vit-base-kidney-stone-Michel_Daudon_-w256_1k_v1-_MIX
Browse files- README.md +103 -16
- all_results.json +14 -14
- model.safetensors +1 -1
- test_results.json +9 -9
- train_results.json +6 -6
- trainer_state.json +0 -0
- training_args.bin +1 -1
README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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- name: Precision
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type: precision
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cv-inside/vit-base-kidney-stone/runs/
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# vit-base-kidney-stone-Michel_Daudon_-w256_1k_v1-_MIX
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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## Model description
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- seed: 42
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- optimizer: Use 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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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch
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### Framework versions
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8870833333333333
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- name: Precision
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type: precision
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value: 0.8988360882885232
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- name: Recall
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type: recall
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value: 0.8870833333333333
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- name: F1
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type: f1
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value: 0.8880432263296901
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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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+
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cv-inside/vit-base-kidney-stone/runs/bhdwgbgx)
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# vit-base-kidney-stone-Michel_Daudon_-w256_1k_v1-_MIX
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4892
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- Accuracy: 0.8871
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- Precision: 0.8988
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- Recall: 0.8871
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- F1: 0.8880
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## Model description
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- seed: 42
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- optimizer: Use 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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- 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 | Precision | Recall | F1 |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.3419 | 0.3333 | 100 | 0.5920 | 0.8104 | 0.8329 | 0.8104 | 0.8037 |
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| 0.1693 | 0.6667 | 200 | 0.6791 | 0.8054 | 0.8274 | 0.8054 | 0.8085 |
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| 0.1732 | 1.0 | 300 | 0.7756 | 0.7979 | 0.8415 | 0.7979 | 0.7981 |
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| 0.0691 | 1.3333 | 400 | 0.7158 | 0.8158 | 0.8508 | 0.8158 | 0.8188 |
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| 0.0714 | 1.6667 | 500 | 0.7522 | 0.8317 | 0.8499 | 0.8317 | 0.8266 |
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| 0.0673 | 2.0 | 600 | 0.5385 | 0.8621 | 0.8655 | 0.8621 | 0.8598 |
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| 0.0655 | 2.3333 | 700 | 0.7799 | 0.8433 | 0.8497 | 0.8433 | 0.8359 |
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| 0.0135 | 2.6667 | 800 | 0.6978 | 0.8396 | 0.8529 | 0.8396 | 0.8413 |
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| 0.0075 | 3.0 | 900 | 1.0180 | 0.8104 | 0.8370 | 0.8104 | 0.8161 |
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| 0.0338 | 3.3333 | 1000 | 0.7638 | 0.8429 | 0.8601 | 0.8429 | 0.8422 |
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| 0.0488 | 3.6667 | 1100 | 1.0401 | 0.7983 | 0.8228 | 0.7983 | 0.7986 |
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| 0.0794 | 4.0 | 1200 | 0.7388 | 0.8496 | 0.8497 | 0.8496 | 0.8481 |
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| 0.0034 | 4.3333 | 1300 | 0.9749 | 0.8279 | 0.8427 | 0.8279 | 0.8252 |
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| 0.0276 | 4.6667 | 1400 | 1.1395 | 0.8067 | 0.8351 | 0.8067 | 0.8116 |
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| 0.0855 | 5.0 | 1500 | 0.6391 | 0.8729 | 0.8860 | 0.8729 | 0.8763 |
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| 0.0256 | 5.3333 | 1600 | 1.0149 | 0.8108 | 0.8289 | 0.8108 | 0.8105 |
