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tv-resnet18\n      - tv-resnet34\n      - tv-resnet50\n      - tv-resnet101\n      - tv-resnet152\n  \u003c/details\u003e\n\n  * \u003cdetails\u003e \u003csummary\u003eResNeXt\u003c/summary\u003e\n\n      - tv-resnext50_32x4d\n      - tv-resnext101_32x4d\n      - tv-resnext101_32x8d\n      - tv-resnext101_32x16d\n      - tv-resnext101_32x32d\n      - tv-resnext101_32x48d\n  \u003c/details\u003e\n\n  * \u003cdetails\u003e \u003csummary\u003eDenseNet\u003c/summary\u003e\n\n      - tv-densenet121\n      - tv-densenet169\n      - tv-densenet201\n      - tv-densenet161\n\n  \u003c/details\u003e\n\n  * \u003cdetails\u003e \u003csummary\u003eEfficientNetV1\u003c/summary\u003e\n\n      - timm-efficientnet-b0\n      - timm-efficientnet-b1\n      - timm-efficientnet-b2\n      - timm-efficientnet-b3\n      - timm-efficientnet-b4\n      - timm-efficientnet-b5\n      - timm-efficientnet-b6\n      - timm-efficientnet-b7\n\n  \u003c/details\u003e\n\n  * \u003cdetails\u003e \u003csummary\u003eEfficientNetLite\u003c/summary\u003e\n\n      - timm-efficientnet-lite0\n      - timm-efficientnet-lite1\n      - timm-efficientnet-lite2\n      - timm-efficientnet-lite3\n      - timm-efficientnet-lite4\n\n  \u003c/details\u003e\n\n#### Pretraining\n\n  * Autoencoder\n\n#### Segmentation\n\n  * Unet [[paper](https://arxiv.org/abs/1505.04597)]\n\n## Citing\n\nIf you find this library useful for your research, please consider citing:\n\n```\n@misc{epifanov2023ecgmentations,\n  Author = {Rostislav Epifanov},\n  Title = {NNSTP},\n  Year = {2023},\n  Publisher = {GitHub},\n  Journal = {GitHub repository},\n  Howpublished = {\\url{https://github.com/rostepifanov/nnspt}}\n}\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frostepifanov%2Fnnspt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frostepifanov%2Fnnspt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frostepifanov%2Fnnspt/lists"}