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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- webdataset |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: vit-base-patch16-224-in21k-v2025-1-31 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: webdataset |
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type: webdataset |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8972972972972973 |
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- name: F1 |
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type: f1 |
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value: 0.7667958656330749 |
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- name: Precision |
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type: precision |
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value: 0.7866136514247847 |
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- name: Recall |
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type: recall |
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value: 0.7479521109010712 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vit-base-patch16-224-in21k-v2025-1-31 |
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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 webdataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3391 |
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- Accuracy: 0.8973 |
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- F1: 0.7668 |
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- Precision: 0.7866 |
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- Recall: 0.7480 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.4871 | 0.5682 | 100 | 0.4866 | 0.7903 | 0.1400 | 0.9449 | 0.0756 | |
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| 0.4151 | 1.1364 | 200 | 0.4007 | 0.8361 | 0.4540 | 0.9159 | 0.3018 | |
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| 0.3517 | 1.7045 | 300 | 0.3460 | 0.8671 | 0.6481 | 0.8060 | 0.5419 | |
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| 0.3337 | 2.2727 | 400 | 0.3202 | 0.8777 | 0.7034 | 0.7768 | 0.6427 | |
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| 0.3128 | 2.8409 | 500 | 0.2995 | 0.8774 | 0.6943 | 0.7940 | 0.6169 | |
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| 0.3199 | 3.4091 | 600 | 0.2980 | 0.8771 | 0.6960 | 0.7880 | 0.6232 | |
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| 0.3094 | 3.9773 | 700 | 0.3051 | 0.8764 | 0.7031 | 0.7679 | 0.6484 | |
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| 0.3068 | 4.5455 | 800 | 0.2753 | 0.8900 | 0.7409 | 0.7915 | 0.6963 | |
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| 0.3003 | 5.1136 | 900 | 0.2699 | 0.8890 | 0.7351 | 0.7973 | 0.6818 | |
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| 0.3012 | 5.6818 | 1000 | 0.2860 | 0.8799 | 0.7256 | 0.7495 | 0.7032 | |
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| 0.267 | 6.25 | 1100 | 0.2848 | 0.8832 | 0.7216 | 0.7812 | 0.6704 | |
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| 0.2364 | 6.8182 | 1200 | 0.2608 | 0.8896 | 0.7399 | 0.7903 | 0.6957 | |
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| 0.2401 | 7.3864 | 1300 | 0.2695 | 0.8885 | 0.7406 | 0.7798 | 0.7051 | |
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| 0.219 | 7.9545 | 1400 | 0.2599 | 0.8909 | 0.7413 | 0.7975 | 0.6925 | |
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| 0.1985 | 8.5227 | 1500 | 0.2668 | 0.8898 | 0.7421 | 0.7863 | 0.7026 | |
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| 0.1986 | 9.0909 | 1600 | 0.2762 | 0.8851 | 0.7316 | 0.7737 | 0.6938 | |
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| 0.1988 | 9.6591 | 1700 | 0.2765 | 0.8862 | 0.7404 | 0.7632 | 0.7190 | |
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| 0.167 | 10.2273 | 1800 | 0.2630 | 0.8940 | 0.7594 | 0.7788 | 0.7410 | |
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| 0.207 | 10.7955 | 1900 | 0.2637 | 0.8923 | 0.7557 | 0.7745 | 0.7379 | |
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| 0.1811 | 11.3636 | 2000 | 0.2568 | 0.8946 | 0.7609 | 0.7798 | 0.7429 | |
