Instructions to use ProbeX/Model-J__ResNet__model_idx_0766 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_0766 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_0766") 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_0766") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0766", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96c393012ee722c8e07f9be802180fc9a0c07cc6205aafeba43a95e7e5ef18f1
- Size of remote file:
- 5.37 kB
- SHA256:
- 7a7d4680ef1b00d22d15b08dab5dbdcafa777e39aeab4118884cb34f92fbbc90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.