Instructions to use ProbeX/Model-J__ResNet__model_idx_0619 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_0619 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_0619") 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_0619") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0619", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4473c742b7ea630a2ec785a4a8c3c60788144077d257d80c7a9d8c6f6060b1a
- Size of remote file:
- 171 MB
- SHA256:
- 54ca43777014de227e6bf256147f1b84a382bf5b8f5c13d632b2cc91c79a1fed
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