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| 0.171 | 11.9318 | 2100 | 0.2607 | 0.8935 | 0.7527 | 0.7906 | 0.7183 | |
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| 0.1571 | 12.5 | 2200 | 0.2552 | 0.8972 | 0.7708 | 0.7755 | 0.7662 | |
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| 0.1234 | 13.0682 | 2300 | 0.2676 | 0.8993 | 0.7694 | 0.7964 | 0.7442 | |
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| 0.1299 | 13.6364 | 2400 | 0.2683 | 0.8970 | 0.7655 | 0.7875 | 0.7448 | |
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| 0.1335 | 14.2045 | 2500 | 0.2823 | 0.8949 | 0.7559 | 0.7944 | 0.7209 | |
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| 0.1235 | 14.7727 | 2600 | 0.2753 | 0.8976 | 0.7671 | 0.7880 | 0.7473 | |
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| 0.1163 | 15.3409 | 2700 | 0.2884 | 0.8962 | 0.7644 | 0.7836 | 0.7461 | |
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| 0.1111 | 15.9091 | 2800 | 0.2770 | 0.8973 | 0.7675 | 0.7847 | 0.7511 | |
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| 0.1128 | 16.4773 | 2900 | 0.2773 | 0.8987 | 0.7722 | 0.7843 | 0.7606 | |
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| 0.0982 | 17.0455 | 3000 | 0.2754 | 0.8993 | 0.7716 | 0.7905 | 0.7536 | |
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| 0.1115 | 17.6136 | 3100 | 0.2956 | 0.8972 | 0.7640 | 0.7927 | 0.7372 | |
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| 0.07 | 18.1818 | 3200 | 0.2961 | 0.8977 | 0.7683 | 0.7863 | 0.7511 | |
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| 0.0993 | 18.75 | 3300 | 0.3041 | 0.8959 | 0.7639 | 0.7826 | 0.7461 | |
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| 0.0779 | 19.3182 | 3400 | 0.3012 | 0.9 | 0.7745 | 0.7889 | 0.7606 | |
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| 0.0691 | 19.8864 | 3500 | 0.3075 | 0.8964 | 0.7674 | 0.7784 | 0.7568 | |
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| 0.063 | 20.4545 | 3600 | 0.3271 | 0.8912 | 0.7509 | 0.7770 | 0.7265 | |
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| 0.0668 | 21.0227 | 3700 | 0.3229 | 0.8952 | 0.7649 | 0.7745 | 0.7555 | |
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| 0.0573 | 21.5909 | 3800 | 0.3236 | 0.8960 | 0.7626 | 0.7869 | 0.7398 | |
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| 0.0668 | 22.1591 | 3900 | 0.3251 | 0.8972 | 0.7629 | 0.7955 | 0.7328 | |
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| 0.062 | 22.7273 | 4000 | 0.3221 | 0.8987 | 0.7702 | 0.7895 | 0.7517 | |
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| 0.0647 | 23.2955 | 4100 | 0.3179 | 0.8959 | 0.7663 | 0.7767 | 0.7561 | |
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| 0.0417 | 23.8636 | 4200 | 0.3323 | 0.8969 | 0.7662 | 0.7847 | 0.7486 | |
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| 0.0623 | 24.4318 | 4300 | 0.3396 | 0.8945 | 0.7602 | 0.7804 | 0.7410 | |
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| 0.0361 | 25.0 | 4400 | 0.3418 | 0.8959 | 0.7623 | 0.7863 | 0.7398 | |
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| 0.0334 | 25.5682 | 4500 | 0.3404 | 0.8984 | 0.7703 | 0.7870 | 0.7543 | |
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| 0.0326 | 26.1364 | 4600 | 0.3376 | 0.8967 | 0.7676 | 0.7801 | 0.7555 | |
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| 0.052 | 26.7045 | 4700 | 0.3395 | 0.8972 | 0.7679 | 0.7827 | 0.7536 | |
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| 0.0341 | 27.2727 | 4800 | 0.3440 | 0.8953 | 0.7638 | 0.7783 | 0.7498 | |
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| 0.0459 | 27.8409 | 4900 | 0.3406 | 0.8980 | 0.7689 | 0.7869 | 0.7517 | |
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| 0.0392 | 28.4091 | 5000 | 0.3389 | 0.8977 | 0.7680 | 0.7870 | 0.7498 | |
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| 0.0407 | 28.9773 | 5100 | 0.3410 | 0.8976 | 0.7677 | 0.7865 | 0.7498 | |
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| 0.0445 | 29.5455 | 5200 | 0.3395 | 0.8969 | 0.7661 | 0.7851 | 0.7480 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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