Test Resnet.r160 In1k is a model published on Hugging Face as part of the timm PyTorch Image Models collection. It is an image classification model trained on the imagenet-1k dataset and provided with an apache-2.0 license.
The model can be loaded and used through the timm library by calling timm.create_model with the identifier "hf_hub:timm/test_resnet.r160_in1k" and the pretrained flag set to true. It is also compatible with the Hugging Face Transformers library, where it supports the image-classification pipeline and direct loading via AutoModel.from_pretrained with an optional device_map parameter. Example inference code demonstrates classification of an image URL using the pipeline.
Publication on the Hugging Face hub makes the model available for download in PyTorch and Safetensors formats. The repository includes a model card that supplies usage instructions for libraries, inference providers, notebooks, and local applications, along with links to Google Colab and Kaggle environments. It belongs to the class of image classification models within the timm collection.
Test Resnet.r160 In1k sits in PulseGate's Other AI category. It focuses on providing a lightweight test model for validating timm and Transformers pipelines. It is built as an open-source project for machine learning engineers. Test Resnet.r160 In1k is open source under the Apache-2.0 license. It runs on the web and API.
Ross Wightman builds and maintains Test Resnet.r160 In1k, and the product first shipped in 2019. Development happens publicly on GitHub with 37k stars and 32 commits in the last 90 days.
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