Instructions to use ProbeX/Model-J__ResNet__model_idx_0068 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_0068 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_0068") 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_0068") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0068", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0068: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0068/resolve/692b013c3822743d4c266eed3405ab59aec5b58e/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0068@692b013c3822743d4c266eed3405ab59aec5b58e/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0068/resolve/692b013c3822743d4c266eed3405ab59aec5b58e/training_args.bin
5.37 kB
- Xet hash:
- 41592bb93f1e2428f2b7282e0026625f6cbebc347bb8912924a643680bc6e9f5
- Size of remote file:
- 5.37 kB
- SHA256:
- 79f611e753c11f88e1d169ac345a9932e7123152e2913a922c80ba9b2a7839ce
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