Instructions to use ProbeX/Model-J__ResNet__model_idx_0761 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_0761 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_0761") 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_0761") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0761", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29ab0d7d5ab2adf7de49b7a652e4da253d921d0871a2333c2f5b0b3f1b0f0a73
- Size of remote file:
- 171 MB
- SHA256:
- 98b7a0479b6f5989803948cf6bb2d77d13afc08244c4f594f7594306bb544319
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