Instructions to use ProbeX/Model-J__ResNet__model_idx_0967 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_0967 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_0967") 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_0967") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0967", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b532f0b78fc9bc512658bb4f7ad8307cc33bd23f7b19d2ced3b053966a1be0e3
- Size of remote file:
- 5.37 kB
- SHA256:
- 6fbbe8d089da39cb6dffeffd1d86160afa23a50c94416344d70c280d67171fb8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.