Downloads · 30 days
10
8% of all-time downloads
vit-base-oxford_flowers102
loretyan/vit-base-oxford_flowers102
vit-base-oxford_flowers102 is a image classification model from loretyan. 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
122
Public
Parameters
85.9M
4.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors344 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- May 28, 2025
- Updated
- May 29, 2025
- SHA
- 8d92a4f6
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_flowers102
This model is a fine-tuned version of google/vit-base-patch16-224 on the dpdl-benchmark/oxford_flowers102 dataset. It achieves the following results on the evaluation set:
- Loss: 1.1771
- Accuracy: 0.9706
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 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 51 | 3.4099 | 0.5490 |
| 3.341 | 2.0 | 102 | 2.3499 | 0.8431 |
| 3.341 | 3.0 | 153 | 1.6991 | 0.9020 |
| 1.3666 | 4.0 | 204 | 1.3820 | 0.9412 |
| 1.3666 | 5.0 | 255 | 1.2861 | 0.9510 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.1
Results
Accuracy: 0.0137 Precision: 0.0017 Recall: 0.0137
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- May 28, 2025
- Updated
- May 29, 2025