xeranthemum_cylindraceum_sm_",
"xeranthemum_inapertum__l___mill_",
"xerochrysum_bracteatum__vent___tzvelev",
"xeronema_moorei_brongn____gris",
"xerophyllum_tenax__pursh__nutt_",
"xerosicyos_danguyi_humbert",
"ximenia_americana_l_",
"ximenia_caffra_sond_",
"xiphidium_caeruleum_aubl_",
"xylocarpus_granatum_j_koenig",
"xylopia_crinita_r_e_fr_",
"xylopia_frutescens_aubl_",
"xylopia_nitida_dunal",
"youngia_japonica__l___dc_",
"yucca_aloifolia_l_",
"yucca_angustissima_engelm__ex_trel_",
"yucca_arkansana_trel_",
"yucca_baccata_torr_",
"yucca_brevifolia_engelm_",
"yucca_elata__engelm___engelm_",
"yucca_elephantipes_regel_ex_trel_",
"yucca_faxoniana_sarg_",
"yucca_filamentosa_l_",
"yucca_filifera_chabaud",
"yucca_flaccida_haw_",
"yucca_gigantea_lem_",
"yucca_glauca_nutt_",
"yucca_gloriosa_l_",
"yucca_pallida_mckelvey",
"yucca_recurvifolia_salisb_",
"yucca_rostrata_engelm__ex_trel_",
"yucca_rupicola_scheele",
"yucca_schidigera_roezl_ex_ortgies",
"yucca_thompsoniana_trel_",
"zamia_furfuracea_l_f__ex_aiton",
"zamia_lacandona_schutzman___vovides",
"zamia_pumila_l_",
"zamia_sandovalii_c_nelson",
"zamioculcas_zamiifolia__lodd___engl_",
"zannichellia_palustris_l_",
"zantedeschia_aethiopica__l___spreng_",
"zantedeschia_albomaculata__hook___baill_",
"zantedeschia_elliottiana__w_watson__engl_",
"zantedeschia_rehmannii_engl_",
"zanthoxylum_americanum_mill_",
"zanthoxylum_armatum_dc_",
"zanthoxylum_bungeanum_maxim_",
"zanthoxylum_capense__thunb___harv_",
"zanthoxylum_chalybeum_engl_",
"zanthoxylum_clava_herculis_l_",
"zanthoxylum_fagara__l___sarg_",
"zauschneria_californica_c__presl",
"zea_mays_l_",
"zelkova_carpinifolia__pall___k_koch",
"zelkova_carpinifolia__pall___k__koch",
"zelkova_serrata__thunb___makino",
"zenobia_pulverulenta__w__bartram_ex_willd___pollard",
"zephyranthes_candida__lindl___herb_",
"zephyranthes_carinata_herb_",
"zephyranthes_citrina_baker",
"zephyranthes_rosea_lindl_",
"zeuxine_strateumatica__l___schltr_",
"zingiber_officinale_roscoe",
"zingiber_spectabile_griff_",
"zingiber_zerumbet__l___roscoe_ex_sm_",
"zinnia_angustifolia_kunth",
"zinnia_elegans_jacq_",
"zinnia_elegans_l_",
"zinnia_grandiflora_nutt_",
"zinnia_haageana_regel",
"zinnia_peruviana_l_",
"zinnia_peruviana__l___l_",
"zizia_aptera__a__gray__fernald",
"ziziphus_jujuba_mill_",
"ziziphus_lotus__l___lam_",
"ziziphus_mauritiana_lam_",
"ziziphus_mucronata_willd_",
"ziziphus_oenopolia__l___mill_",
"ziziphus_rivularis_codd",
"ziziphus_spina_christi__l___desf_",
"zostera_marina_l_",
"zoysia_matrella__l___merr_",
"zygopetalum_maculatum__kunth__garay",
"zygophyllum_fabago_l_"
] |
Kcapocheers/vit-base-oxford-iiit-pets
|
<!-- 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-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2023
- Accuracy: 0.9418
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3652 | 1.0 | 370 | 0.2956 | 0.9256 |
| 0.2163 | 2.0 | 740 | 0.2251 | 0.9378 |
| 0.1771 | 3.0 | 1110 | 0.1993 | 0.9472 |
| 0.1503 | 4.0 | 1480 | 0.1926 | 0.9526 |
| 0.1376 | 5.0 | 1850 | 0.1897 | 0.9526 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
Amoros/Amoros_Beaugosse_batch_64_epochs_200_test-large-2025_05_26_67930-bs64_freeze
|
<!-- 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. -->
# Amoros_Beaugosse_batch_64_epochs_200_test-large-2025_05_26_67930-bs64_freeze
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: 0.0806
- F1 Micro: 0.6521
- F1 Macro: 0.5228
- Accuracy: 0.5581
- 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: 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: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Rate |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|:------:|
| No log | 1.0 | 489 | 0.1057 | 0.4791 | 0.1939 | 0.3399 | 0.001 |
| 0.2268 | 2.0 | 978 | 0.0992 | 0.5171 | 0.2748 | 0.3829 | 0.001 |
| 0.1131 | 3.0 | 1467 | 0.0967 | 0.5206 | 0.3019 | 0.3833 | 0.001 |
| 0.1072 | 4.0 | 1956 | 0.0958 | 0.5299 | 0.3230 | 0.3974 | 0.001 |
| 0.1049 | 5.0 | 2445 | 0.0957 | 0.5463 | 0.3499 | 0.4164 | 0.001 |
| 0.1038 | 6.0 | 2934 | 0.0949 | 0.5293 | 0.3454 | 0.3913 | 0.001 |
| 0.1041 | 7.0 | 3423 | 0.0951 | 0.5530 | 0.3660 | 0.4262 | 0.001 |
| 0.1035 | 8.0 | 3912 | 0.0956 | 0.5493 | 0.3504 | 0.4249 | 0.001 |
| 0.1028 | 9.0 | 4401 | 0.0945 | 0.5652 | 0.3616 | 0.4453 | 0.001 |
| 0.1029 | 10.0 | 4890 | 0.0934 | 0.5579 | 0.3733 | 0.4259 | 0.001 |
| 0.1037 | 11.0 | 5379 | 0.0938 | 0.5515 | 0.3631 | 0.4218 | 0.001 |
| 0.1026 | 12.0 | 5868 | 0.0932 | 0.5646 | 0.3631 | 0.4364 | 0.001 |
| 0.1024 | 13.0 | 6357 | 0.0923 | 0.5593 | 0.3755 | 0.4256 | 0.001 |
| 0.1025 | 14.0 | 6846 | 0.0913 | 0.5802 | 0.3898 | 0.4561 | 0.001 |
| 0.1012 | 15.0 | 7335 | 0.0920 | 0.5718 | 0.3889 | 0.4462 | 0.001 |
| 0.1016 | 16.0 | 7824 | 0.0916 | 0.5697 | 0.3873 | 0.4455 | 0.001 |
| 0.1016 | 17.0 | 8313 | 0.0931 | 0.5598 | 0.3802 | 0.4297 | 0.001 |
| 0.102 | 18.0 | 8802 | 0.0925 | 0.5640 | 0.3654 | 0.4359 | 0.001 |
| 0.1017 | 19.0 | 9291 | 0.0914 | 0.5738 | 0.3874 | 0.4467 | 0.001 |
