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vit-base-oxford-iiit-pets
hasnanmr/vit-base-oxford-iiit-pets
vit-base-oxford-iiit-pets 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
- 239dcd96
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-oxford-iiit-pets
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: 0.6194
- Accuracy: 0.7639
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: 16
- 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 |
|---|---|---|---|---|
| 1.0901 | 1.0 | 32 | 0.9278 | 0.4931 |
| 0.5383 | 2.0 | 64 | 0.6985 | 0.6319 |
| 0.2707 | 3.0 | 96 | 0.6691 | 0.7222 |
| 0.0366 | 4.0 | 128 | 0.9557 | 0.6806 |
| 0.0066 | 5.0 | 160 | 0.8927 | 0.7083 |
| 0.0075 | 6.0 | 192 | 1.2046 | 0.7014 |
| 0.0013 | 7.0 | 224 | 1.2583 | 0.7083 |
| 0.0006 | 8.0 | 256 | 1.3180 | 0.6944 |
| 0.0004 | 9.0 | 288 | 1.3468 | 0.7014 |
| 0.0002 | 10.0 | 320 | 1.3582 | 0.6875 |
| 0.0002 | 11.0 | 352 | 1.3868 | 0.6875 |
| 0.0002 | 12.0 | 384 | 1.4094 | 0.6806 |
| 0.0002 | 13.0 | 416 | 1.4392 | 0.6806 |
| 0.0002 | 14.0 | 448 | 1.4536 | 0.6875 |
| 0.0001 | 15.0 | 480 | 1.4695 | 0.6875 |
| 0.0001 | 16.0 | 512 | 1.4850 | 0.6875 |
| 0.0001 | 17.0 | 544 | 1.5004 | 0.6875 |
| 0.0001 | 18.0 | 576 | 1.5110 | 0.6875 |
| 0.0001 | 19.0 | 608 | 1.5219 | 0.6875 |
| 0.0001 | 20.0 | 640 | 1.5340 | 0.6875 |
| 0.0001 | 21.0 | 672 | 1.5461 | 0.6875 |
| 0.0001 | 22.0 | 704 | 1.5541 | 0.6875 |
| 0.0001 | 23.0 | 736 | 1.5624 | 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