Instructions to use ProbeX/Model-J__ResNet__model_idx_0907 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_0907 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_0907") 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_0907") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0907", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f84d8e3ffb67a1398d60805146427fa651ca5e3c0cec9355e1688bf680f69e0
- Size of remote file:
- 171 MB
- SHA256:
- 34934e7d8475eca33d14cf560fb12dca8ed3808736cc475de6ba832386da9529
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