vit-base-kidney-stone-Michel_Daudon_-w256_1k_v1-_MIX
Browse files- README.md +55 -101
- 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/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.
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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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@@ -75,103 +74,58 @@ The following hyperparameters were used during training:
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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 | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.0002 | 14.3333 | 4300 | 1.
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| 0.0002 | 15.0 | 4500 | 1.
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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 | 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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metrics:
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- name: Accuracy
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type: accuracy
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+
value: 0.83375
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- name: Precision
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type: precision
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value: 0.8588680878951838
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- name: Recall
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type: recall
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value: 0.83375
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- name: F1
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type: f1
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+
value: 0.8355968544321966
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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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|
|
|
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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: 0.4940
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+
- Accuracy: 0.8337
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+
- Precision: 0.8589
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+
- Recall: 0.8337
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- F1: 0.8356
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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: 15
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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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| 0.1919 | 0.3333 | 100 | 0.4940 | 0.8337 | 0.8589 | 0.8337 | 0.8356 |
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| 0.1697 | 0.6667 | 200 | 0.6993 | 0.8092 | 0.8485 | 0.8092 | 0.8059 |
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| 0.1514 | 1.0 | 300 | 0.5555 | 0.8442 | 0.8565 | 0.8442 | 0.8443 |
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| 0.0991 | 1.3333 | 400 | 0.5918 | 0.8467 | 0.8741 | 0.8467 | 0.8453 |
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| 0.0415 | 1.6667 | 500 | 0.6080 | 0.8558 | 0.8690 | 0.8558 | 0.8553 |
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| 0.1112 | 2.0 | 600 | 0.9788 | 0.7983 | 0.8485 | 0.7983 | 0.8028 |
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| 0.0658 | 2.3333 | 700 | 1.0272 | 0.8004 | 0.8310 | 0.8004 | 0.8002 |
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| 0.0977 | 2.6667 | 800 | 0.6861 | 0.8479 | 0.8570 | 0.8479 | 0.8482 |
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| 0.03 | 3.0 | 900 | 0.8317 | 0.8025 | 0.8225 | 0.8025 | 0.8048 |
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| 0.0253 | 3.3333 | 1000 | 0.8574 | 0.8242 | 0.8408 | 0.8242 | 0.8254 |
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| 0.0564 | 3.6667 | 1100 | 0.8591 | 0.8392 | 0.8513 | 0.8392 | 0.8343 |
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| 0.0285 | 4.0 | 1200 | 1.3453 | 0.7512 | 0.8090 | 0.7512 | 0.7484 |
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| 0.002 | 4.3333 | 1300 | 0.9746 | 0.8192 | 0.8381 | 0.8192 | 0.8227 |
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| 0.0214 | 4.6667 | 1400 | 0.7404 | 0.8646 | 0.8641 | 0.8646 | 0.8572 |
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| 0.0282 | 5.0 | 1500 | 1.0063 | 0.8233 | 0.8486 | 0.8233 | 0.8219 |
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| 0.03 | 5.3333 | 1600 | 1.0066 | 0.8025 | 0.8376 | 0.8025 | 0.8058 |
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| 0.028 | 5.6667 | 1700 | 1.1451 | 0.8108 | 0.8325 | 0.8108 | 0.8067 |
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| 0.0078 | 6.0 | 1800 | 1.0700 | 0.805 | 0.8220 | 0.805 | 0.8045 |
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| 0.0008 | 6.3333 | 1900 | 1.0180 | 0.8146 | 0.8303 | 0.8146 | 0.8165 |
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| 0.0008 | 6.6667 | 2000 | 0.9882 | 0.8246 | 0.8401 | 0.8246 | 0.8236 |
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| 0.0006 | 7.0 | 2100 | 1.0366 | 0.8283 | 0.8424 | 0.8283 | 0.8270 |
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| 0.0009 | 7.3333 | 2200 | 1.1136 | 0.8121 | 0.8309 | 0.8121 | 0.8143 |
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| 0.0068 | 7.6667 | 2300 | 1.0873 | 0.8117 | 0.8128 | 0.8117 | 0.8015 |
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| 0.0006 | 8.0 | 2400 | 0.8601 | 0.8325 | 0.8383 | 0.8325 | 0.8292 |
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| 0.0187 | 8.3333 | 2500 | 0.9700 | 0.8258 | 0.8375 | 0.8258 | 0.8241 |
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| 0.0005 | 8.6667 | 2600 | 0.8825 | 0.8175 | 0.8339 | 0.8175 | 0.8199 |
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| 0.0005 | 9.0 | 2700 | 1.0314 | 0.8242 | 0.8455 | 0.8242 | 0.8230 |
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| 0.0004 | 9.3333 | 2800 | 1.0323 | 0.8233 | 0.8443 | 0.8233 | 0.8230 |
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| 0.0003 | 9.6667 | 2900 | 1.0397 | 0.8229 | 0.8433 | 0.8229 | 0.8229 |
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| 0.0003 | 10.0 | 3000 | 1.0473 | 0.8237 | 0.8437 | 0.8237 | 0.8239 |
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| 0.0003 | 10.3333 | 3100 | 1.0536 | 0.8229 | 0.8428 | 0.8229 | 0.8233 |
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| 0.0003 | 10.6667 | 3200 | 1.0605 | 0.8229 | 0.8429 | 0.8229 | 0.8234 |
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| 0.0003 | 11.0 | 3300 | 1.0667 | 0.8229 | 0.8429 | 0.8229 | 0.8234 |
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| 0.0002 | 11.3333 | 3400 | 1.0711 | 0.8237 | 0.8436 | 0.8237 | 0.8243 |
