| | --- |
| | license: apache-2.0 |
| | tags: |
| | - image-classification |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - recall |
| | - f1 |
| | - precision |
| | model-index: |
| | - name: vit-base-binary-isic-sharpened-patch-16 |
| | results: [] |
| | --- |
| | |
| | <!-- 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-binary-isic-sharpened-patch-16 |
| |
|
| | 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 ahishamm/isic_binary_sharpened dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2583 |
| | - Accuracy: 0.8912 |
| | - Recall: 0.8912 |
| | - F1: 0.8912 |
| | - Precision: 0.8912 |
| |
|
| | ## 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: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 4 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
| | | 0.3281 | 0.09 | 100 | 0.4381 | 0.8183 | 0.8183 | 0.8183 | 0.8183 | |
| | | 0.3212 | 0.18 | 200 | 0.3179 | 0.8503 | 0.8503 | 0.8503 | 0.8503 | |
| | | 0.2864 | 0.28 | 300 | 0.3126 | 0.8655 | 0.8655 | 0.8655 | 0.8655 | |
| | | 0.2692 | 0.37 | 400 | 0.3217 | 0.8599 | 0.8599 | 0.8599 | 0.8599 | |
| | | 0.3195 | 0.46 | 500 | 0.3061 | 0.8694 | 0.8694 | 0.8694 | 0.8694 | |
| | | 0.2095 | 0.55 | 600 | 0.2910 | 0.8669 | 0.8669 | 0.8669 | 0.8669 | |
| | | 0.2168 | 0.65 | 700 | 0.3248 | 0.8730 | 0.8730 | 0.8730 | 0.8730 | |
| | | 0.2288 | 0.74 | 800 | 0.3067 | 0.8553 | 0.8553 | 0.8553 | 0.8553 | |
| | | 0.2521 | 0.83 | 900 | 0.2723 | 0.8689 | 0.8689 | 0.8689 | 0.8689 | |
| | | 0.1953 | 0.92 | 1000 | 0.2729 | 0.8724 | 0.8724 | 0.8724 | 0.8724 | |
| | | 0.2845 | 1.02 | 1100 | 0.4392 | 0.8666 | 0.8666 | 0.8666 | 0.8666 | |
| | | 0.1484 | 1.11 | 1200 | 0.3031 | 0.8884 | 0.8884 | 0.8884 | 0.8884 | |
| | | 0.153 | 1.2 | 1300 | 0.2849 | 0.8992 | 0.8992 | 0.8992 | 0.8992 | |
| | | 0.1648 | 1.29 | 1400 | 0.2583 | 0.8912 | 0.8912 | 0.8912 | 0.8912 | |
| | | 0.1627 | 1.39 | 1500 | 0.2706 | 0.8933 | 0.8933 | 0.8933 | 0.8933 | |
| | | 0.0943 | 1.48 | 1600 | 0.2783 | 0.9034 | 0.9034 | 0.9034 | 0.9034 | |
| | | 0.0624 | 1.57 | 1700 | 0.2921 | 0.8926 | 0.8926 | 0.8926 | 0.8926 | |
| | | 0.12 | 1.66 | 1800 | 0.2915 | 0.9006 | 0.9006 | 0.9006 | 0.9006 | |
| | | 0.0735 | 1.76 | 1900 | 0.3103 | 0.8897 | 0.8897 | 0.8897 | 0.8897 | |
| | | 0.0609 | 1.85 | 2000 | 0.3382 | 0.8971 | 0.8971 | 0.8971 | 0.8971 | |
| | | 0.1645 | 1.94 | 2100 | 0.2675 | 0.8901 | 0.8901 | 0.8901 | 0.8901 | |
| | | 0.0839 | 2.03 | 2200 | 0.3941 | 0.8962 | 0.8962 | 0.8962 | 0.8962 | |
| | | 0.0571 | 2.13 | 2300 | 0.3888 | 0.9047 | 0.9047 | 0.9047 | 0.9047 | |
| | | 0.0929 | 2.22 | 2400 | 0.3773 | 0.9009 | 0.9009 | 0.9009 | 0.9009 | |
| | | 0.0378 | 2.31 | 2500 | 0.4577 | 0.9029 | 0.9029 | 0.9029 | 0.9029 | |
| | | 0.0085 | 2.4 | 2600 | 0.3183 | 0.9203 | 0.9203 | 0.9203 | 0.9203 | |
| | | 0.06 | 2.5 | 2700 | 0.3548 | 0.9126 | 0.9126 | 0.9126 | 0.9126 | |
| | | 0.0139 | 2.59 | 2800 | 0.3213 | 0.9198 | 0.9198 | 0.9198 | 0.9198 | |
| | | 0.056 | 2.68 | 2900 | 0.3558 | 0.9131 | 0.9131 | 0.9131 | 0.9131 | |
| | | 0.0433 | 2.77 | 3000 | 0.3101 | 0.9215 | 0.9215 | 0.9215 | 0.9215 | |
| | | 0.0074 | 2.87 | 3100 | 0.3140 | 0.9176 | 0.9176 | 0.9176 | 0.9176 | |
| | | 0.0216 | 2.96 | 3200 | 0.3657 | 0.9186 | 0.9186 | 0.9186 | 0.9186 | |
| | | 0.0118 | 3.05 | 3300 | 0.3722 | 0.9195 | 0.9195 | 0.9195 | 0.9195 | |
| | | 0.0014 | 3.14 | 3400 | 0.4089 | 0.9141 | 0.9141 | 0.9141 | 0.9141 | |
| | | 0.001 | 3.23 | 3500 | 0.4045 | 0.9189 | 0.9189 | 0.9189 | 0.9189 | |
| | | 0.0009 | 3.33 | 3600 | 0.3932 | 0.9230 | 0.9230 | 0.9230 | 0.9230 | |
| | | 0.0009 | 3.42 | 3700 | 0.4257 | 0.9174 | 0.9174 | 0.9174 | 0.9174 | |
| | | 0.03 | 3.51 | 3800 | 0.3981 | 0.9222 | 0.9222 | 0.9222 | 0.9222 | |
| | | 0.0007 | 3.6 | 3900 | 0.4211 | 0.9189 | 0.9189 | 0.9189 | 0.9189 | |
| | | 0.0494 | 3.7 | 4000 | 0.4029 | 0.9207 | 0.9207 | 0.9207 | 0.9207 | |
| | | 0.0009 | 3.79 | 4100 | 0.3951 | 0.9226 | 0.9226 | 0.9226 | 0.9226 | |
| | | 0.0319 | 3.88 | 4200 | 0.3944 | 0.9221 | 0.9221 | 0.9221 | 0.9221 | |
| | | 0.0013 | 3.97 | 4300 | 0.3894 | 0.9225 | 0.9225 | 0.9225 | 0.9225 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.30.2 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.13.1 |
| | - Tokenizers 0.13.3 |
| |
|
