Instructions to use ProbeX/Model-J__ResNet__model_idx_0003 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_0003 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_0003") 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_0003") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0003") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4ef4a283d279344aba5ec8ddff6f71fe6500177dbfb5e4b0cd2c084403a69c5
- Size of remote file:
- 5.37 kB
- SHA256:
- fe490f78e9288f27d67a9f1a2fcecc77bc147006846ccff3367065da05f7a463
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.