Instructions to use ProbeX/Model-J__ResNet__model_idx_0930 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_0930 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_0930") 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_0930") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0930") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b2da13c5bc4cb2e430ea132d8f300c221daa2971cc583ae0a7daefa0531a7f6
- Size of remote file:
- 5.37 kB
- SHA256:
- 95e33d98b3733ef4cb4dd80b71c302ea2459e95258dae7c42c417cbe292a100e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.