Instructions to use ProbeX/Model-J__ResNet__model_idx_0565 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_0565 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_0565") 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_0565") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0565") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5aefaad61f52bb1a6977f76817e57d09b9fec6d989fc809707c201257aa859a9
- Size of remote file:
- 5.37 kB
- SHA256:
- 959908bbcea5e4b439076dad9aff0b53a1b2542117171432ff8bdd6154f3b82d
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