Instructions to use ProbeX/Model-J__ResNet__model_idx_0850 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_0850 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_0850") 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_0850") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0850", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b87520d5960fd9a6e9cfa1939fed064cce3eccbf7241c8da822113e4fde513ca
- Size of remote file:
- 5.37 kB
- SHA256:
- be092ac6d942d8170e7846a1c0946a6955449bcdddf6a2b2f8fc32a5fe29544d
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