Instructions to use thethinkmachine/teacher-resnet18-tiny-imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thethinkmachine/teacher-resnet18-tiny-imagenet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="thethinkmachine/teacher-resnet18-tiny-imagenet") 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("thethinkmachine/teacher-resnet18-tiny-imagenet") model = AutoModelForImageClassification.from_pretrained("thethinkmachine/teacher-resnet18-tiny-imagenet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from thethinkmachine/teacher-resnet18-tiny-imagenet: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/thethinkmachine/teacher-resnet18-tiny-imagenet/resolve/main/training_args.bin
- Command line
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hf download hf://thethinkmachine/teacher-resnet18-tiny-imagenet/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/thethinkmachine/teacher-resnet18-tiny-imagenet/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- f22897f0086cc519a0e8ae006dfa612d2a88fedc9b258c517734354ba6d1d786
- Size of remote file:
- 5.84 kB
- SHA256:
- f5cc1abb3c90a6135d941ffcd4bcb70a9c8e41ca96fe54ad1dd5ef7a1a07f8f1
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