Instructions to use ZafarLocAI/convnext_checkpoints_with_class_weight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZafarLocAI/convnext_checkpoints_with_class_weight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ZafarLocAI/convnext_checkpoints_with_class_weight") 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("ZafarLocAI/convnext_checkpoints_with_class_weight") model = AutoModelForImageClassification.from_pretrained("ZafarLocAI/convnext_checkpoints_with_class_weight", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84165024263f42f3fd7b6dfcc2b7e830c7e72c3475057f9e743f7ec5ebe34cc4
- Size of remote file:
- 786 MB
- SHA256:
- 9f89894a27e94046619034894321633029f025c982a0b30a3c9d5d9e147097c5
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