Instructions to use amoghajnalens/siglip2-base-patch16-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amoghajnalens/siglip2-base-patch16-512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amoghajnalens/siglip2-base-patch16-512") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("amoghajnalens/siglip2-base-patch16-512") model = AutoModelForImageClassification.from_pretrained("amoghajnalens/siglip2-base-patch16-512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
siglip2-base-patch16-512
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5239
- Roc Auc: 0.9741
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|---|---|---|---|---|
| 1.9038 | 1.9268 | 40 | 0.5393 | 0.9497 |
| 0.5604 | 3.8293 | 80 | 0.3889 | 0.9759 |
| 0.1351 | 5.7317 | 120 | 0.5239 | 0.9741 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
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