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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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: image_classification_for_fracture |
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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: 0.85 |
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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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# image_classification_for_fracture |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4783 |
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- Accuracy: 0.85 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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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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| No log | 0.8 | 2 | 0.6696 | 0.75 | |
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| No log | 2.0 | 5 | 0.6296 | 0.7 | |
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| No log | 2.8 | 7 | 0.5853 | 0.775 | |
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| 0.639 | 4.0 | 10 | 0.5731 | 0.8 | |
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| 0.639 | 4.8 | 12 | 0.5430 | 0.825 | |
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| 0.639 | 6.0 | 15 | 0.5223 | 0.85 | |
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| 0.639 | 6.8 | 17 | 0.5036 | 0.8 | |
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| 0.5453 | 8.0 | 20 | 0.4783 | 0.85 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cpu |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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