Instructions to use ProbeX/Model-J__ResNet__model_idx_0953 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_0953 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_0953") 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_0953") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0953", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67074bb904227d9e60c41ea4869f567ce0d8dad4ec750c5d05f79ce2281a79c0
- Size of remote file:
- 171 MB
- SHA256:
- 2d0a1c96efaac77935494ac0b96c4c9ce8fb1ec6c0bb5469734e2aca3c3e9b3c
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