Instructions to use ProbeX/Model-J__ResNet__model_idx_0307 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_0307 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_0307") 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_0307") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0307", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dff1b97465d1d4994e8a6794c2e648534df8d0f073133d0c92203008b28d8612
- Size of remote file:
- 5.37 kB
- SHA256:
- 0cf4094ab30982cd7875992e1d7510315585391a278330a78c37d8badd93388d
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