Instructions to use ProbeX/Model-J__ResNet__model_idx_0602 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_0602 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_0602") 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_0602") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0602") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca853a6387867a7b3c22b7376507e629dcbf8d2b40c9da6975504241a75f9b23
- Size of remote file:
- 5.37 kB
- SHA256:
- 3e9b4e4565b96bf6a397bc919effde5f0d13d2c3118122ab1ee33169263a2236
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