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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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tags: |
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- image-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: vit-beta2-0.995 |
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results: [] |
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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-beta2-0.995 |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5119 |
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- Accuracy: 0.8076 |
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- Precision: 0.8207 |
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- Recall: 0.8076 |
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- F1: 0.8098 |
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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: 0.0001 |
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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: Adam with betas=(0.9,0.995) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1733 |
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- num_epochs: 100 |
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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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| 1.7709 | 1.0 | 321 | 0.9409 | 0.7039 | 0.6601 | 0.7039 | 0.6317 | |
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| 1.1633 | 2.0 | 642 | 0.7317 | 0.7372 | 0.7193 | 0.7372 | 0.6970 | |
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| 1.0429 | 3.0 | 963 | 0.6350 | 0.7625 | 0.7357 | 0.7625 | 0.7240 | |
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| 0.9649 | 4.0 | 1284 | 0.5760 | 0.7694 | 0.8038 | 0.7694 | 0.7808 | |
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| 0.9051 | 5.0 | 1605 | 0.6441 | 0.7670 | 0.7941 | 0.7670 | 0.7732 | |
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| 0.9826 | 6.0 | 1926 | 0.5662 | 0.7850 | 0.7956 | 0.7850 | 0.7892 | |
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| 0.8855 | 7.0 | 2247 | 0.6882 | 0.7264 | 0.7937 | 0.7264 | 0.7459 | |
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| 0.789 | 8.0 | 2568 | 0.6491 | 0.7365 | 0.8089 | 0.7365 | 0.7564 | |
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| 0.7192 | 9.0 | 2889 | 0.5119 | 0.8076 | 0.8207 | 0.8076 | 0.8098 | |
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| 0.7012 | 10.0 | 3210 | 0.5414 | 0.7975 | 0.8341 | 0.7975 | 0.8077 | |
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| 0.6376 | 11.0 | 3531 | 0.5712 | 0.7947 | 0.8332 | 0.7947 | 0.8066 | |
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| 0.5412 | 12.0 | 3852 | 0.5661 | 0.8058 | 0.8328 | 0.8058 | 0.8145 | |
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| 0.4667 | 13.0 | 4173 | 0.6375 | 0.8190 | 0.8410 | 0.8190 | 0.8179 | |
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| 0.4766 | 14.0 | 4494 | 0.5736 | 0.8252 | 0.8508 | 0.8252 | 0.8313 | |
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| 0.384 | 15.0 | 4815 | 0.5305 | 0.8356 | 0.8415 | 0.8356 | 0.8371 | |
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| 0.37 | 16.0 | 5136 | 0.5531 | 0.8315 | 0.8499 | 0.8315 | 0.8379 | |
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| 0.2809 | 17.0 | 5457 | 0.5174 | 0.8637 | 0.8630 | 0.8637 | 0.8608 | |
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| 0.2681 | 18.0 | 5778 | 0.5556 | 0.8478 | 0.8555 | 0.8478 | 0.8504 | |
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| 0.2139 | 19.0 | 6099 | 0.6291 | 0.8256 | 0.8518 | 0.8256 | 0.8335 | |
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### Framework versions |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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