Instructions to use ProbeX/Model-J__ResNet__model_idx_0216 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_0216 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_0216") 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_0216") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0216", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a108e466ab9dbcf8ef0d4e09de7df8969ea19c735a8798aadf7f3c29ae3d581
- Size of remote file:
- 5.37 kB
- SHA256:
- e3bba6d4c8f2ed25fcbd50bb11bc112a9c42c38021c32352b6b97ff338d4679b
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