| 0.1019 | 20.0 | 9780 | 0.0917 | 0.5687 | 0.3728 | 0.4417 | 0.001 |
| 0.1006 | 21.0 | 10269 | 0.0881 | 0.5932 | 0.4237 | 0.4712 | 0.0001 |
| 0.0968 | 22.0 | 10758 | 0.0870 | 0.6013 | 0.4384 | 0.4823 | 0.0001 |
| 0.0946 | 23.0 | 11247 | 0.0866 | 0.6039 | 0.4367 | 0.4853 | 0.0001 |
| 0.0942 | 24.0 | 11736 | 0.0861 | 0.6062 | 0.4452 | 0.4893 | 0.0001 |
| 0.0938 | 25.0 | 12225 | 0.0854 | 0.6100 | 0.4490 | 0.4926 | 0.0001 |
| 0.0929 | 26.0 | 12714 | 0.0853 | 0.6145 | 0.4482 | 0.5026 | 0.0001 |
| 0.0921 | 27.0 | 13203 | 0.0851 | 0.6144 | 0.4441 | 0.4999 | 0.0001 |
| 0.0917 | 28.0 | 13692 | 0.0845 | 0.6152 | 0.4457 | 0.5013 | 0.0001 |
| 0.0913 | 29.0 | 14181 | 0.0846 | 0.6143 | 0.4484 | 0.4990 | 0.0001 |
| 0.0916 | 30.0 | 14670 | 0.0843 | 0.6204 | 0.4621 | 0.5092 | 0.0001 |
| 0.0911 | 31.0 | 15159 | 0.0841 | 0.6214 | 0.4646 | 0.5116 | 0.0001 |
| 0.0901 | 32.0 | 15648 | 0.0839 | 0.6225 | 0.4649 | 0.5123 | 0.0001 |
| 0.0904 | 33.0 | 16137 | 0.0837 | 0.6267 | 0.4654 | 0.5180 | 0.0001 |
| 0.0898 | 34.0 | 16626 | 0.0834 | 0.6241 | 0.4665 | 0.5132 | 0.0001 |
| 0.089 | 35.0 | 17115 | 0.0834 | 0.6271 | 0.4762 | 0.5179 | 0.0001 |
| 0.0894 | 36.0 | 17604 | 0.0832 | 0.6244 | 0.4721 | 0.5158 | 0.0001 |
| 0.0891 | 37.0 | 18093 | 0.0831 | 0.6264 | 0.4712 | 0.5174 | 0.0001 |
| 0.0891 | 38.0 | 18582 | 0.0832 | 0.6288 | 0.4690 | 0.5194 | 0.0001 |
| 0.0889 | 39.0 | 19071 | 0.0828 | 0.6318 | 0.4810 | 0.5250 | 0.0001 |
| 0.0885 | 40.0 | 19560 | 0.0826 | 0.6316 | 0.4825 | 0.5232 | 0.0001 |
| 0.0876 | 41.0 | 20049 | 0.0827 | 0.6268 | 0.4703 | 0.5165 | 0.0001 |
| 0.0877 | 42.0 | 20538 | 0.0822 | 0.6324 | 0.4893 | 0.5240 | 0.0001 |
| 0.0879 | 43.0 | 21027 | 0.0819 | 0.6371 | 0.5045 | 0.5321 | 0.0001 |
| 0.0877 | 44.0 | 21516 | 0.0825 | 0.6295 | 0.4782 | 0.5218 | 0.0001 |
| 0.0867 | 45.0 | 22005 | 0.0824 | 0.6305 | 0.4814 | 0.5237 | 0.0001 |
| 0.0867 | 46.0 | 22494 | 0.0821 | 0.6324 | 0.4893 | 0.5245 | 0.0001 |
| 0.0873 | 47.0 | 22983 | 0.0818 | 0.6362 | 0.4933 | 0.5308 | 0.0001 |
| 0.0868 | 48.0 | 23472 | 0.0817 | 0.6393 | 0.4984 | 0.5357 | 0.0001 |
| 0.0868 | 49.0 | 23961 | 0.0821 | 0.6329 | 0.4864 | 0.5260 | 0.0001 |
| 0.0865 | 50.0 | 24450 | 0.0819 | 0.6332 | 0.4850 | 0.5254 | 0.0001 |
| 0.0869 | 51.0 | 24939 | 0.0817 | 0.6373 | 0.4914 | 0.5332 | 0.0001 |
| 0.0865 | 52.0 | 25428 | 0.0821 | 0.6343 | 0.5013 | 0.5295 | 0.0001 |
| 0.0863 | 53.0 | 25917 | 0.0815 | 0.6403 | 0.4996 | 0.5375 | 0.0001 |
| 0.0862 | 54.0 | 26406 | 0.0818 | 0.6374 | 0.4899 | 0.5323 | 0.0001 |
| 0.086 | 55.0 | 26895 | 0.0818 | 0.6326 | 0.4935 | 0.5249 | 0.0001 |
| 0.086 | 56.0 | 27384 | 0.0816 | 0.6393 | 0.4962 | 0.5364 | 0.0001 |
| 0.0854 | 57.0 | 27873 | 0.0817 | 0.6391 | 0.4981 | 0.5363 | 0.0001 |
| 0.0856 | 58.0 | 28362 | 0.0815 | 0.6428 | 0.4922 | 0.5402 | 0.0001 |
| 0.0856 | 59.0 | 28851 | 0.0814 | 0.6360 | 0.4918 | 0.5308 | 0.0001 |
| 0.0852 | 60.0 | 29340 | 0.0813 | 0.6472 | 0.5072 | 0.5491 | 0.0001 |
| 0.0852 | 61.0 | 29829 | 0.0816 | 0.6377 | 0.4847 | 0.5339 | 0.0001 |
| 0.0845 | 62.0 | 30318 | 0.0810 | 0.6401 | 0.4960 | 0.5367 | 0.0001 |
| 0.0851 | 63.0 | 30807 | 0.0814 | 0.6433 | 0.4912 | 0.5433 | 0.0001 |
| 0.0847 | 64.0 | 31296 | 0.0810 | 0.6383 | 0.4912 | 0.5330 | 0.0001 |
| 0.0851 | 65.0 | 31785 | 0.0808 | 0.6409 | 0.5117 | 0.5383 | 0.0001 |
| 0.0841 | 66.0 | 32274 | 0.0807 | 0.6437 | 0.4995 | 0.5417 | 0.0001 |
| 0.0841 | 67.0 | 32763 | 0.0808 | 0.6439 | 0.5073 | 0.5435 | 0.0001 |
| 0.0842 | 68.0 | 33252 | 0.0809 | 0.6405 | 0.5073 | 0.5369 | 0.0001 |
| 0.0843 | 69.0 | 33741 | 0.0810 | 0.6391 | 0.4926 | 0.5361 | 0.0001 |
| 0.0839 | 70.0 | 34230 | 0.0806 | 0.6425 | 0.5095 | 0.5428 | 0.0001 |
| 0.0843 | 71.0 | 34719 | 0.0809 | 0.6404 | 0.5159 | 0.5360 | 0.0001 |
| 0.0838 | 72.0 | 35208 | 0.0813 | 0.6470 | 0.5043 | 0.5485 | 0.0001 |
| 0.0836 | 73.0 | 35697 | 0.0804 | 0.6405 | 0.5029 | 0.5349 | 0.0001 |
| 0.084 | 74.0 | 36186 | 0.0806 | 0.6441 | 0.5028 | 0.5432 | 0.0001 |
| 0.0836 | 75.0 | 36675 | 0.0807 | 0.6421 | 0.5134 | 0.5387 | 0.0001 |
| 0.0831 | 76.0 | 37164 | 0.0805 | 0.6427 | 0.5133 | 0.5408 | 0.0001 |
| 0.0838 | 77.0 | 37653 | 0.0806 | 0.6467 | 0.5015 | 0.5474 | 0.0001 |
| 0.0827 | 78.0 | 38142 | 0.0808 | 0.6402 | 0.4998 | 0.5396 | 0.0001 |
| 0.0833 | 79.0 | 38631 | 0.0805 | 0.6475 | 0.5048 | 0.5496 | 0.0001 |
| 0.0821 | 80.0 | 39120 | 0.0798 | 0.6531 | 0.5148 | 0.5560 | 1e-05 |
| 0.0814 | 81.0 | 39609 | 0.0796 | 0.6533 | 0.5242 | 0.5555 | 1e-05 |
| 0.081 | 82.0 | 40098 | 0.0795 | 0.6568 | 0.5260 | 0.5633 | 1e-05 |
| 0.0808 | 83.0 | 40587 | 0.0794 | 0.6562 | 0.5293 | 0.5608 | 1e-05 |
| 0.0802 | 84.0 | 41076 | 0.0792 | 0.6567 | 0.5257 | 0.5616 | 1e-05 |
| 0.0807 | 85.0 | 41565 | 0.0794 | 0.6577 | 0.5262 | 0.5621 | 1e-05 |
| 0.0799 | 86.0 | 42054 | 0.0794 | 0.6569 | 0.5265 | 0.5616 | 1e-05 |
| 0.0801 | 87.0 | 42543 | 0.0794 | 0.6570 | 0.5198 | 0.5627 | 1e-05 |
| 0.0803 | 88.0 | 43032 | 0.0794 | 0.6571 | 0.5282 | 0.5642 | 1e-05 |
| 0.0801 | 89.0 | 43521 | 0.0794 | 0.6551 | 0.5200 | 0.5597 | 1e-05 |
