Instructions to use ProbeX/Model-J__ResNet__model_idx_0688 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_0688 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_0688") 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_0688") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0688", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7c77be95aa8e14826e12bd68f4de5500ee38c0dc1f0b4e3e779c62dac2f2e69
- Size of remote file:
- 5.37 kB
- SHA256:
- 56e6a82ca92d5a59daf4ca8cb55bcac405e8987bcf4c6146864968c51b30c649
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.