Instructions to use ProbeX/Model-J__ResNet__model_idx_0601 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_0601 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_0601") 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_0601") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0601", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69b547776976ccfc99b3be0df1297e765d9126f68ed18591811c0d0f8be89858
- Size of remote file:
- 171 MB
- SHA256:
- 92f13b553955ce1ca61eddc2312136a2f870f1ff51c5e9411f7f6213b36432a4
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