| 0.0799 | 90.0 | 44010 | 0.0794 | 0.6550 | 0.5186 | 0.5598 | 1e-05 |
| 0.0799 | 91.0 | 44499 | 0.0793 | 0.6558 | 0.5292 | 0.5622 | 0.0000 |
| 0.0799 | 92.0 | 44988 | 0.0793 | 0.6554 | 0.5248 | 0.5610 | 0.0000 |
| 0.0799 | 93.0 | 45477 | 0.0794 | 0.6563 | 0.5230 | 0.5609 | 0.0000 |
| 0.0797 | 94.0 | 45966 | 0.0794 | 0.6533 | 0.5214 | 0.5572 | 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"
] |
BeckerAnas/zesty-waterfall-203
|
<!-- 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. -->
# zesty-waterfall-203
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: 1.3379
- Accuracy: 0.4277
- Precision: 0.5354
- Recall: 0.4277
- F1: 0.3818
- Roc Auc: 0.7103
## 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: 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.4416 | 1.0 | 144 | 1.3819 | 0.3203 | 0.1026 | 0.3203 | 0.1554 | 0.3980 |
| 1.4694 | 2.0 | 288 | 1.5073 | 0.1680 | 0.0282 | 0.1680 | 0.0483 | 0.5547 |
| 1.4062 | 3.0 | 432 | 1.3379 | 0.4277 | 0.5354 | 0.4277 | 0.3818 | 0.7103 |
### 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"
] |
mbiarreta/deit-ena24_MD
|
<!-- 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. -->
# deit-ena24_MD
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the ena24_MD dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8782
- Accuracy: 0.5459
- F1: 0.4851
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 1.1949 | 0.1259 | 100 | 2.2116 | 0.4688 | 0.3895 |
| 0.5955 | 0.2519 | 200 | 2.6496 | 0.4443 | 0.4240 |
| 0.6457 | 0.3778 | 300 | 1.8782 | 0.5459 | 0.4851 |
| 0.5871 | 0.5038 | 400 | 1.9099 | 0.6211 | 0.5460 |
| 0.5023 | 0.6297 | 500 | 1.9720 | 0.6328 | 0.5493 |
| 0.4641 | 0.7557 | 600 | 2.2744 | 0.5869 | 0.5344 |
| 0.7986 | 0.8816 | 700 | 2.3082 | 0.5947 | 0.5330 |
| 0.2118 | 1.0076 | 800 | 2.1929 | 0.6182 | 0.5418 |
| 0.3053 | 1.1335 | 900 | 2.2016 | 0.6533 | 0.5738 |
| 0.2434 | 1.2594 | 1000 | 2.4980 | 0.5908 | 0.5520 |
| 0.1593 | 1.3854 | 1100 | 2.3773 | 0.6279 | 0.5581 |
| 0.6254 | 1.5113 | 1200 | 2.4206 | 0.5898 | 0.5226 |
| 0.3366 | 1.6373 | 1300 | 2.8014 | 0.5762 | 0.5195 |
| 0.2613 | 1.7632 | 1400 | 2.6497 | 0.6172 | 0.5472 |
| 0.1136 | 1.8892 | 1500 | 2.4213 | 0.6074 | 0.5433 |
| 0.1071 | 2.0151 | 1600 | 2.5978 | 0.6436 | 0.5991 |
| 0.087 | 2.1411 | 1700 | 2.6259 | 0.6562 | 0.6066 |
| 0.0358 | 2.2670 | 1800 | 3.1009 | 0.5850 | 0.5267 |
| 0.1166 | 2.3929 | 1900 | 2.7449 | 0.6416 | 0.5866 |
| 0.0119 | 2.5189 | 2000 | 3.0153 | 0.625 | 0.5515 |
| 0.0563 | 2.6448 | 2100 | 2.8322 | 0.6387 | 0.5822 |
| 0.0241 | 2.7708 | 2200 | 2.8272 | 0.6318 | 0.5833 |
| 0.0844 | 2.8967 | 2300 | 2.8638 | 0.6416 | 0.5756 |
| 0.1294 | 3.0227 | 2400 | 2.8058 | 0.6484 | 0.5936 |
| 0.0602 | 3.1486 | 2500 | 2.9597 | 0.6279 | 0.5449 |
| 0.0445 | 3.2746 | 2600 | 2.7733 | 0.6602 | 0.5929 |
| 0.0528 | 3.4005 | 2700 | 2.5498 | 0.6826 | 0.6305 |
| 0.0323 | 3.5264 | 2800 | 2.7229 | 0.6719 | 0.6138 |
| 0.007 | 3.6524 | 2900 | 2.9141 | 0.6650 | 0.6025 |
| 0.0012 | 3.7783 | 3000 | 3.1029 | 0.6533 | 0.5968 |
| 0.0005 | 3.9043 | 3100 | 2.8776 | 0.6221 | 0.5639 |
| 0.0056 | 4.0302 | 3200 | 3.0144 | 0.6455 | 0.5882 |
| 0.0033 | 4.1562 | 3300 | 3.0141 | 0.6270 | 0.5702 |
| 0.0998 | 4.2821 | 3400 | 2.8958 | 0.6553 | 0.6073 |
| 0.0136 | 4.4081 | 3500 | 3.0230 | 0.6641 | 0.6053 |
| 0.0002 | 4.5340 | 3600 | 2.8966 | 0.6484 | 0.5750 |
| 0.0024 | 4.6599 | 3700 | 2.8534 | 0.6650 | 0.6010 |
| 0.0022 | 4.7859 | 3800 | 2.6686 | 0.6748 | 0.6247 |
| 0.0408 | 4.9118 | 3900 | 2.6715 | 0.6904 | 0.6327 |
| 0.1765 | 5.0378 | 4000 | 2.8339 | 0.6621 | 0.5940 |
| 0.0001 | 5.1637 | 4100 | 2.8891 | 0.6738 | 0.6109 |
| 0.0008 | 5.2897 | 4200 | 2.8508 | 0.6797 | 0.6183 |
| 0.0006 | 5.4156 | 4300 | 2.8630 | 0.6787 | 0.6190 |
| 0.0553 | 5.5416 | 4400 | 2.8210 | 0.6836 | 0.6239 |
| 0.0 | 5.6675 | 4500 | 2.7912 | 0.6865 | 0.6291 |
| 0.0 | 5.7935 | 4600 | 2.8061 | 0.6797 | 0.6237 |
| 0.0085 | 5.9194 | 4700 | 2.6576 | 0.6904 | 0.6332 |
| 0.0001 | 6.0453 | 4800 | 2.7866 | 0.6748 | 0.6211 |
| 0.0 | 6.1713 | 4900 | 2.8083 | 0.6738 | 0.6197 |
| 0.0 | 6.2972 | 5000 | 2.8078 | 0.6777 | 0.6251 |
| 0.0 | 6.4232 | 5100 | 2.7978 | 0.6797 | 0.6265 |
| 0.0 | 6.5491 | 5200 | 2.7378 | 0.6963 | 0.6421 |
| 0.0 | 6.6751 | 5300 | 2.7390 | 0.6973 | 0.6428 |
| 0.0 | 6.8010 | 5400 | 2.7374 | 0.6963 | 0.6417 |
| 0.0 | 6.9270 | 5500 | 2.7374 | 0.6963 | 0.6415 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"american black bear",
"american crow",
"eastern fox squirrel",
"eastern gray squirrel",
"grey fox",
"horse",
"northern raccoon",
"red fox",
"striped skunk",
"virginia opossum",
"white_tailed_deer",
"wild turkey",
"bird",
"woodchuck",
"bobcat",
"chicken",
"coyote",
"dog",
"domestic cat",
"eastern chipmunk",
"eastern cottontail"
] |
huangqishan/cnn
|
# 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## 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]
|
[
"0 - zero",
"1 - one",
"2 - two",
"3 - three",
"4 - four",
"5 - five",
"6 - six",
"7 - seven",
"8 - eight",
"9 - nine"
