Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use Bazaar/cv_level1_protected_animals_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_level1_protected_animals_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Bazaar/cv_level1_protected_animals_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Bazaar/cv_level1_protected_animals_classification") model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_level1_protected_animals_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53c1636e2f2dcb07a86ad2611e59af27937e4009adab96d83f565569d37a5bda
- Size of remote file:
- 343 MB
- SHA256:
- d6f46ce25da3b7359b8c1cf7a5ca0aef2eb97291f67045dfa48466321d27eaa1
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