| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224-in21k |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: vit-base-patch16-224-in21k-finetuned-inaturalist |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: validation |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.8541666666666666 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # vit-base-patch16-224-in21k-finetuned-inaturalist |
|
|
| 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 inaturalist dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7703 |
| - Accuracy: 0.8542 |
|
|
| ## 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: 128 |
| - eval_batch_size: 128 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 512 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 100 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-------:|:----:|:---------------:|:--------:| |
| | No log | 0.8421 | 4 | 3.1793 | 0.0347 | |
| | No log | 1.8947 | 9 | 3.1647 | 0.0486 | |
| | 3.1648 | 2.9474 | 14 | 3.1382 | 0.0944 | |
| | 3.1648 | 4.0 | 19 | 3.0995 | 0.1556 | |
| | 3.0817 | 4.8421 | 23 | 3.0555 | 0.2639 | |
| | 3.0817 | 5.8947 | 28 | 2.9849 | 0.3889 | |
| | 2.9167 | 6.9474 | 33 | 2.8932 | 0.5139 | |
| | 2.9167 | 8.0 | 38 | 2.7775 | 0.5972 | |
| | 2.6682 | 8.8421 | 42 | 2.6706 | 0.6528 | |
| | 2.6682 | 9.8947 | 47 | 2.5233 | 0.7069 | |
| | 2.3659 | 10.9474 | 52 | 2.3859 | 0.7375 | |
| | 2.3659 | 12.0 | 57 | 2.2546 | 0.75 | |
| | 2.079 | 12.8421 | 61 | 2.1531 | 0.7528 | |
| | 2.079 | 13.8947 | 66 | 2.0372 | 0.75 | |
| | 1.828 | 14.9474 | 71 | 1.9339 | 0.7597 | |
| | 1.828 | 16.0 | 76 | 1.8403 | 0.7694 | |
| | 1.6253 | 16.8421 | 80 | 1.7733 | 0.7764 | |
| | 1.6253 | 17.8947 | 85 | 1.6914 | 0.7903 | |
| | 1.4502 | 18.9474 | 90 | 1.6153 | 0.7875 | |
| | 1.4502 | 20.0 | 95 | 1.5510 | 0.7986 | |
| | 1.4502 | 20.8421 | 99 | 1.5016 | 0.8 | |
| | 1.2959 | 21.8947 | 104 | 1.4454 | 0.8222 | |
| | 1.2959 | 22.9474 | 109 | 1.3912 | 0.8181 | |
| | 1.1802 | 24.0 | 114 | 1.3390 | 0.8333 | |
| | 1.1802 | 24.8421 | 118 | 1.2995 | 0.8333 | |
| | 1.0629 | 25.8947 | 123 | 1.2707 | 0.8389 | |
| | 1.0629 | 26.9474 | 128 | 1.2335 | 0.8361 | |
| | 0.9801 | 28.0 | 133 | 1.1975 | 0.8444 | |
| | 0.9801 | 28.8421 | 137 | 1.1672 | 0.8389 | |
| | 0.9076 | 29.8947 | 142 | 1.1338 | 0.8444 | |
| | 0.9076 | 30.9474 | 147 | 1.1137 | 0.8472 | |
| | 0.8349 | 32.0 | 152 | 1.0855 | 0.8528 | |
| | 0.8349 | 32.8421 | 156 | 1.0717 | 0.8542 | |
| | 0.7782 | 33.8947 | 161 | 1.0483 | 0.8514 | |
| | 0.7782 | 34.9474 | 166 | 1.0352 | 0.85 | |