] |
BeckerAnas/still-universe-209
|
<!-- 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. -->
# still-universe-209
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: 0.5721
- Accuracy: 0.6497
- Precision: 0.6965
- Recall: 0.6497
- F1: 0.6583
- Roc Auc: 0.8795
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.379 | 1.0 | 17 | 1.3021 | 0.4544 | 0.5543 | 0.4544 | 0.4617 | 0.7377 |
| 1.3695 | 2.0 | 34 | 1.2286 | 0.5391 | 0.5315 | 0.5391 | 0.5266 | 0.7806 |
| 1.1945 | 3.0 | 51 | 1.1127 | 0.5794 | 0.5651 | 0.5794 | 0.5647 | 0.8098 |
| 1.0517 | 4.0 | 68 | 0.8318 | 0.5872 | 0.6134 | 0.5872 | 0.5961 | 0.8273 |
| 1.0323 | 5.0 | 85 | 0.8958 | 0.5156 | 0.6297 | 0.5156 | 0.5319 | 0.8189 |
| 0.9029 | 6.0 | 102 | 0.7313 | 0.5365 | 0.6126 | 0.5365 | 0.5398 | 0.8267 |
| 0.9002 | 7.0 | 119 | 0.7217 | 0.5794 | 0.5998 | 0.5794 | 0.5558 | 0.8445 |
| 0.7855 | 8.0 | 136 | 0.6522 | 0.6029 | 0.6629 | 0.6029 | 0.6064 | 0.8581 |
| 0.756 | 9.0 | 153 | 0.6371 | 0.5964 | 0.6263 | 0.5964 | 0.5653 | 0.8643 |
| 0.7164 | 10.0 | 170 | 0.6291 | 0.5690 | 0.6930 | 0.5690 | 0.5780 | 0.8579 |
| 0.6894 | 11.0 | 187 | 0.6194 | 0.5938 | 0.6360 | 0.5938 | 0.5735 | 0.8699 |
| 0.6606 | 12.0 | 204 | 0.5834 | 0.6289 | 0.6906 | 0.6289 | 0.6402 | 0.8742 |
| 0.6273 | 13.0 | 221 | 0.5766 | 0.6510 | 0.6972 | 0.6510 | 0.6607 | 0.8780 |
| 0.6046 | 14.0 | 238 | 0.5732 | 0.6497 | 0.6965 | 0.6497 | 0.6583 | 0.8790 |
| 0.6255 | 15.0 | 255 | 0.5721 | 0.6497 | 0.6965 | 0.6497 | 0.6583 | 0.8795 |
### 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"
] |
Skorm/food11-vit
|
# food11-vit
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the [Food11 dataset](https://www.kaggle.com/datasets/trolukovich/food11-image-dataset).
## Model description
ViT-base transformer trained to classify food images into 11 categories using transfer learning and PyTorch Lightning.
## Intended uses & limitations
This model is intended for food image classification tasks with a fixed set of 11 common food types. It may not generalize to out-of-distribution food images or fine-grained food variants.
## Classes
- Bread
- Dairy product
- Dessert
- Egg
- Fried food
- Meat
- Noodles-Pasta
- Rice
- Seafood
- Soup
- Vegetable-Fruit
## Training and evaluation data
The model was trained on the training split of the Food11 dataset (9,866 images) and validated on the validation split (3,430 images). The test set was not used.
## Training procedure
### Training hyperparameters
The following hyperparameters were used:
- learning_rate: 2e-5
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: AdamW
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Epoch | Step | Training Loss | Validation Loss | Validation Accuracy |
|-------|------|----------------|-----------------|----------------------|
| 1 | 308 | 1.2517 | 0.1991 | 0.9531 |
| 2 | 617 | 0.4728 | 0.1376 | 0.9621 |
| 3 | 926 | 0.2027 | 0.1281 | 0.9621 |
| 4 | 1235 | 0.2861 | 0.1395 | 0.9589 |
| 5 | 1544 | 0.2943 | 0.1223 | 0.9659 |
### Framework versions
- Transformers 4.39.3
- PyTorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1
|
[
"bread",
"dairy product",
"dessert",
"egg",
"fried food",
"meat",
"noodles-pasta",
"rice",
"seafood",
"soup",
"vegetable-fruit"
] |
prithivMLmods/tooth-agenesis-siglip2
|
# tooth-agenesis-siglip2
> tooth-agenesis-siglip2 is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect various **dental anomalies and conditions** such as **Calculus**, **Caries**, **Gingivitis**, **Mouth Ulcer**, **Tooth Discoloration**, and **Hypodontia**. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
```py
Classification Report:
precision recall f1-score support
Calculus 0.6640 0.7623 0.7098 1296
Caries 0.9525 0.9558 0.9541 2601
Gingivitis 0.8496 0.7842 0.8156 2349
Mouth Ulcer 0.9939 0.9893 0.9916 2806
Tooth Discoloration 0.9314 0.9757 0.9530 2017
hypodontia 0.9983 0.9161 0.9554 1251
accuracy 0.9096 12320
macro avg 0.8983 0.8972 0.8966 12320
weighted avg 0.9132 0.9096 0.9105 12320
```
---
## Label Space: 6 Classes
```
Class 0: Calculus
Class 1: Caries
Class 2: Gingivitis
Class 3: Mouth Ulcer
Class 4: Tooth Discoloration
Class 5: hypodontia
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/tooth-agenesis-siglip2" # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Calculus",
"1": "Caries",
"2": "Gingivitis",
"3": "Mouth Ulcer",
"4": "Tooth Discoloration",
"5": "hypodontia"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=6, label="Dental Condition Classification"),
title="Tooth Agenesis Detection",
description="Upload a dental image to detect conditions such as Calculus, Caries, Gingivitis, Mouth Ulcer, Tooth Discoloration, or Hypodontia."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`tooth-agenesis-siglip2` is designed for:
* **Dental Diagnosis Support** – Assists dentists and clinicians in identifying common dental conditions from images.
* **Oral Health Monitoring** – A tool for regular monitoring of dental health in clinical or remote settings.
