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vit-base-finetuned-cephalometric
hasnanmr/vit-base-finetuned-cephalometric
vit-base-finetuned-cephalometric is a image classification model from hasnanmr. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Mar 1, 2025
- Updated
- Mar 1, 2025
- SHA
- b0a78f6f
Base models
Model card
From the Hugging Face model README
<!-- 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-finetuned-cephalometric
This model is a fine-tuned version of google/vit-base-patch16-224 on the cepha-cutoutCLAHE dataset. It achieves the following results on the evaluation set:
- Loss: 0.7340
- Accuracy: 0.6528
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 16 | 0.9458 | 0.5486 |
| 0.9879 | 2.0 | 32 | 0.6947 | 0.6597 |
| 0.4628 | 3.0 | 48 | 0.6375 | 0.6597 |
| 0.135 | 4.0 | 64 | 0.7060 | 0.6944 |
| 0.0339 | 5.0 | 80 | 0.7301 | 0.6597 |
| 0.0339 | 6.0 | 96 | 0.9236 | 0.6875 |
| 0.0059 | 7.0 | 112 | 0.9261 | 0.6806 |
| 0.0024 | 8.0 | 128 | 0.9961 | 0.6875 |
| 0.0012 | 9.0 | 144 | 1.0060 | 0.6736 |
| 0.0008 | 10.0 | 160 | 1.0329 | 0.6875 |
| 0.0008 | 11.0 | 176 | 1.0575 | 0.6944 |
| 0.0006 | 12.0 | 192 | 1.0768 | 0.6944 |
| 0.0006 | 13.0 | 208 | 1.1002 | 0.6944 |
| 0.0005 | 14.0 | 224 | 1.1220 | 0.6875 |
| 0.0004 | 15.0 | 240 | 1.1367 | 0.6875 |
| 0.0004 | 16.0 | 256 | 1.1538 | 0.6875 |
| 0.0004 | 17.0 | 272 | 1.1707 | 0.6875 |
| 0.0003 | 18.0 | 288 | 1.1855 | 0.6875 |
| 0.0003 | 19.0 | 304 | 1.2007 | 0.6875 |
| 0.0003 | 20.0 | 320 | 1.2066 | 0.6806 |
| 0.0003 | 21.0 | 336 | 1.2211 | 0.6806 |
| 0.0003 | 22.0 | 352 | 1.2291 | 0.6875 |
| 0.0002 | 23.0 | 368 | 1.2385 | 0.6875 |
| 0.0002 | 24.0 | 384 | 1.2508 | 0.6875 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Mar 1, 2025
- Updated
- Mar 1, 2025