Instructions to use ProbeX/Model-J__ResNet__model_idx_0171 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_0171 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_0171") 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_0171") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0171", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffb3029e0e47278f788b1f461f49e5e99293561d6ca31fb237c402df881718ed
- Size of remote file:
- 171 MB
- SHA256:
- adb4b241a2651a5948b684273aa4a5a9073aeac15992acb778e82b1d72f1622a
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