| | 0.7208 | 36.0 | 171 | 1.0202 | 0.8556 | |
| | 0.7208 | 36.8421 | 175 | 0.9994 | 0.8486 | |
| | 0.6708 | 37.8947 | 180 | 0.9814 | 0.8556 | |
| | 0.6708 | 38.9474 | 185 | 0.9691 | 0.8542 | |
| | 0.6303 | 40.0 | 190 | 0.9599 | 0.8486 | |
| | 0.6303 | 40.8421 | 194 | 0.9422 | 0.8472 | |
| | 0.6303 | 41.8947 | 199 | 0.9278 | 0.8486 | |
| | 0.6018 | 42.9474 | 204 | 0.9172 | 0.8528 | |
| | 0.6018 | 44.0 | 209 | 0.9093 | 0.8514 | |
| | 0.5622 | 44.8421 | 213 | 0.9030 | 0.8583 | |
| | 0.5622 | 45.8947 | 218 | 0.8972 | 0.8625 | |
| | 0.5474 | 46.9474 | 223 | 0.8859 | 0.8569 | |
| | 0.5474 | 48.0 | 228 | 0.8858 | 0.8653 | |
| | 0.5254 | 48.8421 | 232 | 0.8779 | 0.8556 | |
| | 0.5254 | 49.8947 | 237 | 0.8635 | 0.8569 | |
| | 0.5036 | 50.9474 | 242 | 0.8563 | 0.8611 | |
| | 0.5036 | 52.0 | 247 | 0.8613 | 0.8542 | |
| | 0.4855 | 52.8421 | 251 | 0.8546 | 0.8625 | |
| | 0.4855 | 53.8947 | 256 | 0.8469 | 0.8597 | |
| | 0.4697 | 54.9474 | 261 | 0.8327 | 0.8528 | |
| | 0.4697 | 56.0 | 266 | 0.8268 | 0.8597 | |
| | 0.4482 | 56.8421 | 270 | 0.8188 | 0.8556 | |
| | 0.4482 | 57.8947 | 275 | 0.8171 | 0.8653 | |
| | 0.4436 | 58.9474 | 280 | 0.8133 | 0.8486 | |
| | 0.4436 | 60.0 | 285 | 0.8070 | 0.8639 | |
| | 0.4436 | 60.8421 | 289 | 0.7986 | 0.8542 | |
| | 0.4211 | 61.8947 | 294 | 0.7937 | 0.8597 | |
| | 0.4211 | 62.9474 | 299 | 0.7908 | 0.8611 | |
| | 0.4228 | 64.0 | 304 | 0.7952 | 0.8625 | |
| | 0.4228 | 64.8421 | 308 | 0.8010 | 0.8514 | |
| | 0.4046 | 65.8947 | 313 | 0.7975 | 0.8472 | |
| | 0.4046 | 66.9474 | 318 | 0.7927 | 0.8417 | |
| | 0.4048 | 68.0 | 323 | 0.7880 | 0.8556 | |
| | 0.4048 | 68.8421 | 327 | 0.7860 | 0.8514 | |
| | 0.3925 | 69.8947 | 332 | 0.7899 | 0.8403 | |
| | 0.3925 | 70.9474 | 337 | 0.7883 | 0.8417 | |
| | 0.3936 | 72.0 | 342 | 0.7885 | 0.8417 | |
| | 0.3936 | 72.8421 | 346 | 0.7874 | 0.8361 | |
| | 0.3985 | 73.8947 | 351 | 0.7832 | 0.8417 | |
| | 0.3985 | 74.9474 | 356 | 0.7787 | 0.8514 | |
| | 0.3849 | 76.0 | 361 | 0.7753 | 0.8486 | |
| | 0.3849 | 76.8421 | 365 | 0.7746 | 0.8514 | |
| | 0.3796 | 77.8947 | 370 | 0.7736 | 0.8542 | |
| | 0.3796 | 78.9474 | 375 | 0.7731 | 0.8528 | |
| | 0.3717 | 80.0 | 380 | 0.7715 | 0.8556 | |
| | 0.3717 | 80.8421 | 384 | 0.7709 | 0.8556 | |
| | 0.3717 | 81.8947 | 389 | 0.7706 | 0.8569 | |
| | 0.3802 | 82.9474 | 394 | 0.7704 | 0.8556 | |
| | 0.3802 | 84.0 | 399 | 0.7704 | 0.8542 | |
| | 0.3782 | 84.2105 | 400 | 0.7703 | 0.8542 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.45.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.20.1 |
|
|