* **Tele-dentistry** – Enables automated screening in virtual consultations and rural healthcare setups.
* **Research and Education** – Useful for academic institutions and training platforms for demonstrating AI in dental diagnostics.
* **Early Detection** – Helps identify oral health issues early to prevent progression.
|
[
"calculus",
"caries",
"gingivitis",
"mouth ulcer",
"tooth discoloration",
"hypodontia"
] |
eiitndidkwh/roadwork
|
# 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).
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### Model Architecture and Objective
[More Information Needed]
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|
[
"none",
"roadwork"
] |
hfhooray/72_base
|
# 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.
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## 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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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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"
] |
prithivMLmods/shoe-type-detection
|
# shoe-type-detection
> shoe-type-detection is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect different types of shoes such as **Ballet Flats**, **Boat Shoes**, **Brogues**, **Clogs**, and **Sneakers**. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
```py
Classification Report:
precision recall f1-score support
Ballet Flat 0.8980 0.9465 0.9216 2000
Boat 0.9333 0.8750 0.9032 2000
Brogue 0.9313 0.9490 0.9401 2000
Clog 0.9244 0.8800 0.9016 2000
Sneaker 0.9137 0.9480 0.9306 2000
accuracy 0.9197 10000
macro avg 0.9202 0.9197 0.9194 10000
weighted avg 0.9202 0.9197 0.9194 10000
```
---
## Label Space: 5 Classes
```
Class 0: Ballet Flat
Class 1: Boat
Class 2: Brogue
Class 3: Clog
Class 4: Sneaker
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/shoe-type-detection" # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Ballet Flat",
"1": "Boat",
"2": "Brogue",
"3": "Clog",
"4": "Sneaker"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=5, label="Shoe Type Classification"),
title="Shoe Type Detection",
description="Upload an image of a shoe to classify it as Ballet Flat, Boat, Brogue, Clog, or Sneaker."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`shoe-type-detection` is designed for:
* **E-Commerce Automation** – Automate product tagging and classification in online retail platforms.
* **Footwear Inventory Management** – Efficiently organize and categorize large volumes of shoe images.
* **Retail Intelligence** – Enable AI-powered search and filtering based on shoe types.
* **Smart Surveillance** – Identify and analyze footwear types in surveillance footage for retail analytics.
* **Fashion and Apparel Research** – Analyze trends in shoe types and customer preferences using image datasets.
|
[
"ballet flat",
"boat",
"brogue",
"clog",
"sneaker"
] |
prithivMLmods/facial-age-detection
|
# facial-age-detection
> facial-age-detection is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect and classify human faces into **age groups** ranging from early childhood to elderly adults. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
```py
Classification Report:
precision recall f1-score support
age 01-10 0.9614 0.9669 0.9641 2474
age 11-20 0.8418 0.8467 0.8442 1181
age 21-30 0.8118 0.8326 0.8220 1523
age 31-40 0.6937 0.6683 0.6808 1010
age 41-55 0.7106 0.7528 0.7311 1181
age 56-65 0.6878 0.6646 0.6760 799
age 66-80 0.7949 0.7596 0.7768 653
age 80 + 0.9349 0.8343 0.8817 344
accuracy 0.8225 9165
macro avg 0.8046 0.7907 0.7971 9165
weighted avg 0.8226 0.8225 0.8223 9165
```
---
## Label Space: 8 Classes
```
Class 0: age 01-10
Class 1: age 11-20
Class 2: age 21-30
Class 3: age 31-40
Class 4: age 41-55
Class 5: age 56-65
Class 6: age 66-80
Class 7: age 80 +
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/facial-age-detection" # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "age 01-10",
"1": "age 11-20",
"2": "age 21-30",
"3": "age 31-40",
"4": "age 41-55",
"5": "age 56-65",
"6": "age 66-80",
"7": "age 80 +"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=8, label="Age Group Classification"),
title="Facial Age Detection",
description="Upload a face image to estimate the age group: 01–10, 11–20, 21–30, 31–40, 41–55, 56–65, 66–80, or 80+."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`facial-age-detection` is designed for:
* **Demographic Analytics** – Estimate age distributions in image datasets for research and commercial analysis.
* **Access Control & Verification** – Enforce age-based access in digital or physical environments.
* **Retail & Marketing** – Understand customer demographics in retail spaces through camera-based analytics.
* **Surveillance & Security** – Enhance people classification systems by integrating age detection.
* **Human-Computer Interaction** – Adapt experiences and interfaces based on user age.
|
[
"age 01-10",
"age 11-20",
"age 21-30",
"age 31-40",
"age 41-55",
"age 56-65",
"age 66-80",
"age 80 +"
] |
ohjoonhee/siglip2-giant-rokn393-linear
|
# 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.
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## Uses
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### Direct Use
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### Downstream Use [optional]
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[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
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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
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#### Testing Data
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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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"k8_하이브리드_2022_2024",
"캐스퍼_2022_2024",
"쏘나타_dn8_2020_2023",
"sm7_뉴아트_2008_2011",
"b_클래스_w246_2013_2018",
"머스탱_2015_2023",
"팰리세이드_2019_2022",
"sm6_2016_2020",
"올_뉴_말리부_2017_2018",
"뉴쏘렌토_r_2013_2014",
"xc40_2019_2022",
"니로_2017_2019",
"더_뉴_스파크_2019_2022",
"x6_g06_2024_2025",
"랭글러_jl_2018_2024",
"파일럿_3세대_2016_2018",
"스파크_2012_2015",
"더_뉴_k5_2세대_2019_2020",
"qx60_2016_2018",
"z4_g29_2019_2025",
"g_클래스_w463_2009_2017",
"k5_하이브리드_3세대_2020_2023",
"파나메라_971_2017_2023",
"디_올뉴코나_2023_2025",
"뉴_g80_2025_2026",
"뉴_mkz_2017_2020",
"a_클래스_w176_2015_2018",
"rav4_2016_2018",
"베리_뉴_티볼리_2020_2023",
"a5_f5_2019_2024",
"a7_2012_2016",
"리얼_뉴_콜로라도_2021_2022",
"제네시스_dh_2014_2016",
"qm3_2014_2017",
"레인지로버_이보크_2세대_2023_2024",
"올_뉴_모닝_2012_2015",
"gla_클래스_x156_2015_2019",
"셀토스_2020_2023",
"아베오_2012_2016",
"모델_3_2019_2022",
"아이오닉5_2022_2023",
"토레스_2023_2025",
"모닝_어반_ja_2021_2023",
"더_뉴_아반떼_cn7_2023_2025",
"마칸_2019_2021",
"더_뉴_파사트_2012_2019",
"5008_2세대_2021_2024",
"6시리즈_gt_g32_2018_2020",
"올_뉴_k7_하이브리드_2017_2019",
"e_클래스_w213_2021_2023",
"eqa_h243_2021_2024",
