Instructions to use ProbeX/Model-J__ResNet__model_idx_0754 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_0754 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_0754") 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_0754") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0754", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ecd9236ffe901afcc859fca074e3b83ee0bd9fb5a0e4fc2d595270ac7518e81
- Size of remote file:
- 171 MB
- SHA256:
- ddfcc52046e39b1d5c98c3d13884be7232954006362777630010d8e4c3282cfe
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