Instructions to use ProbeX/Model-J__ResNet__model_idx_0948 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_0948 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_0948") 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_0948") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0948", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ccbc6b64ab8b76a6d818f9037d33a872b896f221320dc98601b90be7ee7f319
- Size of remote file:
- 5.37 kB
- SHA256:
- a6771719adfcbe71cdc068b246b51e185273e2e320933cf19fd9557f411b447a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.