Image Classification
Transformers
Safetensors
siglip_vision_model
Generated from Trainer
siglip
custom_code
Instructions to use p1atdev/siglip-tagger-test-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p1atdev/siglip-tagger-test-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="p1atdev/siglip-tagger-test-3", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("p1atdev/siglip-tagger-test-3", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("p1atdev/siglip-tagger-test-3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from p1atdev/siglip-tagger-test-3: direct link, hf CLI and curl.
- Browser
- Download file 368 Bytes
-
https://huggingface.co/p1atdev/siglip-tagger-test-3/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://p1atdev/siglip-tagger-test-3@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/p1atdev/siglip-tagger-test-3/resolve/refs%2Fpr%2F2/preprocessor_config.json
368 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "SiglipProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| } | |
| } | |