Instructions to use ProbeX/Model-J__ResNet__model_idx_0720 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_0720 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_0720") 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_0720") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0720", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cefc77d2186942bb2a40fccd52f1e25308078bb5457e40423dbd54707118a54
- Size of remote file:
- 171 MB
- SHA256:
- 22710307a1acf2ab2da689d04783a9efb0136eab7c3eb749b6e7d077350e4eab
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