Instructions to use ProbeX/Model-J__ResNet__model_idx_0176 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_0176 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_0176") 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_0176") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0176", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae023d423d6914317274a067e8b4950f7685b3b6613c33455cd9c5df40908df9
- Size of remote file:
- 5.37 kB
- SHA256:
- d0db9d3235cdf1e54c6c3678e080d42682bf368fbd69484f726711c3ef4d9cb3
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