Instructions to use ProbeX/Model-J__ResNet__model_idx_0585 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_0585 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_0585") 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_0585") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0585", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ba6dfd2a37db425c676aed215e32a67dd4e5bd8f78e178c7c9b8da27ab14464
- Size of remote file:
- 171 MB
- SHA256:
- ebdf857222dd8b0856e8271ec8c471df937cc1c2e9fe071ba47269d5794d8416
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.