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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-16-thesis-demo-HAM10000
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-base-16-thesis-demo-HAM10000
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/HAM_db_enhanced_balanced_reduced_50_20_20_50 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5296
- Accuracy: 0.8344
- Recall: 0.8344
- F1: 0.8344
- Precision: 0.8344
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.4855 | 0.12 | 50 | 1.3519 | 0.5093 | 0.5093 | 0.5093 | 0.5093 |
| 1.044 | 0.23 | 100 | 1.0515 | 0.6268 | 0.6268 | 0.6268 | 0.6268 |
| 1.0774 | 0.35 | 150 | 1.2104 | 0.5681 | 0.5681 | 0.5681 | 0.5681 |
| 0.9508 | 0.46 | 200 | 1.0624 | 0.6061 | 0.6061 | 0.6061 | 0.6061 |
| 0.9522 | 0.58 | 250 | 0.9338 | 0.6449 | 0.6449 | 0.6449 | 0.6449 |
| 0.774 | 0.69 | 300 | 0.8939 | 0.6676 | 0.6676 | 0.6676 | 0.6676 |
| 0.7675 | 0.81 | 350 | 0.7742 | 0.7183 | 0.7183 | 0.7183 | 0.7183 |
| 0.7167 | 0.92 | 400 | 0.7695 | 0.7216 | 0.7216 | 0.7216 | 0.7216 |
| 0.5204 | 1.04 | 450 | 0.8005 | 0.7303 | 0.7303 | 0.7303 | 0.7303 |
| 0.456 | 1.15 | 500 | 0.8523 | 0.6903 | 0.6903 | 0.6903 | 0.6903 |
| 0.5421 | 1.27 | 550 | 0.6753 | 0.7543 | 0.7543 | 0.7543 | 0.7543 |
| 0.4446 | 1.38 | 600 | 0.6042 | 0.7810 | 0.7810 | 0.7810 | 0.7810 |
| 0.455 | 1.5 | 650 | 0.6913 | 0.7410 | 0.7410 | 0.7410 | 0.7410 |
| 0.4175 | 1.61 | 700 | 0.6142 | 0.7810 | 0.7810 | 0.7810 | 0.7810 |
| 0.3626 | 1.73 | 750 | 0.5831 | 0.8004 | 0.8004 | 0.8004 | 0.8004 |
| 0.4816 | 1.84 | 800 | 0.5586 | 0.7891 | 0.7891 | 0.7891 | 0.7891 |
| 0.3257 | 1.96 | 850 | 0.5759 | 0.7991 | 0.7991 | 0.7991 | 0.7991 |
| 0.3111 | 2.07 | 900 | 0.6100 | 0.7931 | 0.7931 | 0.7931 | 0.7931 |
| 0.2052 | 2.19 | 950 | 0.5674 | 0.8111 | 0.8111 | 0.8111 | 0.8111 |
| 0.2273 | 2.3 | 1000 | 0.5975 | 0.8017 | 0.8017 | 0.8017 | 0.8017 |
| 0.3007 | 2.42 | 1050 | 0.5714 | 0.8204 | 0.8204 | 0.8204 | 0.8204 |
| 0.2812 | 2.53 | 1100 | 0.6081 | 0.8004 | 0.8004 | 0.8004 | 0.8004 |
| 0.2661 | 2.65 | 1150 | 0.5653 | 0.8224 | 0.8224 | 0.8224 | 0.8224 |
| 0.1796 | 2.76 | 1200 | 0.5447 | 0.8338 | 0.8338 | 0.8338 | 0.8338 |
| 0.1882 | 2.88 | 1250 | 0.5357 | 0.8284 | 0.8284 | 0.8284 | 0.8284 |
| 0.1596 | 3.0 | 1300 | 0.5296 | 0.8344 | 0.8344 | 0.8344 | 0.8344 |
| 0.075 | 3.11 | 1350 | 0.5876 | 0.8198 | 0.8198 | 0.8198 | 0.8198 |
| 0.1128 | 3.23 | 1400 | 0.5612 | 0.8338 | 0.8338 | 0.8338 | 0.8338 |
| 0.0677 | 3.34 | 1450 | 0.5911 | 0.8331 | 0.8331 | 0.8331 | 0.8331 |
| 0.0794 | 3.46 | 1500 | 0.5971 | 0.8304 | 0.8304 | 0.8304 | 0.8304 |
| 0.0367 | 3.57 | 1550 | 0.5634 | 0.8378 | 0.8378 | 0.8378 | 0.8378 |
| 0.0279 | 3.69 | 1600 | 0.5674 | 0.8391 | 0.8391 | 0.8391 | 0.8391 |
| 0.0216 | 3.8 | 1650 | 0.5777 | 0.8358 | 0.8358 | 0.8358 | 0.8358 |
| 0.0161 | 3.92 | 1700 | 0.5608 | 0.8438 | 0.8438 | 0.8438 | 0.8438 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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