Instructions to use ProbeX/Model-J__ResNet__model_idx_0367 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_0367 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_0367") 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_0367") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0367") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01f08c1ead3d7e09da1c41b45579a0210ae24aa51f714a1b611facdacc9007d1
- Size of remote file:
- 5.37 kB
- SHA256:
- 33f492af2a5d81c700735c8acda1939775102db66ebf231f3bd97b30220231ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.