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--- |
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license: apache-2.0 |
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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: histo_train_vit |
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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: 0.825 |
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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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# histo_train_vit |
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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: 0.7340 |
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- Accuracy: 0.825 |
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## Model description |
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More information needed |
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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: 0.0002 |
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- train_batch_size: 64 |
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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: linear |
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- num_epochs: 20 |
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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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| 1.0481 | 1.67 | 10 | 0.4926 | 0.825 | |
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| 0.3714 | 3.33 | 20 | 0.3388 | 0.9 | |
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| 0.0642 | 5.0 | 30 | 0.3255 | 0.875 | |
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| 0.0199 | 6.67 | 40 | 0.4111 | 0.875 | |
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| 0.0074 | 8.33 | 50 | 0.3334 | 0.925 | |
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| 0.0024 | 10.0 | 60 | 0.3710 | 0.9 | |
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| 0.0131 | 11.67 | 70 | 0.5366 | 0.85 | |
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| 0.0067 | 13.33 | 80 | 0.5172 | 0.875 | |
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| 0.0152 | 15.0 | 90 | 0.4835 | 0.9 | |
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| 0.0058 | 16.67 | 100 | 0.3979 | 0.875 | |
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| 0.0005 | 18.33 | 110 | 0.5964 | 0.825 | |
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| 0.0008 | 20.0 | 120 | 0.7340 | 0.825 | |
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### Framework versions |
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- Transformers 4.27.3 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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