Instructions to use ProbeX/Model-J__ResNet__model_idx_0135 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_0135 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_0135") 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_0135") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0135") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04b1b13630fa9a118ee16e67be1b6fdf3ca562bbe7b732c3afd5f00008bf8965
- Size of remote file:
- 5.37 kB
- SHA256:
- e1de93eeaf02baef182ca6e2cf0d2109b920162feff29b1c3b05f0f5612c11c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.