Instructions to use ProbeX/Model-J__ResNet__model_idx_0243 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_0243 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_0243") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0243") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0243", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0243: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0243/resolve/9a34e8a6492fc04ead7fc9131343cbcfbfdd7fa7/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0243@9a34e8a6492fc04ead7fc9131343cbcfbfdd7fa7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0243/resolve/9a34e8a6492fc04ead7fc9131343cbcfbfdd7fa7/training_args.bin
5.37 kB
- Xet hash:
- 0a2adc67b82ea3c5d98e58db38e97e74d10b6901adc09f4f3af6fd1d9ffa9d1f
- Size of remote file:
- 5.37 kB
- SHA256:
- 1828ec8bbacbdbcc012f9e1fa55533b510ecc4b38db3cc5fddb26c9a75c1eb00
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