Instructions to use ProbeX/Model-J__ResNet__model_idx_0934 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_0934 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_0934") 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_0934") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0934", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c72f44e995a50d160729cacd16758d08b804269a8f6d684511167737c21a969a
- Size of remote file:
- 171 MB
- SHA256:
- 6f3f21d6a406dcff5934d337f34e09e410680174ff8111cdfeec89ba094af25d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.