Instructions to use ProbeX/Model-J__ResNet__model_idx_0294 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_0294 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_0294") 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_0294") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0294", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8be3ca290e366b78f796e1b90f8852074b61d7494667e6ba94c0e90cbbbccd92
- Size of remote file:
- 171 MB
- SHA256:
- 95932bd4b54733304d80b16fdb893db7ad0dfa5e06a157ee7a9a25e0a81f164d
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