"더_뉴_g70_2021_2025",
"xc60_2세대_2022_2025",
"더_뉴_카니발_2019_2020",
"q7_4m_2016_2019",
"3시리즈_gt_f34_2014_2021",
"올_뉴_k7_2016_2019",
"k5_3세대_2020_2023",
"더_뉴_모하비_2017_2019",
"xc90_2세대_2020_2025",
"넥쏘_2018_2024",
"g_클래스_w463b_2019_2025",
"xm3_2020_2023",
"올_뉴_k3_2019_2021",
"투아렉_3세대_2020_2023",
"q3_f3_2020_2024",
"모하비_더_마스터_2020_2024",
"xc60_2세대_2018_2021",
"eqe_v295_2022_2024",
"싼타페_tm_2019_2020",
"티볼리_에어_2016_2019",
"k3_2013_2015",
"x1_u11_2023_2024",
"8시리즈_g15_2020_2024",
"eqs_v297_2022_2023",
"아반떼_n_2022_2023",
"k7_프리미어_하이브리드_2020_2021",
"glb_클래스_x247_2020_2023",
"gv80_2020_2022",
"올_뉴_카마로_2017_2018",
"7시리즈_g70_2023_2025",
"sm3_네오_2015_2019",
"트레일블레이저_2021_2022",
"더_뉴_맥스크루즈_2016_2018",
"s_클래스_w223_2021_2025",
"라브4_4세대_2013_2018",
"3008_2세대_2018_2023",
"에비에이터_2세대_2020_2025",
"gls_클래스_x167_2020_2024",
"더_기아_레이_ev_2024_2025",
"레인지로버_이보크_2016_2019",
"트래버스_2020_2023",
"a8_d5_2018_2023",
"sm5_노바_2015_2019",
"gle_클래스_w167_2019_2024",
"g80_rg3_2021_2023",
"m4_f82_2015_2020",
"1시리즈_f20_2013_2015",
"프리우스_4세대_2019_2022",
"1시리즈_f20_2016_2019",
"컨티넨탈_gt_2세대_2012_2017",
"더_뉴_sm6_2021_2024",
"더_뉴_아이오닉_하이브리드_2020",
"더_뉴_k5_하이브리드_3세대_2023_2025",
"뉴_es300h_2013_2015",
"sm7_노바_2015_2019",
"x7_g07_2023_2025",
"q50_2014_2017",
"시에나_4세대_2021_2024",
"모델_y_2021_2025",
"더_뉴_k7_2013_2016",
"스포티지_더_볼드_2019_2022",
"glc_클래스_x253_2023",
"2시리즈_액티브_투어러_u06_2022_2024",
"레인지로버_4세대_2014_2017",
"더_뉴_k3_2016_2018",
"ix_2022_2024",
"쿠퍼_컨버터블_2016_2024",
"컨티넨탈_10세대_2017_2019",
"eq900_2016_2018",
"프리우스_c_2018_2020",
"에스컬레이드_2015_2020",
"스포티지_4세대_2016_2018",
"싼타페_더_프라임_2016_2018",
"올_뉴_쏘렌토_2015_2017",
"gv70_2021_2023",
"디_올뉴그랜저_2023_2025",
"ux250h_2019_2024",
"그랜드카니발_2006_2010",
"x3_g01_2022_2024",
"m2_f87_2016_2021",
"q5_fy_2020",
"뉴_sm5_플래티넘_2013_2014",
"더_뉴_셀토스_2023_2025",
"컨티넨탈_gt_3세대_2018_2023",
"q8_4m_2020_2025",
"아이오닉_하이브리드_2016_2019",
"q5_fy_2021_2024",
"그랜저_hg_2011_2014",
"5시리즈_g60_2024_2025",
"더_뉴_k5_3세대_2024_2025",
"그랜저_gn7_2023_2025",
"쏘나타_뉴_라이즈_2018_2019",
"디_올뉴니로ev_2023_2024",
"레인지로버_스포츠_2세대_2018_2022",
"어코드_10세대_2018_2022",
"올_뉴_투싼_tl_2016_2018",
"x6_g06_2020_2023",
"에스컬레이드_5세대_2021_2024",
"xc90_2세대_2017_2019",
"3시리즈_f30_2013_2018",
"glc_클래스_x253_2017_2019",
"콰트로포르테_2017_2022",
"x2_f39_2018_2023",
"더_뉴_모닝_2015_2016",
"라브4_5세대_2019_2024",
"뉴_스타일_코란도_c_2017_2019",
"뉴_qm5_2012_2014",
"스타리아_2022_2025",
"718_카이맨_2017_2024",
"아슬란_2015_2018",
"뉴_es300h_2016_2018",
"말리부_2012_2016",
"더_뉴_아반떼_ad_2019_2020",
"티볼리_아머_2018_2019",
"더_뉴스포티지r_2014_2016",
"1시리즈_f40_2020_2024",
"x5_f15_2014_2018",
"컴패스_2세대_2018_2022",
"더_뉴_니로_2020_2022",
"레니게이드_2015_2017",
"캠리_xv70_2018_2024",
"레인지로버_스포츠_2세대_2013_2017",
"s_클래스_w221_2006_2013",
"쿠퍼_클럽맨_2016_2024",
"에쿠스_신형_2010_2015",
"new_xf_2012_2015",
"아이오닉6_2023_2025",
"올_뉴_투싼_tl_2019_2020",
"아반떼_ad_2016_2018",
"g80_rg3_2025",
"올_뉴_렉스턴_2021_2025",
"일렉트리파이드_gv70_2022_2024",
"yf쏘나타_하이브리드_2011_2015",
"더_올뉴g80_2021_2024",
"그랑_콜레오스_2025",
"뷰티풀_코란도_2019_2024",
"디스커버리_5_2017_2020",
"팰리세이드_lx3_2025",
"v90_크로스컨트리_2018_2024",
"아반떼_md_2011_2014",
"뉴_a6_2015_2018",
"몬데오_4세대_2015_2020",
"2시리즈_그란쿠페_f44_2020_2024",
"g90_2019_2022",
"체로키_kl_2019_2023",
"m5_f90_2018_2023",
"5시리즈_gt_f07_2010_2017",
"뉴_카이엔_2011_2018",
"디스커버리_스포츠_2세대_2020_2025",
"ev9_2024_2025",
"e_클래스_w212_2010_2016",
"레니게이드_2019_2023",
"7시리즈_g11_2016_2018",
"더_뉴_싼타페_2021_2023",
"뉴qm3_2018_2019",
"4시리즈_g22_2021_2023",
"3시리즈_g20_2019_2022",
"더_뉴_k9_2세대_2022_2025",
"아반떼_하이브리드_cn7_2021_2023",
"더_뉴_팰리세이드_2023_2024",
"c_클래스_w206_2022_2024",
"2시리즈_액티브_투어러_f45_2019_2021",
"x1_f48_2016_2019",
"i4_2022_2024",
"그랜드_스타렉스_2016_2018",
"티볼리_2015_2018",
"쿠퍼_컨트리맨_2012_2015",
"투싼_nx4_2021_2023",
"x4_g02_2022_2025",
"x5_g05_2024_2025",
"x7_g07_2019_2022",
"티볼리_에어_2021_2022",
"콰트로포르테_2014_2016",
"레인지로버_5세대_2023_2024",
"뉴_sm5_임프레션_2008_2010",
"더_뉴_그랜저_ig_2020_2023",
"더_넥스트_스파크_2016_2018",
"f150_2004_2021",
"콜로라도_2020_2020",
"박스터_718_2017_2024",
"뉴_티구안_2012_2016",
"디_올_뉴_니로_2022_2025",
"카니발_4세대_2021",
"더_뉴_그랜드_스타렉스_2018_2021",
"기블리_2014_2023",
"더_뉴_기아_레이_2022_2025",
"뉴_a6_2012_2014",
"yf쏘나타_2009_2012",
"gv80_2024_2025",
"더_뉴_레이_2018_2022",
"더_뉴_qm6_2024_2025",
"르반떼_2017_2022",
"i30_pd_2017_2018",
"더_뉴_투싼_nx4_2023_2025",
"트레일블레이저_2023",
"쏘나타_디_엣지_dn8_2024_2025",
"글래디에이터_jt_2020_2023",
"뉴_cc_2012_2016",
"cle_클래스_c236_2024_2025",
"디_올_뉴_스포티지_2022_2024",
"a4_b9_2016_2019",
"쏘렌토_4세대_2021_2023",
"a4_b9_2020_2024",
"q30_2017_2019",
"5008_2세대_2018_2019",
"레인지로버_4세대_2018_2022",
"s90_2021_2025",
"6시리즈_gt_g32_2021_2024",
"e_트론_2020_2023",
"gle_클래스_w166_2016_2018",
"glc_클래스_x254_2023_2025",
"싼타페_mx5_2024_2025",
"티구안_올스페이스_2018_2023",
"렉스턴_스포츠_칸_2019_2020",
"v40_2015_2018",
"4시리즈_g22_2024_2025",
"더_k9_2019_2021",
"x6_f16_2015_2019",
"랭글러_jk_2009_2017",
"익스플로러_6세대_2020_2025",
"마칸_2014_2018",
"k7_프리미어_2020_2021",
"뉴_제타_2011_2016",
"ev6_2022_2024",
"더_뉴_k3_2세대_2022_2024",
"e_클래스_w213_2017_2020",
"4시리즈_f32_2014_2020",
"s_클래스_w222_2014_2020",
"그랜드_체로키_wl_2021_2023",
"모델_3_2024_2025",
"더_뉴_쏘렌토_4세대_2024_2025",
"a6_c8_2019_2025",
"3시리즈_e90_2005_2012",
"더_뉴_모닝_ja_2024_2025",
"더_뉴_말리부_2019_2022",
"트랙스_크로스오버_2024_2025",
"e_클래스_w214_2024_2025",
"뉴_체어맨_w_2012_2016",
"아테온_2018_2023",
"q7_4m_2020_2023",
"더_뉴_카니발_4세대_2024_2025",
"스팅어_마이스터_2021_2023",
"mkc_2015_2018",
"디스커버리_스포츠_2015_2019",
"더_뉴_코란도_스포츠_2016_2018",
"그랜저_hg_2015_2017",
"c_클래스_w205_2015_2021",
"cla_클래스_c117_2014_2019",
"벨로스터_js_2018_2020",
"k8_2022_2024",
"g70_2018_2020",
"s90_2017_2020",
"7시리즈_f01_2009_2015",
"마칸_2022_2024",
"7시리즈_g11_2019_2022",
"익스플로러_2016_2017",
"트랙스_2013_2016",
"코나_2018_2020",
"x4_g02_2019_2021",
"더_뉴_쏘렌토_2018_2020",
"더_뉴_코나_2021_2023",
"g90_rs4_2022_2025",
"k5_2세대_2016_2018",
"그랜저tg_2007_2008",
"아반떼_cn7_2021_2023",
"베뉴_2020_2024",
"올_뉴_모닝_ja_2017_2020",
"뉴_qm6_2021_2023",
"올_뉴_카니발_2015_2019",
"cls_클래스_c257_2019_2023",
"x1_f48_2020_2022",
"ct6_2016_2018",
"익스플로러_2018_2019",
"카니발_4세대_2022_2023",
"lf_쏘나타_2015_2017",
"더_뉴_아반떼_2014_2016",
"렉스턴_스포츠_2018_2021",
"스포티지_5세대_2022_2024",
"디스커버리_5_2022_2024",
"all_new_xj_2016_2019",
"qm6_2017_2019",
"rav4_5세대_2019_2024",
"파사트_gt_b8_2018_2022",
"x5_g05_2019_2023",
"쿠퍼_컨트리맨_2016_2024",
"타이칸_2021_2025",
"e_pace_2018_2020",
"cla_클래스_c118_2020_2025",
"x4_f26_2015_2018",
"알티마_2017_2018",
"gla_클래스_h247_2020_2025",
"디스커버리_4_2010_2016",
"프리우스_4세대_2016_2018",
"그랜드_체로키_2014_2020",
"911_2003_2019",
"임팔라_2016_2019",
"2008_2015_2017",
"amg_gt_2016_2024",
"cls_클래스_w218_2012_2017",
"코세어_2020_2022",
"그랜저_ig_2017_2019",
"a7_4k_2020_2024",
"디펜더_l663_2020_2025",
"c_클래스_w204_2008_2015",
"더_뉴_렉스턴_스포츠_칸_2021_2025",
"스토닉_2018_2020",
"gls_클래스_x166_2017_2019",
"카이엔_po536_2019_2023",
"glc_클래스_x253_2020_2022",
"더_뉴_트랙스_2017_2022",
"911_992_2020_2024",
"s60_3세대_2020_2024",
"5시리즈_f10_2010_2016",
"x3_g01_2018_2021",
"g4_렉스턴_2018_2020",
"올란도_2012_2018",
"xf_x260_2016_2020",
"디_올뉴싼타페_2024_2025",
"5시리즈_g30_2017_2023",
"f_pace_2017_2019",
"6시리즈_f12_2011_2018",
"골프_7세대_2013_2016",
"xe_2016_2019",
"파나메라_2010_2016",
"코나_sx2_2023_2025",
