Instructions to use ZafarLocAI/mar_20_class_split_without_class_weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZafarLocAI/mar_20_class_split_without_class_weights with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ZafarLocAI/mar_20_class_split_without_class_weights") 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/mar_20_class_split_without_class_weights") model = AutoModelForImageClassification.from_pretrained("ZafarLocAI/mar_20_class_split_without_class_weights", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd90eb3baf0941d9f297f99e04c96e6bd58c374c3003f2ed0a7cee47e1d341cb
- Size of remote file:
- 786 MB
- SHA256:
- 6f657b0cc0ff4f1eaf5e73f811613d06799cea5d63cb834254ad3e2ae19f1590
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