Instructions to use ProbeX/Model-J__ResNet__model_idx_0859 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_0859 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_0859") 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_0859") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0859", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce3eec904d51a7b0dd3ae77f2d56552be889c8aa831d6188864b07d71b8083bc
- Size of remote file:
- 5.37 kB
- SHA256:
- 9b1254aa0dab10bc9df308fbf995728ef5d8bb8948e037f4b9bb0604d3e906ed
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