"엑센트_신형_2011_2019",
"xj_8세대_2010_2019",
"더_올뉴투싼_하이브리드_2021_2023",
"es300h_7세대_2019_2026",
"뉴_gv80_2024_2025",
"더_뉴_렉스턴_스포츠_2021_2025",
"a_클래스_w177_2020_2025",
"레인지로버_벨라_2018_2019",
"g80_2017_2020",
"레이_2012_2017",
"레인지로버_이보크_2세대_2020_2022",
"액티언_2세대_2025",
"3시리즈_g20_2023_2025",
"v60_크로스컨트리_2세대_2020_2025",
"스팅어_2018_2020",
"더_뉴_qm6_2020_2023",
"xm3_2024"
] |
plumpyfield/natix3
|
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ninja990621/natix
|
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afull05/n-model-001
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plumpyfield/natix-hot9
|
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plumpyfield/natix-hot1
|
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plumpyfield/natix-hot8
|
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plumpyfield/natix-hot20
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plumpyfield/natix-hot15
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plumpyfield/natix-hot27
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plumpyfield/natix-hot25
|
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plumpyfield/natix-hot33
|
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plumpyfield/natix-hot14
|
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plumpyfield/natix-hot10
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plumpyfield/natix-hot39
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plumpyfield/natix-hot35
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plumpyfield/natix-hot12
|
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plumpyfield/natix-hot47
|
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plumpyfield/natix-hot40
|
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plumpyfield/natix-hot22
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plumpyfield/natix-hot24
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plumpyfield/natix-hot50
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plumpyfield/natix-hot52
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plumpyfield/natix-hot55
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plumpyfield/natix-hot13
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plumpyfield/natix-hot6
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plumpyfield/natix-hot28
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plumpyfield/natix-hot53
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plumpyfield/natix-hot37
|
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plumpyfield/natix-hot30
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plumpyfield/natix-hot42
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plumpyfield/natix-hot26
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plumpyfield/natix-hot34
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plumpyfield/natix-hot23
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plumpyfield/natix-hot7
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plumpyfield/natix-hot31
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plumpyfield/natix-hot2
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plumpyfield/natix-hot44
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plumpyfield/natix-hot4
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plumpyfield/natix-hot3
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plumpyfield/natix-hot5
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plumpyfield/natix-hot18
|
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plumpyfield/natix-hot41
|
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plumpyfield/natix-hot48
|
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plumpyfield/natix-hot38
|
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"none",
"roadwork"
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plumpyfield/natix-hot17
|
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[
"none",
"roadwork"
] |
plumpyfield/natix-hot32
|
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[
"none",
"roadwork"
] |
plumpyfield/natix-hot49
|
# Model Card for Model ID
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[
"none",
"roadwork"
] |
plumpyfield/natix-hot19
|
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[
"none",
"roadwork"
] |
plumpyfield/natix-hot43
|
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[
"none",
"roadwork"
] |
plumpyfield/natix-hot29
|
# Model Card for Model ID
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[
"none",
"roadwork"
] |
plumpyfield/natix-hot46
|
# Model Card for Model ID
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[
"none",
"roadwork"
] |
plumpyfield/natix-hot54
|
# Model Card for Model ID
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plumpyfield/natix-hot51
|
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plumpyfield/natix-hot45
|
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plumpyfield/natix-hot16
|
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plumpyfield/natix-hot36
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plumpyfield/natix-hot11
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plumpyfield/natix-hot21
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[
"none",
"roadwork"
] |
James4u/sn72
|
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|
[
"none",
"roadwork"
] |
moonranger/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: 1.6419
- Accuracy: 0.887
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6766 | 1.0 | 63 | 2.5040 | 0.835 |
| 1.8227 | 2.0 | 126 | 1.7880 | 0.879 |
| 1.5865 | 3.0 | 189 | 1.6419 | 0.887 |
### 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"
] |
YumaVal/streetvision
|
# Model Card for Model ID
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|
[
"none",
"roadwork"
] |
AKumaaR03/streetvision
|
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[
"none",
"roadwork"
] |
AKumaaR004/streetvision
|
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[
"none",
"roadwork"
] |
KumarKhaan005/streetvision
|
# Model Card for Model ID
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JohnKumaaR006/streetvision
|
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KumaarJJ007/streetvision
|
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BootsofLagrangian/ortho-vit-b-imagenet1k-hf
|
# Model Card for OrthoViT-B ImageNet-1k
This model is a Vision Transformer (ViT-B) trained on [ImageNet-1k](https://huggingface.co/datasets/timm/imagenet-1k-wds), incorporating _Orthogonal Residual Updates_ as proposed in the paper [Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks](https://arxiv.org/abs/2505.11881). The core idea is to decompose a module's output relative to the input stream and add only the component orthogonal to this stream, aiming for richer feature learning and more efficient training.
