Instructions to use ProbeX/Model-J__ResNet__model_idx_0421 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_0421 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_0421") 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_0421") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0421") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25398de1a003458dbd69b5718b337f55ea49c4110ae3b6c89e731226eb6a5ebb
- Size of remote file:
- 171 MB
- SHA256:
- 6ad23e6689aa240d14bfaab40dff9f925e0d0d410137172dc93b5b9c2eded5d1
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