Instructions to use ProbeX/Model-J__ResNet__model_idx_0484 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0484 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0484") 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("ProbeX/Model-J__ResNet__model_idx_0484") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0484", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 954b8ccfefb5c13ed8646f0281dd1db0b2421f57969c244ecf80f4db68e330ac
- Size of remote file:
- 171 MB
- SHA256:
- d6f698e86674db3e3120a9dc8fd7858f48037441f6b31b3786ef6382839e2257
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