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| 0.0017 | 5.6667 | 1700 | 0.9153 | 0.8279 | 0.8575 | 0.8279 | 0.8299 |
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| 0.0393 | 6.0 | 1800 | 1.0392 | 0.8175 | 0.8205 | 0.8175 | 0.8169 |
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| 0.0031 | 6.3333 | 1900 | 0.4892 | 0.8871 | 0.8988 | 0.8871 | 0.8880 |
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| 0.1446 | 6.6667 | 2000 | 0.8977 | 0.8187 | 0.8362 | 0.8187 | 0.8177 |
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| 0.0176 | 7.0 | 2100 | 0.6661 | 0.8608 | 0.8756 | 0.8608 | 0.8637 |
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| 0.0312 | 7.3333 | 2200 | 0.7722 | 0.8408 | 0.8520 | 0.8408 | 0.8412 |
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| 0.0018 | 7.6667 | 2300 | 0.8194 | 0.8483 | 0.8641 | 0.8483 | 0.8453 |
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| 0.0008 | 8.0 | 2400 | 0.7871 | 0.8571 | 0.8752 | 0.8571 | 0.8535 |
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| 0.0033 | 8.3333 | 2500 | 0.9942 | 0.8258 | 0.8480 | 0.8258 | 0.8220 |
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| 0.0017 | 8.6667 | 2600 | 1.1084 | 0.8175 | 0.8562 | 0.8175 | 0.8187 |
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| 0.0672 | 9.0 | 2700 | 0.8912 | 0.8438 | 0.8734 | 0.8438 | 0.8445 |
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| 0.0227 | 9.3333 | 2800 | 1.1547 | 0.8113 | 0.8295 | 0.8113 | 0.8086 |
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| 0.0012 | 9.6667 | 2900 | 1.1734 | 0.8154 | 0.8369 | 0.8154 | 0.8129 |
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| 0.0011 | 10.0 | 3000 | 0.9762 | 0.8542 | 0.8800 | 0.8542 | 0.8558 |
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| 0.0006 | 10.3333 | 3100 | 1.0484 | 0.8433 | 0.8707 | 0.8433 | 0.8447 |
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| 0.0291 | 10.6667 | 3200 | 0.7566 | 0.8475 | 0.8606 | 0.8475 | 0.8473 |
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| 0.0381 | 11.0 | 3300 | 0.8845 | 0.8496 | 0.8736 | 0.8496 | 0.8499 |
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| 0.0004 | 11.3333 | 3400 | 0.5031 | 0.8767 | 0.8904 | 0.8767 | 0.8796 |
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| 0.0237 | 11.6667 | 3500 | 0.7363 | 0.8438 | 0.8639 | 0.8438 | 0.8497 |
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| 0.0091 | 12.0 | 3600 | 0.8048 | 0.84 | 0.8455 | 0.84 | 0.8418 |
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| 0.0161 | 12.3333 | 3700 | 0.8593 | 0.8333 | 0.8518 | 0.8333 | 0.8377 |
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| 0.0389 | 12.6667 | 3800 | 1.0442 | 0.8275 | 0.8661 | 0.8275 | 0.8350 |
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| 0.0003 | 13.0 | 3900 | 0.9752 | 0.8329 | 0.8535 | 0.8329 | 0.8382 |
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| 0.0003 | 13.3333 | 4000 | 0.8313 | 0.8521 | 0.8735 | 0.8521 | 0.8564 |
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| 0.0003 | 13.6667 | 4100 | 1.4003 | 0.7887 | 0.8193 | 0.7887 | 0.7881 |
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| 0.0007 | 14.0 | 4200 | 1.1201 | 0.8171 | 0.8392 | 0.8171 | 0.8205 |
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| 0.0002 | 14.3333 | 4300 | 1.0160 | 0.8413 | 0.8667 | 0.8413 | 0.8428 |
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| 0.0002 | 14.6667 | 4400 | 1.0599 | 0.8271 | 0.8464 | 0.8271 | 0.8282 |
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| 0.0002 | 15.0 | 4500 | 1.0467 | 0.8358 | 0.8645 | 0.8358 | 0.8385 |
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| 0.0002 | 15.3333 | 4600 | 0.9069 | 0.8421 | 0.8616 | 0.8421 | 0.8454 |
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| 0.0002 | 15.6667 | 4700 | 0.9158 | 0.845 | 0.8646 | 0.845 | 0.8483 |
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| 0.0002 | 16.0 | 4800 | 0.9191 | 0.8471 | 0.8670 | 0.8471 | 0.8504 |
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| 0.0001 | 16.3333 | 4900 | 0.9290 | 0.845 | 0.8647 | 0.845 | 0.8483 |
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| 0.0001 | 16.6667 | 5000 | 0.9366 | 0.8471 | 0.8663 | 0.8471 | 0.8502 |
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| 0.0001 | 17.0 | 5100 | 0.9468 | 0.8471 | 0.8663 | 0.8471 | 0.8502 |
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| 0.0001 | 17.3333 | 5200 | 0.9553 | 0.8475 | 0.8665 | 0.8475 | 0.8506 |
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| 0.0001 | 17.6667 | 5300 | 0.9640 | 0.8467 | 0.8666 | 0.8467 | 0.8498 |
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| 0.0001 | 18.0 | 5400 | 0.9722 | 0.8462 | 0.8662 | 0.8462 | 0.8494 |
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| 0.0001 | 18.3333 | 5500 | 0.9799 | 0.8462 | 0.8664 | 0.8462 | 0.8494 |
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| 0.0001 | 18.6667 | 5600 | 0.9872 | 0.8467 | 0.8667 | 0.8467 | 0.8498 |
