Instructions to use ProbeX/Model-J__ResNet__model_idx_0832 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_0832 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_0832") 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_0832") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0832", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e78cf365b87a35ed17038b414891045ea364ac73db55ca1fd24f6517da1f2819
- Size of remote file:
- 5.37 kB
- SHA256:
- d7810202030b1e91d6543711f32b5ca5fc0979b2d90db8654518569a5409ffa9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.