Instructions to use ProbeX/Model-J__ResNet__model_idx_0482 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_0482 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_0482") 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_0482") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0482", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea0c81a77b57df814401c2e4afc6a498d158435f69113e09712fa3f10921c703
- Size of remote file:
- 171 MB
- SHA256:
- 338d0e15edbcbfae34dd79dd36b468c89482d017b55a10d7afbd83638dc5c81f
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