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| 0.0002 | 11.6667 | 3500 | 1.0750 | 0.8246 | 0.8441 | 0.8246 | 0.8251 |
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| 0.0002 | 12.0 | 3600 | 1.0804 | 0.825 | 0.8443 | 0.825 | 0.8257 |
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| 0.0002 | 12.3333 | 3700 | 1.0839 | 0.825 | 0.8440 | 0.825 | 0.8257 |
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| 0.0002 | 12.6667 | 3800 | 1.0875 | 0.8246 | 0.8436 | 0.8246 | 0.8253 |
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| 0.0002 | 13.0 | 3900 | 1.0909 | 0.8246 | 0.8436 | 0.8246 | 0.8253 |
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| 0.0002 | 13.3333 | 4000 | 1.0930 | 0.8246 | 0.8436 | 0.8246 | 0.8253 |
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| 0.0002 | 13.6667 | 4100 | 1.0954 | 0.8237 | 0.8429 | 0.8237 | 0.8246 |
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| 0.0002 | 14.0 | 4200 | 1.0975 | 0.8237 | 0.8429 | 0.8237 | 0.8246 |
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| 0.0002 | 14.3333 | 4300 | 1.0988 | 0.8237 | 0.8429 | 0.8237 | 0.8246 |
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| 0.0002 | 14.6667 | 4400 | 1.0997 | 0.8237 | 0.8429 | 0.8237 | 0.8246 |
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| 0.0002 | 15.0 | 4500 | 1.1000 | 0.8237 | 0.8429 | 0.8237 | 0.8246 |
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### Framework versions
|
all_results.json
CHANGED
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@@ -1,16 +1,16 @@
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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": 16.
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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": 2.
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}
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{
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"epoch": 15.0,
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"eval_accuracy": 0.83375,
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"eval_f1": 0.8355968544321966,
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+
"eval_loss": 0.49404826760292053,
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+
"eval_precision": 0.8588680878951838,
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| 7 |
+
"eval_recall": 0.83375,
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+
"eval_runtime": 16.6108,
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| 9 |
+
"eval_samples_per_second": 144.484,
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+
"eval_steps_per_second": 18.061,
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| 11 |
+
"total_flos": 1.115924655734784e+19,
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| 12 |
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"train_loss": 0.036104821799529924,
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| 13 |
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| 14 |
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"train_samples_per_second": 72.943,
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| 15 |
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"train_steps_per_second": 2.279
|
| 16 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
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| 3 |
size 343236280
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|
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:816c0329c7df3ff4c5d3934af2b89aee05a3250e99db1a4a0a9e62b4d4def868
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| 3 |
size 343236280
|
test_results.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
-
"epoch":
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| 3 |
-
"eval_accuracy": 0.
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| 4 |
-
"eval_f1": 0.
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| 5 |
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"eval_loss": 0.
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| 6 |
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"eval_precision": 0.
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| 7 |
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"eval_recall": 0.
|
| 8 |
-
"eval_runtime": 16.
|
| 9 |
-
"eval_samples_per_second":
|
| 10 |
-
"eval_steps_per_second":
|
| 11 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"epoch": 15.0,
|
| 3 |
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"eval_accuracy": 0.83375,
|
| 4 |
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"eval_f1": 0.8355968544321966,
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| 5 |
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"eval_loss": 0.49404826760292053,
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| 6 |
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"eval_precision": 0.8588680878951838,
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| 7 |
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"eval_recall": 0.83375,
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| 8 |
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"eval_runtime": 16.6108,
|
| 9 |
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"eval_samples_per_second": 144.484,
|
| 10 |
+
"eval_steps_per_second": 18.061
|
| 11 |
}
|
train_results.json
CHANGED
|
@@ -1,8 +1,8 @@
|
|
| 1 |
{
|
| 2 |
-
"epoch":
|
| 3 |
-
"total_flos":
|
| 4 |
-
"train_loss": 0.
|
| 5 |
-
"train_runtime":
|
| 6 |
-
"train_samples_per_second":
|
| 7 |
-
"train_steps_per_second": 2.
|
| 8 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"epoch": 15.0,
|
| 3 |
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"total_flos": 1.115924655734784e+19,
|
| 4 |
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"train_loss": 0.036104821799529924,
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| 5 |
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"train_runtime": 1974.1364,
|
| 6 |
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"train_samples_per_second": 72.943,
|
| 7 |
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"train_steps_per_second": 2.279
|
| 8 |
}
|
trainer_state.json
CHANGED
|
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See raw diff
|
|
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
|
| 3 |
size 5432
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|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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oid sha256:35779c4b14f476d1d8998bcefdf259f30b0bf91189e06a1e7fdd4b9eb4b2b619
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| 3 |
size 5432
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