Instructions to use ProbeX/Model-J__ResNet__model_idx_0879 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_0879 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_0879") 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_0879") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0879", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 581fee837a5628189fe8a89e56bfbde3371938386665df3c02cc26cc4f6dea7f
- Size of remote file:
- 171 MB
- SHA256:
- b161ee1bb6e9a0b8ea254c5f60b015460dc14ee4984763be2aed67e8d8de8cc1
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