Instructions to use ProbeX/Model-J__ResNet__model_idx_0835 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_0835 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_0835") 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_0835") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0835", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24258dfcdf8de9936952e3e0f402a2e5bb2e38c68fa0bb57e4b0eb1360bd6447
- Size of remote file:
- 171 MB
- SHA256:
- 43965c1257475d9982ba817b9292d0d0289dcb0a4c1ff2dfbb775a0f4b11e0d7
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