Instructions to use ProbeX/Model-J__ResNet__model_idx_0297 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_0297 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_0297") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0297") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0297", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0297: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0297/resolve/main/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0297/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0297/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- d90a3e1c0d244ab67a09e963e08dad050b32f9e8cb817e339af442bc52506967
- Size of remote file:
- 5.37 kB
- SHA256:
- 952f02a567caa99704512b2856f9eaab3552d6206cbf9cf490eb7fc187b7b6af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.