Instructions to use ProbeX/Model-J__ResNet__model_idx_0752 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_0752 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_0752") 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_0752") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0752", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f56cd30fc7e26c868a4f1e4e168d65f3be051bb281f4e5fa1bdf056da1c80f5
- Size of remote file:
- 171 MB
- SHA256:
- c52fb7ec77c977dc22daf73e0ddf9a607f820337eb0d9b925aba650fb54904dd
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