Instructions to use ProbeX/Model-J__ResNet__model_idx_0539 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_0539 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_0539") 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_0539") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0539", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b59dec1e8580946e33dc0e8f26985ec35b531ba1cf2ab91cf81877d1cd2dca3
- Size of remote file:
- 171 MB
- SHA256:
- ca23b6fe21786f86ae8b15399f7e0a6fdb0eb75a2abbfd57f9894083247a0910
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