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license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-huge-binary-isic-patch-14
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-huge-binary-isic-patch-14
This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the ahishamm/isic_binary_augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2524
- Accuracy: 0.8910
- Recall: 0.8910
- F1: 0.8910
- Precision: 0.8910
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3123 | 0.09 | 100 | 0.2954 | 0.8634 | 0.8634 | 0.8634 | 0.8634 |
| 0.2976 | 0.19 | 200 | 0.3114 | 0.8551 | 0.8551 | 0.8551 | 0.8551 |
| 0.3031 | 0.28 | 300 | 0.3799 | 0.8070 | 0.8070 | 0.8070 | 0.8070 |
| 0.2181 | 0.37 | 400 | 0.2541 | 0.8938 | 0.8938 | 0.8938 | 0.8938 |
| 0.2653 | 0.46 | 500 | 0.2780 | 0.8580 | 0.8580 | 0.8580 | 0.8580 |
| 0.1503 | 0.56 | 600 | 0.2552 | 0.8957 | 0.8957 | 0.8957 | 0.8957 |
| 0.2676 | 0.65 | 700 | 0.3207 | 0.8717 | 0.8717 | 0.8717 | 0.8717 |
| 0.1188 | 0.74 | 800 | 0.2524 | 0.8910 | 0.8910 | 0.8910 | 0.8910 |
| 0.0759 | 0.84 | 900 | 0.3596 | 0.8874 | 0.8874 | 0.8874 | 0.8874 |
| 0.0516 | 0.93 | 1000 | 0.3129 | 0.8981 | 0.8981 | 0.8981 | 0.8981 |
| 0.038 | 1.02 | 1100 | 0.3258 | 0.8890 | 0.8890 | 0.8890 | 0.8890 |
| 0.1238 | 1.12 | 1200 | 0.3292 | 0.8828 | 0.8828 | 0.8828 | 0.8828 |
| 0.0557 | 1.21 | 1300 | 0.3667 | 0.8770 | 0.8770 | 0.8770 | 0.8770 |
| 0.0586 | 1.3 | 1400 | 0.3858 | 0.9015 | 0.9015 | 0.9015 | 0.9015 |
| 0.063 | 1.39 | 1500 | 0.3371 | 0.9061 | 0.9061 | 0.9061 | 0.9061 |
| 0.0351 | 1.49 | 1600 | 0.3462 | 0.8995 | 0.8995 | 0.8995 | 0.8995 |
| 0.0149 | 1.58 | 1700 | 0.4622 | 0.8861 | 0.8861 | 0.8861 | 0.8861 |
| 0.0404 | 1.67 | 1800 | 0.4071 | 0.8903 | 0.8903 | 0.8903 | 0.8903 |
| 0.002 | 1.77 | 1900 | 0.4530 | 0.8971 | 0.8971 | 0.8971 | 0.8971 |
| 0.0459 | 1.86 | 2000 | 0.3853 | 0.8898 | 0.8898 | 0.8898 | 0.8898 |
| 0.0067 | 1.95 | 2100 | 0.4223 | 0.8968 | 0.8968 | 0.8968 | 0.8968 |
| 0.0041 | 2.04 | 2200 | 0.4549 | 0.8948 | 0.8948 | 0.8948 | 0.8948 |
| 0.0012 | 2.14 | 2300 | 0.4800 | 0.8962 | 0.8962 | 0.8962 | 0.8962 |
| 0.0159 | 2.23 | 2400 | 0.5657 | 0.8916 | 0.8916 | 0.8916 | 0.8916 |
| 0.0327 | 2.32 | 2500 | 0.5150 | 0.8884 | 0.8884 | 0.8884 | 0.8884 |
| 0.0011 | 2.42 | 2600 | 0.5171 | 0.8962 | 0.8962 | 0.8962 | 0.8962 |
| 0.027 | 2.51 | 2700 | 0.5732 | 0.8865 | 0.8865 | 0.8865 | 0.8865 |
| 0.0031 | 2.6 | 2800 | 0.4335 | 0.9075 | 0.9075 | 0.9075 | 0.9075 |
| 0.0006 | 2.7 | 2900 | 0.4453 | 0.9084 | 0.9084 | 0.9084 | 0.9084 |
| 0.0008 | 2.79 | 3000 | 0.4262 | 0.9047 | 0.9047 | 0.9047 | 0.9047 |
| 0.0031 | 2.88 | 3100 | 0.4823 | 0.9003 | 0.9003 | 0.9003 | 0.9003 |
| 0.0005 | 2.97 | 3200 | 0.5086 | 0.9022 | 0.9022 | 0.9022 | 0.9022 |
| 0.0004 | 3.07 | 3300 | 0.4912 | 0.9061 | 0.9061 | 0.9061 | 0.9061 |
| 0.0005 | 3.16 | 3400 | 0.5218 | 0.9027 | 0.9027 | 0.9027 | 0.9027 |
| 0.0037 | 3.25 | 3500 | 0.5006 | 0.9054 | 0.9054 | 0.9054 | 0.9054 |
| 0.0004 | 3.35 | 3600 | 0.5137 | 0.9040 | 0.9040 | 0.9040 | 0.9040 |
| 0.0003 | 3.44 | 3700 | 0.5105 | 0.9057 | 0.9057 | 0.9057 | 0.9057 |
| 0.0004 | 3.53 | 3800 | 0.5087 | 0.9058 | 0.9058 | 0.9058 | 0.9058 |
| 0.0003 | 3.62 | 3900 | 0.5095 | 0.9079 | 0.9079 | 0.9079 | 0.9079 |
| 0.0003 | 3.72 | 4000 | 0.5294 | 0.9045 | 0.9045 | 0.9045 | 0.9045 |
| 0.0003 | 3.81 | 4100 | 0.5242 | 0.9049 | 0.9049 | 0.9049 | 0.9049 |
| 0.0003 | 3.9 | 4200 | 0.5256 | 0.9050 | 0.9050 | 0.9050 | 0.9050 |
| 0.0096 | 4.0 | 4300 | 0.5267 | 0.9050 | 0.9050 | 0.9050 | 0.9050 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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