Instructions to use ProbeX/Model-J__ResNet__model_idx_0106 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_0106 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_0106") 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_0106") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0106", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a38ef679c21cb79792a2a2bb7931266df604e18960f0dae769d6a9f3f743218
- Size of remote file:
- 5.37 kB
- SHA256:
- 3fa01743f14a331fecb59d6d9c8a809fa20a46044e2d94858c4c93fd41266a63
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