Instructions to use ProbeX/Model-J__ResNet__model_idx_0716 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_0716 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_0716") 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_0716") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0716") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01488f1938d7b4a6ffde26184fb1fe88042ac3b598d654cc102375d23e6a750d
- Size of remote file:
- 5.37 kB
- SHA256:
- 10d963783a66f01efb9d5f0f96971e31a457432edf471c7d4ace17939f394f18
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