Instructions to use ProbeX/Model-J__ResNet__model_idx_0746 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_0746 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_0746") 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_0746") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0746", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0e203391a54178197fbbd9cbb13b1f41947bb963e97e94af991b700e309aad1
- Size of remote file:
- 171 MB
- SHA256:
- cc04e16045cec0a7c08a261bd16e43bb35a769198a5302d3ef1ee8cd7f360e07
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.