Instructions to use ProbeX/Model-J__ResNet__model_idx_0045 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_0045 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_0045") 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_0045") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0045", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bef4aaaaa770563986e1ddc145d390f389ecc1bd29819f76309333705ea52838
- Size of remote file:
- 5.37 kB
- SHA256:
- a6c398cb9239486a68f978ce4395df4aa36885f153efe8f34ab2dba1d9dc09d2
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