Instructions to use ProbeX/Model-J__ResNet__model_idx_0634 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_0634 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_0634") 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_0634") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0634", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca4b58f9a77edcf8149d25d77c5cb44aa30b4a5f2692679ba17d40df61e28a8f
- Size of remote file:
- 171 MB
- SHA256:
- ff981792141b76fd0f92cee2fb143dae0ee5165b5a6c19b84cca99fe0ceb1de0
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