Instructions to use ProbeX/Model-J__ResNet__model_idx_0795 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_0795 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_0795") 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_0795") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0795", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0795: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0795/resolve/be9651906245e455d5f4ba2e47cb46386f41fa19/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0795@be9651906245e455d5f4ba2e47cb46386f41fa19/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0795/resolve/be9651906245e455d5f4ba2e47cb46386f41fa19/training_args.bin
5.37 kB
- Xet hash:
- 89aaa35ce73d1f5a67f3f7ce899a814e6581fc4b74912f1a66d2abf9c3576e81
- Size of remote file:
- 5.37 kB
- SHA256:
- 34f11a57988665c49ed19a15092092eff9df93e56eee89e60e0b284bedd70777
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