Instructions to use ProbeX/Model-J__ResNet__model_idx_0977 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_0977 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_0977") 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_0977") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0977", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0771ba15f54807a60e9ed0ed514459c542f459699e093fe1325a3a399371b616
- Size of remote file:
- 5.37 kB
- SHA256:
- 329b468dfc07ba7e11baf0ee1a24da07f5bfc4c3f99eabba88c9d2689e5675c0
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