Instructions to use ProbeX/Model-J__ResNet__model_idx_0974 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_0974 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_0974") 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_0974") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0974", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 296f0e5b502dde85c79c0add14fd649e21564a48e0f9c5ade32acc02aaa654de
- Size of remote file:
- 5.37 kB
- SHA256:
- ea1344a8bd847c5ef2d082df43ebb8f26f5ec2965401d83c781b5f15205935ec
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