Instructions to use ProbeX/Model-J__ResNet__model_idx_0543 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_0543 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_0543") 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_0543") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0543", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c7d71f62f7a47e4369b28d1675a321a24a306eccbee9ec43f09b54b96330789
- Size of remote file:
- 5.37 kB
- SHA256:
- 39be5f39fec4a1a15e11e298e003900bfcf41ce520a9bd3fc32f6fd179723d91
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