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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-beta1-0.88 |
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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-beta1-0.88 |
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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: 0.4922 |
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- Accuracy: 0.8523 |
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- Precision: 0.8564 |
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- Recall: 0.8523 |
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- F1: 0.8539 |
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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=(0.88,0.999) 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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| 1.713 | 1.0 | 321 | 0.9541 | 0.6987 | 0.6717 | 0.6987 | 0.6282 | |
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| 1.1578 | 2.0 | 642 | 0.7858 | 0.6956 | 0.7680 | 0.6956 | 0.7157 | |
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| 1.035 | 3.0 | 963 | 0.7133 | 0.7282 | 0.7631 | 0.7282 | 0.6990 | |
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| 0.9749 | 4.0 | 1284 | 0.5555 | 0.7712 | 0.7960 | 0.7712 | 0.7759 | |
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| 0.9392 | 5.0 | 1605 | 0.6676 | 0.7642 | 0.7943 | 0.7642 | 0.7721 | |
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| 0.992 | 6.0 | 1926 | 0.6117 | 0.7725 | 0.7958 | 0.7725 | 0.7767 | |
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| 0.8857 | 7.0 | 2247 | 0.6220 | 0.7562 | 0.7969 | 0.7562 | 0.7664 | |
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| 0.7885 | 8.0 | 2568 | 0.7200 | 0.7042 | 0.8134 | 0.7042 | 0.7319 | |
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| 0.7404 | 9.0 | 2889 | 0.5796 | 0.7798 | 0.7973 | 0.7798 | 0.7801 | |
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| 0.6687 | 10.0 | 3210 | 0.5593 | 0.7757 | 0.8340 | 0.7757 | 0.7910 | |
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| 0.6173 | 11.0 | 3531 | 0.5573 | 0.8017 | 0.8328 | 0.8017 | 0.8090 | |
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| 0.5531 | 12.0 | 3852 | 0.5931 | 0.7902 | 0.8270 | 0.7902 | 0.8024 | |
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| 0.4717 | 13.0 | 4173 | 0.6534 | 0.8221 | 0.8265 | 0.8221 | 0.8126 | |
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| 0.4553 | 14.0 | 4494 | 0.5501 | 0.8329 | 0.8489 | 0.8329 | 0.8367 | |
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| 0.3776 | 15.0 | 4815 | 0.6037 | 0.8193 | 0.8392 | 0.8193 | 0.8242 | |
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| 0.3435 | 16.0 | 5136 | 0.6238 | 0.8093 | 0.8367 | 0.8093 | 0.8176 | |
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| 0.2983 | 17.0 | 5457 | 0.4922 | 0.8523 | 0.8564 | 0.8523 | 0.8539 | |
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| 0.259 | 18.0 | 5778 | 0.5820 | 0.8356 | 0.8512 | 0.8356 | 0.8406 | |
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| 0.2105 | 19.0 | 6099 | 0.6471 | 0.8270 | 0.8536 | 0.8270 | 0.8349 | |
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| 0.2268 | 20.0 | 6420 | 0.6060 | 0.8519 | 0.8551 | 0.8519 | 0.8527 | |
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| 0.155 | 21.0 | 6741 | 0.5968 | 0.8693 | 0.8649 | 0.8693 | 0.8663 | |
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| 0.1226 | 22.0 | 7062 | 0.6047 | 0.8682 | 0.8661 | 0.8682 | 0.8668 | |
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| 0.1267 | 23.0 | 7383 | 0.6395 | 0.8682 | 0.8617 | 0.8682 | 0.8629 | |
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| 0.1184 | 24.0 | 7704 | 0.6441 | 0.8693 | 0.8660 | 0.8693 | 0.8660 | |
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| 0.1034 | 25.0 | 8025 | 0.6301 | 0.8689 | 0.8671 | 0.8689 | 0.8672 | |
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| 0.0907 | 26.0 | 8346 | 0.6288 | 0.8717 | 0.8674 | 0.8717 | 0.8687 | |
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| 0.1358 | 27.0 | 8667 | 0.6250 | 0.8727 | 0.8682 | 0.8727 | 0.8698 | |
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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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