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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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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-colon-cancer-classification |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8210439105219552 |
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pipeline_tag: image-classification |
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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-colon-cancer-classification |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6794 |
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- Accuracy: 0.8210 |
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## Model description |
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- Fine tuned vision transformer for classification of colon cancer. |
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- Four classes: Normal Tissue, Serrated Lesion, Adenoma, Adenocarcinoma |
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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: 2e-05 |
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- train_batch_size: 10 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- num_epochs: 15 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.8993 | 0.35 | 100 | 0.6462 | 0.7341 | |
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| 0.6042 | 0.71 | 200 | 0.6380 | 0.7432 | |
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| 0.6284 | 1.06 | 300 | 0.5628 | 0.7821 | |
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| 0.5494 | 1.42 | 400 | 0.5643 | 0.7788 | |
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| 0.5218 | 1.77 | 500 | 0.5478 | 0.7970 | |
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| 0.5053 | 2.13 | 600 | 0.5356 | 0.7846 | |
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| 0.4441 | 2.48 | 700 | 0.6928 | 0.7133 | |
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| 0.4492 | 2.84 | 800 | 0.4898 | 0.8078 | |
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| 0.429 | 3.19 | 900 | 0.5166 | 0.8020 | |
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| 0.3474 | 3.55 | 1000 | 0.5373 | 0.8061 | |
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| 0.337 | 3.9 | 1100 | 0.5442 | 0.7904 | |
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| 0.3243 | 4.26 | 1200 | 0.5171 | 0.8111 | |
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| 0.3003 | 4.61 | 1300 | 0.5463 | 0.8070 | |
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| 0.3127 | 4.96 | 1400 | 0.5122 | 0.8202 | |
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| 0.2587 | 5.32 | 1500 | 0.5807 | 0.8152 | |
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| 0.2434 | 5.67 | 1600 | 0.5392 | 0.8219 | |
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| 0.1996 | 6.03 | 1700 | 0.6343 | 0.8045 | |
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| 0.2033 | 6.38 | 1800 | 0.5855 | 0.8128 | |
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| 0.2056 | 6.74 | 1900 | 0.6516 | 0.8144 | |
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| 0.1927 | 7.09 | 2000 | 0.5770 | 0.8227 | |
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| 0.1688 | 7.45 | 2100 | 0.6153 | 0.8293 | |
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| 0.1566 | 7.8 | 2200 | 0.5994 | 0.8268 | |
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| 0.1406 | 8.16 | 2300 | 0.6192 | 0.8277 | |
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| 0.1381 | 8.51 | 2400 | 0.6334 | 0.8202 | |
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| 0.12 | 8.87 | 2500 | 0.6444 | 0.8136 | |
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| 0.104 | 9.22 | 2600 | 0.6709 | 0.8202 | |
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| 0.1049 | 9.57 | 2700 | 0.6752 | 0.8227 | |
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| 0.1349 | 9.93 | 2800 | 0.6980 | 0.8186 | |
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| 0.0846 | 10.28 | 2900 | 0.6794 | 0.8210 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
