Instructions to use ProbeX/Model-J__ResNet__model_idx_0940 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_0940 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_0940") 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_0940") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0940", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95998329bee7988f84dfc79d1b38c667e21dc9ea1013908fcb51a7f1e81ab456
- Size of remote file:
- 171 MB
- SHA256:
- 59cf5a515bc100afce1bc01c96dbe71aa8213ec06d377aca5849f6832f8eb5e5
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