Instructions to use ProbeX/Model-J__ResNet__model_idx_0337 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_0337 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_0337") 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_0337") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0337", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86b6cf188ba8e640091e75e5620770510ea780aecfb572a51812e0c106283707
- Size of remote file:
- 5.37 kB
- SHA256:
- 6f4eb3db4f15c885ab94cba9bfc60e1302fd7c376fccaa7036878d83d6e91d28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.