Instructions to use ProbeX/Model-J__ResNet__model_idx_0738 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_0738 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_0738") 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_0738") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0738", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23cb9b73085b789ae4ec58ad3be54e5d3ab01d0cb9c52c0a0ca5e76c9849fd4d
- Size of remote file:
- 171 MB
- SHA256:
- d1ca83534f5b65cb24d5b1d995c3f7285038c35f2c8d75823432f237a8eb6e0c
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