Downloads · 30 days
1
5% of all-time downloads
vit-base-oxford-iiit-pets
Vinci96/vit-base-oxford-iiit-pets
vit-base-oxford-iiit-pets is a image classification model from Vinci96. 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. --
All-time downloads
22
Public
Parameters
85.8M
1.7 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors343 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Apr 17, 2025
- Updated
- Apr 17, 2025
- SHA
- 6bf0b6b8
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 pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set:
- Loss: 0.1634
- Accuracy: 0.9526
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3942 | 1.0 | 370 | 0.2973 | 0.9229 |
| 0.2301 | 2.0 | 740 | 0.2206 | 0.9391 |
| 0.1671 | 3.0 | 1110 | 0.2077 | 0.9364 |
| 0.1555 | 4.0 | 1480 | 0.2004 | 0.9418 |
| 0.1276 | 5.0 | 1850 | 0.1970 | 0.9391 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
Zero Shot Evaluation
- Accuracy: 0.8800
- Precision: 0.8768
- Recall: 0.8800
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Apr 17, 2025
- Updated
- Apr 17, 2025