Instructions to use ProbeX/Model-J__ResNet__model_idx_0963 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_0963 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_0963") 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_0963") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0963") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 651df8ebb96352e85ea628d7e086f94f6fd94b484262ca10e8cd295f7c80c72c
- Size of remote file:
- 5.37 kB
- SHA256:
- 7244f7ce8a96df9e2b28a8be39520e75112b468b43c4b4e0ee37e19e334c48b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.