Instructions to use ProbeX/Model-J__ResNet__model_idx_0188 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_0188 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_0188") 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_0188") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0188") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd001689aade095e21deafa2f0a1976cd6bdfe38b301047373885efff64897f3
- Size of remote file:
- 5.37 kB
- SHA256:
- 8089f9d01aa081ec185124b900aeb4df13edc1013aafb9da710eea674f22b026
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