Instructions to use ProbeX/Model-J__ResNet__model_idx_0328 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_0328 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_0328") 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_0328") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0328", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 241369035cd40a281e76d8d5ad4bb65e2a687d91bb6538a7a1de8fd141f2d763
- Size of remote file:
- 171 MB
- SHA256:
- bceaef9fbe71a282bb02c5c49620616a997e994ca2d6ccbd93931ad07b2da3ff
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