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| 0.0001 | 19.0 | 5700 | 0.9936 | 0.8467 | 0.8667 | 0.8467 | 0.8498 |
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| 0.0001 | 19.3333 | 5800 | 0.9997 | 0.8467 | 0.8667 | 0.8467 | 0.8498 |
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| 0.0001 | 19.6667 | 5900 | 1.0062 | 0.8467 | 0.8667 | 0.8467 | 0.8498 |
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| 0.0001 | 20.0 | 6000 | 1.0122 | 0.8462 | 0.8663 | 0.8462 | 0.8493 |
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| 0.0001 | 20.3333 | 6100 | 1.0177 | 0.8462 | 0.8663 | 0.8462 | 0.8493 |
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| 0.0001 | 20.6667 | 6200 | 1.0232 | 0.8467 | 0.8667 | 0.8467 | 0.8498 |
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| 0.0001 | 21.0 | 6300 | 1.0291 | 0.8471 | 0.8672 | 0.8471 | 0.8502 |
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| 0.0001 | 21.3333 | 6400 | 1.0342 | 0.8475 | 0.8678 | 0.8475 | 0.8506 |
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| 0.0001 | 21.6667 | 6500 | 1.0392 | 0.8471 | 0.8675 | 0.8471 | 0.8502 |
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| 0.0001 | 22.0 | 6600 | 1.0442 | 0.8467 | 0.8674 | 0.8467 | 0.8499 |
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| 0.0001 | 22.3333 | 6700 | 1.0487 | 0.8467 | 0.8674 | 0.8467 | 0.8499 |
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| 0.0001 | 22.6667 | 6800 | 1.0533 | 0.8467 | 0.8674 | 0.8467 | 0.8499 |
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| 0.0001 | 23.0 | 6900 | 1.0578 | 0.8471 | 0.8677 | 0.8471 | 0.8503 |
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| 0.0001 | 23.3333 | 7000 | 1.0623 | 0.8471 | 0.8682 | 0.8471 | 0.8504 |
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| 0.0001 | 23.6667 | 7100 | 1.0661 | 0.8467 | 0.8680 | 0.8467 | 0.8500 |
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| 0.0001 | 24.0 | 7200 | 1.0701 | 0.8467 | 0.8680 | 0.8467 | 0.8500 |
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| 0.0001 | 24.3333 | 7300 | 1.0740 | 0.8467 | 0.8680 | 0.8467 | 0.8500 |
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| 0.0 | 24.6667 | 7400 | 1.0775 | 0.8467 | 0.8678 | 0.8467 | 0.8499 |
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| 0.0 | 25.0 | 7500 | 1.0810 | 0.8467 | 0.8678 | 0.8467 | 0.8499 |
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| 0.0 | 25.3333 | 7600 | 1.0841 | 0.8467 | 0.8676 | 0.8467 | 0.8499 |
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| 0.0 | 25.6667 | 7700 | 1.0872 | 0.8467 | 0.8678 | 0.8467 | 0.8499 |
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| 0.0 | 26.0 | 7800 | 1.0904 | 0.8467 | 0.8678 | 0.8467 | 0.8499 |
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| 0.0 | 26.3333 | 7900 | 1.0937 | 0.8467 | 0.8678 | 0.8467 | 0.8499 |
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| 0.0 | 26.6667 | 8000 | 1.0964 | 0.8467 | 0.8678 | 0.8467 | 0.8499 |
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| 0.0 | 27.0 | 8100 | 1.0986 | 0.8467 | 0.8678 | 0.8467 | 0.8499 |
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| 0.0 | 27.3333 | 8200 | 1.1008 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 27.6667 | 8300 | 1.1030 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 28.0 | 8400 | 1.1049 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 28.3333 | 8500 | 1.1066 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 28.6667 | 8600 | 1.1078 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 29.0 | 8700 | 1.1090 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 29.3333 | 8800 | 1.1098 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 29.6667 | 8900 | 1.1103 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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| 0.0 | 30.0 | 9000 | 1.1105 | 0.8462 | 0.8675 | 0.8462 | 0.8496 |
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### Framework versions
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all_results.json
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{
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"epoch":
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"eval_accuracy": 0.
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"eval_f1": 0.
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"eval_loss": 0.
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"eval_precision": 0.
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"eval_recall": 0.
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"eval_runtime":
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"eval_samples_per_second":
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"eval_steps_per_second":
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"total_flos":
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"train_loss": 0.
