Instructions to use ProbeX/Model-J__ResNet__model_idx_0737 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_0737 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_0737") 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_0737") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0737", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eda7089d903d157688f35e1359fda63c6ae6a2d0e55faf6e8e577ab9c1bb81f4
- Size of remote file:
- 171 MB
- SHA256:
- 895d265009688d831571f0e0f315646a53b8f714e2adae59ed371dea13450e58
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