Instructions to use ProbeX/Model-J__ResNet__model_idx_0551 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_0551 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_0551") 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_0551") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0551", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fef0993523e93eae8099df5594ec8f050022e4e4c348c5dd1f26dfa9a20e6377
- Size of remote file:
- 5.37 kB
- SHA256:
- ec4d3f5442b4cbf155d30ccd22fecf6355ea8b2b2778a57035c3dc8fe2774b15
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