Instructions to use ProbeX/Model-J__ResNet__model_idx_0745 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_0745 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_0745") 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_0745") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0745") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4a87cc6473edfb67dc1698de7e653f53858b60a50c4e0f48396c0d053243a6b
- Size of remote file:
- 5.37 kB
- SHA256:
- 867ec463f7b7083fb309950897d957434741ac904febec0b3c8ed3826a458d92
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