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--- |
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library_name: transformers |
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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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- image-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-finetuned-cephalometric |
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results: [] |
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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-base-finetuned-cephalometric |
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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 cepha-cutoutCLAHE 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: 0.6528 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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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 | 1.0 | 16 | 0.9458 | 0.5486 | |
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| 0.9879 | 2.0 | 32 | 0.6947 | 0.6597 | |
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| 0.4628 | 3.0 | 48 | 0.6375 | 0.6597 | |
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| 0.135 | 4.0 | 64 | 0.7060 | 0.6944 | |
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| 0.0339 | 5.0 | 80 | 0.7301 | 0.6597 | |
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| 0.0339 | 6.0 | 96 | 0.9236 | 0.6875 | |
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| 0.0059 | 7.0 | 112 | 0.9261 | 0.6806 | |
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| 0.0024 | 8.0 | 128 | 0.9961 | 0.6875 | |
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| 0.0012 | 9.0 | 144 | 1.0060 | 0.6736 | |
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| 0.0008 | 10.0 | 160 | 1.0329 | 0.6875 | |
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| 0.0008 | 11.0 | 176 | 1.0575 | 0.6944 | |
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| 0.0006 | 12.0 | 192 | 1.0768 | 0.6944 | |
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| 0.0006 | 13.0 | 208 | 1.1002 | 0.6944 | |
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| 0.0005 | 14.0 | 224 | 1.1220 | 0.6875 | |
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| 0.0004 | 15.0 | 240 | 1.1367 | 0.6875 | |
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| 0.0004 | 16.0 | 256 | 1.1538 | 0.6875 | |
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| 0.0004 | 17.0 | 272 | 1.1707 | 0.6875 | |
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| 0.0003 | 18.0 | 288 | 1.1855 | 0.6875 | |
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| 0.0003 | 19.0 | 304 | 1.2007 | 0.6875 | |
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| 0.0003 | 20.0 | 320 | 1.2066 | 0.6806 | |
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| 0.0003 | 21.0 | 336 | 1.2211 | 0.6806 | |
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| 0.0003 | 22.0 | 352 | 1.2291 | 0.6875 | |
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| 0.0002 | 23.0 | 368 | 1.2385 | 0.6875 | |
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| 0.0002 | 24.0 | 384 | 1.2508 | 0.6875 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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