Instructions to use ProbeX/Model-J__ResNet__model_idx_0519 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_0519 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_0519") 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_0519") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0519", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- daf2771bff80c02beebe163affd7c99e8dfa065ae80cf8c62b0f82016868533b
- Size of remote file:
- 171 MB
- SHA256:
- a281a6b14b0a504e28c15ffe04d506789e33bfa7dd3271296813d2c6f801dc88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.