Instructions to use ProbeX/Model-J__ResNet__model_idx_0849 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_0849 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_0849") 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_0849") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0849", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63d4204a7d24e79398ab0b7c56a3bb8cd69bca2526511aaf8ef939b3e8dadc57
- Size of remote file:
- 5.37 kB
- SHA256:
- fed285994c38ab80f3e0688823651e2de44324cb702018dde1787d4ddd738223
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.