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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-beta2-0.99
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-beta2-0.99
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.
It achieves the following results on the evaluation set:
- Loss: 0.5248
- Accuracy: 0.8623
- Precision: 0.8584
- Recall: 0.8623
- F1: 0.8596
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1733
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7217 | 1.0 | 321 | 1.0190 | 0.7035 | 0.6635 | 0.7035 | 0.6527 |
| 1.1622 | 2.0 | 642 | 0.7386 | 0.7056 | 0.7576 | 0.7056 | 0.7192 |
| 1.0368 | 3.0 | 963 | 0.6550 | 0.7517 | 0.7564 | 0.7517 | 0.7214 |
| 0.9653 | 4.0 | 1284 | 0.5641 | 0.7843 | 0.7948 | 0.7843 | 0.7863 |
| 0.9272 | 5.0 | 1605 | 0.7957 | 0.6768 | 0.7959 | 0.6768 | 0.7035 |
| 0.9878 | 6.0 | 1926 | 0.5809 | 0.7871 | 0.8062 | 0.7871 | 0.7904 |
| 0.872 | 7.0 | 2247 | 0.6815 | 0.7216 | 0.8081 | 0.7216 | 0.7442 |
| 0.7998 | 8.0 | 2568 | 0.6104 | 0.7559 | 0.8143 | 0.7559 | 0.7723 |
| 0.733 | 9.0 | 2889 | 0.5296 | 0.8148 | 0.8254 | 0.8148 | 0.8172 |
| 0.6957 | 10.0 | 3210 | 0.5797 | 0.7996 | 0.8322 | 0.7996 | 0.8052 |
| 0.6271 | 11.0 | 3531 | 0.5926 | 0.7933 | 0.8343 | 0.7933 | 0.8058 |
| 0.5614 | 12.0 | 3852 | 0.5879 | 0.7920 | 0.8384 | 0.7920 | 0.8060 |
| 0.4576 | 13.0 | 4173 | 0.6665 | 0.8138 | 0.8312 | 0.8138 | 0.8028 |
| 0.4645 | 14.0 | 4494 | 0.5515 | 0.8294 | 0.8470 | 0.8294 | 0.8329 |
| 0.3913 | 15.0 | 4815 | 0.5474 | 0.8225 | 0.8466 | 0.8225 | 0.8288 |
| 0.3693 | 16.0 | 5136 | 0.5769 | 0.8235 | 0.8464 | 0.8235 | 0.8308 |
| 0.2794 | 17.0 | 5457 | 0.5328 | 0.8509 | 0.8571 | 0.8509 | 0.8516 |
| 0.2677 | 18.0 | 5778 | 0.5248 | 0.8623 | 0.8584 | 0.8623 | 0.8596 |
| 0.2104 | 19.0 | 6099 | 0.6284 | 0.8433 | 0.8572 | 0.8433 | 0.8473 |
| 0.2459 | 20.0 | 6420 | 0.6137 | 0.8544 | 0.8596 | 0.8544 | 0.8555 |
| 0.1769 | 21.0 | 6741 | 0.5960 | 0.8637 | 0.8573 | 0.8637 | 0.8566 |
| 0.1294 | 22.0 | 7062 | 0.5844 | 0.8700 | 0.8687 | 0.8700 | 0.8687 |
| 0.1597 | 23.0 | 7383 | 0.6580 | 0.8665 | 0.8604 | 0.8665 | 0.8589 |
| 0.1227 | 24.0 | 7704 | 0.6226 | 0.8731 | 0.8720 | 0.8731 | 0.8712 |
| 0.1054 | 25.0 | 8025 | 0.6198 | 0.8752 | 0.8728 | 0.8752 | 0.8721 |
| 0.0945 | 26.0 | 8346 | 0.6050 | 0.8793 | 0.8757 | 0.8793 | 0.8764 |
| 0.1242 | 27.0 | 8667 | 0.6078 | 0.8828 | 0.8788 | 0.8828 | 0.8798 |
| 0.0819 | 28.0 | 8988 | 0.6190 | 0.8797 | 0.8748 | 0.8797 | 0.8756 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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