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") 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f79279ef9f01663fd6e292d625d388f6324bc7b08cbe57d0dd1214a74e86f3e
- Size of remote file:
- 343 MB
- SHA256:
- 0873f7b3fd2d67372d65b8d9f0c29ece3f6649a9f03ad59e04bf11060ec93084
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