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"train_runtime":
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"train_samples_per_second":
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"train_steps_per_second":
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}
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{
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"epoch": 30.0,
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"eval_accuracy": 0.8870833333333333,
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"eval_f1": 0.8880432263296901,
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"eval_loss": 0.4892176389694214,
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"eval_precision": 0.8988360882885232,
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"eval_recall": 0.8870833333333333,
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"eval_runtime": 16.7314,
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"eval_samples_per_second": 143.443,
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"eval_steps_per_second": 17.93,
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"total_flos": 2.231849311469568e+19,
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"train_loss": 0.02316958835389879,
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"train_runtime": 4210.125,
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"train_samples_per_second": 68.407,
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"train_steps_per_second": 2.138
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 343236280
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:14787cb320225db08f3922079550b8f4c3f0d732808d55ba33a5f6070bc03b1f
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size 343236280
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test_results.json
CHANGED
|
@@ -1,11 +1,11 @@
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|
| 1 |
{
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| 2 |
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| 3 |
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| 5 |
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| 8 |
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| 9 |
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"eval_samples_per_second":
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| 10 |
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"eval_steps_per_second":
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| 11 |
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|
| 1 |
{
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| 2 |
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"epoch": 30.0,
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| 3 |
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| 4 |
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"eval_f1": 0.8880432263296901,
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| 5 |
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"eval_loss": 0.4892176389694214,
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"eval_samples_per_second": 143.443,
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| 10 |
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"eval_steps_per_second": 17.93
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| 11 |
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train_results.json
CHANGED
|
@@ -1,8 +1,8 @@
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|
| 1 |
{
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| 2 |
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"epoch":
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| 3 |
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| 7 |
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"train_steps_per_second":
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| 8 |
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|
|
|
| 1 |
{
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| 2 |
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"epoch": 30.0,
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| 3 |
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"total_flos": 2.231849311469568e+19,
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"train_samples_per_second": 68.407,
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| 7 |
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"train_steps_per_second": 2.138
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| 8 |
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trainer_state.json
CHANGED
|
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|
|
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:
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| 3 |
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| 1 |
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size 5432
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