Instructions to use ProbeX/Model-J__ResNet__model_idx_0959 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_0959 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_0959") 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_0959") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0959", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0959: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0959/resolve/3f7302506a35f4e5f062aa4ed69a9e9d4e4ce371/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0959@3f7302506a35f4e5f062aa4ed69a9e9d4e4ce371/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0959/resolve/3f7302506a35f4e5f062aa4ed69a9e9d4e4ce371/training_args.bin
5.37 kB
- Xet hash:
- de1f781daad18c9e9d5ab5cf30e81b641afe126bcd28a3f0bfafd3b3173de779
- Size of remote file:
- 5.37 kB
- SHA256:
- f9a380e608f5d5c9d799a7ffcc313293237f0ad3792d49ed9825e375fd47d76f
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