Instructions to use ZafarLocAI/mar_20_class_split_with_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_with_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_with_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_with_class_weights") model = AutoModelForImageClassification.from_pretrained("ZafarLocAI/mar_20_class_split_with_class_weights", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a060a1cba85bd6901a8e5a6aca7a44d3bf250ce86bc4275b317f75d4fd24a35
- Size of remote file:
- 786 MB
- SHA256:
- e421c03266670f59d5092f7c6d240a8e48fa97e670d87ca96f7986edc4701a13
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