Instructions to use ProbeX/Model-J__ResNet__model_idx_0899 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_0899 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_0899") 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_0899") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0899", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97a550f0ab902e8a2783272e95983550dc27468302ae4c03e4c1c966f040b802
- Size of remote file:
- 171 MB
- SHA256:
- 6bb10e749c13b77534238f8fece4ce05aa36ee3b5a5b02ef7c8f3bd447db0d73
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