Instructions to use ProbeX/Model-J__ResNet__model_idx_0749 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_0749 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_0749") 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_0749") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0749") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5833d3a4afa7f548d632e707bb100df11861372cd0a6c381b9d329c35a483bed
- Size of remote file:
- 171 MB
- SHA256:
- 838bbc60bc0910affed208fd22faf8d29c33513cc722e69814bf85b88be44f8f
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