Instructions to use ProbeX/Model-J__ResNet__model_idx_0925 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_0925 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_0925") 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_0925") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0925", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b9012519049d48dbd30a98580215be160c76e79a1b20136bebc198ad2439a8b
- Size of remote file:
- 5.37 kB
- SHA256:
- 33f2c07c1f09206f358f92aa9db3007d82c502e4331abe6538c414eef47c0840
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.