Instructions to use ProbeX/Model-J__ResNet__model_idx_0338 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_0338 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_0338") 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_0338") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0338", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0338: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0338/resolve/main/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0338/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0338/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 5496b051c131f6c887a55595df104ba8b7324c92450a837e5dbaa4bf732ad999
- Size of remote file:
- 5.37 kB
- SHA256:
- 08388562836391b0346eea2640a7e10edc98704de4ce91aeec0f836d97fb7be6
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