Instructions to use ProbeX/Model-J__ResNet__model_idx_0916 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_0916 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_0916") 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_0916") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0916", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c53518dfdb14b6a96b614d837c4c7c55c8c393eafe52bf996d8cafbdbd1e90c3
- Size of remote file:
- 171 MB
- SHA256:
- 62d20f0d59e5bba3c833bbb3635bf3e3f9e4ce9edef47b20816df33cac7b5309
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