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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-lr-linear
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-lr-linear
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: 0.4920
- Accuracy: 0.8322
- Precision: 0.8400
- Recall: 0.8322
- F1: 0.8323
## 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=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6029 | 0.31 | 100 | 0.6126 | 0.7805 | 0.7602 | 0.7805 | 0.7529 |
| 0.5726 | 0.62 | 200 | 0.6950 | 0.7649 | 0.7613 | 0.7649 | 0.7177 |
| 0.6521 | 0.93 | 300 | 0.5102 | 0.8124 | 0.8149 | 0.8124 | 0.8060 |
| 0.3803 | 1.25 | 400 | 0.6125 | 0.7843 | 0.8128 | 0.7843 | 0.7934 |
| 0.4048 | 1.56 | 500 | 0.5059 | 0.8214 | 0.8156 | 0.8214 | 0.8078 |
| 0.2939 | 1.87 | 600 | 0.6723 | 0.7680 | 0.8366 | 0.7680 | 0.7818 |
| 0.2138 | 2.18 | 700 | 0.6351 | 0.8128 | 0.8480 | 0.8128 | 0.8170 |
| 0.2615 | 2.49 | 800 | 0.4920 | 0.8322 | 0.8400 | 0.8322 | 0.8323 |
| 0.2125 | 2.8 | 900 | 0.5596 | 0.8492 | 0.8509 | 0.8492 | 0.8432 |
| 0.0768 | 3.12 | 1000 | 0.8239 | 0.8291 | 0.8500 | 0.8291 | 0.8235 |
| 0.0649 | 3.43 | 1100 | 0.6827 | 0.8367 | 0.8481 | 0.8367 | 0.8360 |
| 0.1382 | 3.74 | 1200 | 0.6838 | 0.8450 | 0.8467 | 0.8450 | 0.8399 |
| 0.0486 | 4.05 | 1300 | 0.6367 | 0.8578 | 0.8548 | 0.8578 | 0.8494 |
| 0.1122 | 4.36 | 1400 | 0.7330 | 0.8398 | 0.8368 | 0.8398 | 0.8330 |
| 0.0302 | 4.67 | 1500 | 0.7137 | 0.8450 | 0.8470 | 0.8450 | 0.8442 |
| 0.0462 | 4.98 | 1600 | 0.8198 | 0.8516 | 0.8519 | 0.8516 | 0.8456 |
| 0.0109 | 5.3 | 1700 | 0.8482 | 0.8478 | 0.8384 | 0.8478 | 0.8378 |
| 0.0545 | 5.61 | 1800 | 0.8046 | 0.8499 | 0.8547 | 0.8499 | 0.8506 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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