Instructions to use ProbeX/Model-J__ResNet__model_idx_0848 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_0848 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_0848") 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_0848") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0848", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f300627f6577e7774e78669212b6099d44df2377c76d6a49de777383b0ed18f5
- Size of remote file:
- 171 MB
- SHA256:
- 0b60803b7e77fd4a7f0e3bed331d842b2aa991b844344c48570f5351bc1aa8cf
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