Instructions to use ProbeX/Model-J__ResNet__model_idx_0632 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_0632 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_0632") 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_0632") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0632", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d53a6d92c0e340c4fdec057f5a207c44f2a856162e3c9283e4bf4ded9fdf0966
- Size of remote file:
- 171 MB
- SHA256:
- 66d3991204dfe7911d8a0fc89d645c328c00583bc073b96903111c3332953bec
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