Instructions to use ProbeX/Model-J__ResNet__model_idx_0187 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_0187 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_0187") 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_0187") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0187", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd7364f8d6507bc41b87ba1c4910dd60698d37ac02b7ebbdb19faeda9b59ac11
- Size of remote file:
- 5.37 kB
- SHA256:
- abe6a2a99ce2aedbe0141a1751072b242ce08c4334e911b83b71f1a0ce9a9f55
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