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siglip2-giant-fine_tuned-food101
alecccdd/siglip2-giant-fine_tuned-food101
siglip2-giant-fine_tuned-food101 is a image classification model from alecccdd. 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
- siglip
- Access
- Public
- Created
- Dec 8, 2025
- Updated
- Dec 8, 2025
- SHA
- ccd78672
Base models
Datasets
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. -->
siglip2-giant-fine_tuned-food101
This model is a fine-tuned version of google/siglip2-giant-opt-patch16-384 on the food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1489
- Accuracy: 0.953
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.3311 | 1.0 | 63 | 0.3234 | 0.905 |
| 2.5188 | 2.0 | 126 | 0.2158 | 0.937 |
| 0.9382 | 3.0 | 189 | 0.1489 | 0.953 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
- Task
- Image Classification
- Library
- transformers
- Type
- siglip
- License
- apache-2.0
- Created
- Dec 8, 2025
- Updated
- Dec 8, 2025