Instructions to use ProbeX/Model-J__ResNet__model_idx_0240 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_0240 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_0240") 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_0240") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0240") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d837e1c9a378dc2c7d7e58a19ed0fdf62da9cf07c1fd6a44da4020bfb000ed37
- Size of remote file:
- 5.37 kB
- SHA256:
- 4be742b6a4d87472b22ce014acb54f4d5753573e13e056cc9382c252434d8b14
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