Instructions to use ProbeX/Model-J__ResNet__model_idx_0197 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_0197 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_0197") 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_0197") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0197", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 828e178639b33ec66f3f27045e8a3fc6eaaf66ec42119e994ab24be7c2685ba6
- Size of remote file:
- 171 MB
- SHA256:
- 50c040cde08ccaf5cf25558a7288047a1a6f0b2d10b9d28b8ab32964936260eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.