This specific checkpoint was trained for approximately 90,000 steps (roughly 270 epochs out of a planned 300).
## Model Details
### Evaluation
_**Note:** Validation accuracy below is measured on checkpoint at step 90k (not the final model); results may differ slightly from those reported in the paper._
| Steps | Connection | Top-1 Accuracy (%) | Top-5 Accuracy (%) | Link |
|-------|-------------|--------------------|---------------------|------|
| 90k | Orthogonal | **74.62** | **92.26** | [here](https://huggingface.co/BootsofLagrangian/ortho-vit-b-imagenet1k-hf) |
| 90k | Linear | 71.23 | 90.29 | [link](https://huggingface.co/BootsofLagrangian/linear-vit-b-imagenet1k-hf) |
### Abstract
Residual connections are pivotal for deep neural networks, enabling greater depth by mitigating vanishing gradients. However, in standard residual updates, the module's output is directly added to the input stream. This can lead to updates that predominantly reinforce or modulate the existing stream direction, potentially underutilizing the module's capacity for learning entirely novel features. In this work, we introduce _Orthogonal Residual Update_: we decompose the module's output relative to the input stream and add only the component orthogonal to this stream. This design aims to guide modules to contribute primarily new representational directions, fostering richer feature learning while promoting more efficient training. We demonstrate that our orthogonal update strategy improves generalization accuracy and training stability across diverse architectures (ResNetV2, Vision Transformers) and datasets (CIFARs, TinyImageNet, ImageNet-1k), achieving, for instance, a +4.3\%p top-1 accuracy gain for ViT-B on ImageNet-1k.
### Method Overview
Our core idea is to modify the standard residual update $x_{n+1} = x_n + f(\sigma(x_n))$ by projecting out the component of $f(\sigma(x_n))$ that is parallel to $x_n$. The update then becomes $x_{n+1} = x_n + f_{\perp}(x_n)$, where $f_{\perp}(x_n)$ is the component of $f(\sigma(x_n))$ orthogonal to $x_n$.
*Figure 1: (Left) Standard residual update. (Right) Our Orthogonal Residual Update, which discards the parallel component $f_{||}$ and adds only the orthogonal component $f_{\perp}$.*
This approach aims to ensure that each module primarily contributes new information to the residual stream, enhancing representational diversity and mitigating potential interference from updates that merely rescale or oppose the existing stream.
### Key Results: Stable and Efficient Learning
Our Orthogonal Residual Update strategy leads to more stable training dynamics and improved learning efficiency. For example, models trained with our method often exhibit faster convergence to better generalization performance, as illustrated by comparative training curves.
*Figure 2: Example comparison (e.g., ViT-B on ImageNet-1k) showing Orthogonal Residual Update (blue) achieving lower training loss and higher validation accuracy in less wall-clock time compared to linear residual updates (red).*
### Model Sources
- **Repository (Original Implementation):** [https://github.com/BootsofLagrangian/ortho-residual](https://github.com/BootsofLagrangian/ortho-residual)
- **Paper:** [Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks (arXiv:2505.11881)](https://arxiv.org/abs/2505.11881)
## Evaluation
```python
import torch
import torchvision.transforms as transforms
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForImageClassification
from tqdm import tqdm
import argparse
from typing import Tuple, List
def accuracy_counts(
logits: torch.Tensor,
target: torch.Tensor,
topk: Tuple[int, ...] = (1, 5),
) -> List[int]:
"""
Given model outputs and targets, return a list of correct-counts
for each k in topk.
"""
maxk = max(topk)
_, pred = logits.topk(maxk, dim=1, largest=True, sorted=True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.item())
return res
def evaluate_model():
device = torch.device("cuda" if torch.cuda.is_available() and not args.cpu else "cpu")
print(f"Using device: {device}")
model = AutoModelForImageClassification.from_pretrained(
"BootsofLagrangian/ortho-vit-b-imagenet1k-hf",
trust_remote_code=True
)
model.to(device)
model.eval()
img_size = 224
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
transform_eval = transforms.Compose([
transforms.Lambda(lambda img: img.convert("RGB")),
transforms.Resize(img_size, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize(mean, std),
])
val_dataset = load_dataset("timm/imagenet-1k-wds", split="validation")
def collate_fn(batch):
images = torch.stack([transform_eval(item['jpg']) for item in batch])
labels = torch.tensor([item['cls'] for item in batch])
return images, labels
val_loader = DataLoader(
val_dataset,
batch_size=32,
shuffle=False,
num_workers=4,
collate_fn=collate_fn,
pin_memory=True
)
total_samples, correct_top1, correct_top5 = 0, 0, 0
with torch.no_grad():
for images, labels in tqdm(val_loader, desc="Evaluating"):
images = images.to(device)
labels = labels.to(device)
outputs = model(pixel_values=images)
logits = outputs.logits
counts = accuracy_counts(logits, labels, topk=(1, 5))
correct_top1 += counts[0]
correct_top5 += counts[1]
total_samples += images.size(0)
top1_accuracy = (correct_top1 / total_samples) * 100
top5_accuracy = (correct_top5 / total_samples) * 100
print("\n--- Evaluation Results ---")
print(f"Total samples evaluated: {total_samples}")
print(f"Top-1 Accuracy: {top1_accuracy:.2f}%")
print(f"Top-5 Accuracy: {top5_accuracy:.2f}%")
```
## Citation
```bib
@article{oh2025revisitingresidualconnectionsorthogonal,
title={Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks},
author={Giyeong Oh and Woohyun Cho and Siyeol Kim and Suhwan Choi and Younjae Yu},
year={2025},
journal={arXiv preprint arXiv:2505.11881},
eprint={2505.11881},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.11881}
}
```
|
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rg6693/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: 1.5739
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6447 | 1.0 | 63 | 2.4853 | 0.812 |
| 1.7882 | 2.0 | 126 | 1.7672 | 0.865 |
| 1.5327 | 3.0 | 189 | 1.5739 | 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"
] |
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