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")# 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) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2f0c074d062df6d6801e693e986df63c42731cac8a408cfc6b55b4d5db4d63b
- Size of remote file:
- 4.73 kB
- SHA256:
- 0427472951958484836b7c88470da20e324324aa5f003198da31ce640c92d77c
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