Instructions to use ProbeX/Model-J__ResNet__model_idx_0502 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_0502 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_0502") 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_0502") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0502", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9eec2fff637c81353b8abc75f9d136af04a5554c43211ab67ddd5170dd7ceec6
- Size of remote file:
- 5.37 kB
- SHA256:
- d23fe0b0fa28824e1920e6e39e2964bf6556748aab12af0b9c9d99041effb5a0
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