Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use Bazaar/cv_bird_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_bird_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Bazaar/cv_bird_classification", device_map="auto") 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_bird_classification") model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_bird_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 804742fe6e7c881dcf5f2d03c3b04fc2118e977dd8d16b773c9741edede241dd
- Size of remote file:
- 343 MB
- SHA256:
- caaae0b30c1270458d9bde14861f5d8f5e1d4eb9c63de0349b3d82304cb0a114
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