Instructions to use ProbeX/Model-J__ResNet__model_idx_0123 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_0123 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_0123") 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_0123") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0123", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49b191c2fc9c0aeb2c18babba480e54bd43a0b993f2ab2056b66863f51843d52
- Size of remote file:
- 5.37 kB
- SHA256:
- 5494be6a2696b624f526bffe542644db193bf641099c4fbd9318c6df761501e5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.