Instructions to use ProbeX/Model-J__ResNet__model_idx_0540 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_0540 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_0540") 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_0540") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0540") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc5bcd64d05157045450ec046099034a7d40a3b7b8fd3c5c2b29654c3ba76530
- Size of remote file:
- 5.37 kB
- SHA256:
- fcb31841e8c1b52ec8978676b086bcce93f426cd5715e0a69a9c73e4f78c97e8
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