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vit-food
thini77/vit-food
vit-food is a image classification model from thini77. 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
- May 28, 2025
- Updated
- May 29, 2025
- SHA
- 0458367d
Base models
Datasets
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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-food
This model is a fine-tuned version of google/vit-base-patch16-224 on the thini77/data-food-classification dataset. It achieves the following results on the evaluation set:
- Loss: 0.2856
- Accuracy: 0.9143
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 | 53 | 0.5583 | 0.9238 |
| 0.8727 | 2.0 | 106 | 0.3149 | 0.9238 |
| 0.8727 | 3.0 | 159 | 0.2508 | 0.9238 |
| 0.306 | 4.0 | 212 | 0.2295 | 0.9238 |
| 0.306 | 5.0 | 265 | 0.2246 | 0.9238 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- May 28, 2025
- Updated
- May 29, 2025