Update READMEs
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
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Micaela Verucchi
@@ -19,6 +19,7 @@ If you use tkDNN in your research, please cite the [following paper](https://iee
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### What's new (20 July 2021)
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- [x] Support to sematic segmentation [README](docs/README_seg.md)
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- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md)
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- [ ] Support to TensorRT8 (WIP)
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## FPS Results
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@@ -70,15 +71,13 @@ Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
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- [tkDNN](#tkdnn)
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- [Index](#index)
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- [Dependencies](#dependencies)
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- [About OpenCV](#about-opencv)
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- [How to compile this repo](#how-to-compile-this-repo)
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- [Workflow](#workflow)
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- [How to export weights](#how-to-export-weights)
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- [Run the demo](#run-the-demo)
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- [mAP demo](#map-demo)
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- [Exporting weights](#exporting-weights)
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- [Run the demos](#run-the-demos)
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- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
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- [Existing tests and supported networks](#existing-tests-and-supported-networks)
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- [References](#references)
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- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
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## Dependencies
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@@ -97,7 +96,7 @@ sudo apt install libyaml-cpp-dev curl libeigen3-dev
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```
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## About OpenCV
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#### About OpenCV
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To compile and install OpenCV4 with contrib us the script ```install_OpenCV4.sh```. It will download and compile OpenCV in Download folder.
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```
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bash scripts/install_OpenCV4.sh
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@@ -125,15 +124,21 @@ Steps needed to do inference on tkDNN with a custom neural network.
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## Exporting weights
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For specific details on how to export weights see [here](./docs/exporting_weights.md)
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For specific details on how to export weights see [HERE](./docs/exporting_weights.md).
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## Run the demo
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## Run the demos
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For specific details on how to run the demos see [here](./docs/demo.md)
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For specific details on how to run:
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- 2D object detection demos, details on FP16, INT8 and batching see [HERE](./docs/demo.md).
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- segmentation demos see [HERE](./docs/README_seg.md).
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- 2D/3D object detection and tracking demos see [HERE](./docs/README_2d3dtracking.md).
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- mAP demo to evaluate 2D object detectors see [HERE](./docs/mAP_demo.md).
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## mAP demo
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For specific details on how to run the mAP demo see [here](./docs/mAP_demo.md)
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## tkDNN on Windows 10 (experimental)
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For specific details on how to run tkDNN on Windows 10 see [HERE](./docs/windows.md).
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## Existing tests and supported networks
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@@ -160,14 +165,20 @@ For specific details on how to run the mAP demo see [here](./docs/mAP_demo.md)
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| resnet101_cnet | Centernet (Resnet101 backend)<sup>4</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/5BTjHMWBcJk8g3i/download) |
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| csresnext50-panet-spp | Cross Stage Partial Network <sup>7</sup> | [COCO 2014](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/Kcs4xBozwY4wFx8/download) |
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| yolo4 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_320 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 320x320 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_512 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_608 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 608x608 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_berkeley | Yolov4 <sup>8</sup> | [BDD100K ](https://bair.berkeley.edu/blog/2018/05/30/bdd/) | 10 | 540x320 | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download) |
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| yolo4tiny | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 640x640 | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download) |
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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) |
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| yolo4tiny_512 | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
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80 | 640x640 | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download) |
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| yolo4x-cps | Scaled Yolov4 <sup>10</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download) |
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| shelfnet | ShelfNet18_realtime<sup>11</sup> | [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download) |
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| shelfnet_berkeley | ShelfNet18_realtime<sup>11</sup> | [DeepDrive](https://bdd-data.berkeley.edu/) | 20 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download) |
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| dla34_cnet3d | Centernet3D (DLA34 backend)<sup>4</sup> | [KITTI 2017](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) | 1 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/2MDyWGzQsTKMjmR/download) |
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| dla34_ctrack | CenterTrack (DLA34 backend)<sup>12</sup> | [NuScenes 3D](https://www.nuscenes.org/) | 7 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/rjNfgGL9FtAXLHp/download) |
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### tkDNN on Windows 10 (experimental)
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For specific details on how to run the demos on Windows 10 see [here](./docs/windows.md)
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## References
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@@ -181,3 +192,5 @@ For specific details on how to run the demos on Windows 10 see [here](./docs/win
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8. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020).
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9. Bochkovskiy, Alexey, "Yolo v4, v3 and v2 for Windows and Linux" (https://github.com/AlexeyAB/darknet)
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10. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "Scaled-YOLOv4: Scaling Cross Stage Partial Network." arXiv preprint arXiv:2011.08036 (2020).
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11. Zhuang, Juntang, et al. "ShelfNet for fast semantic segmentation." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019.
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12. Zhou, Xingyi, Vladlen Koltun, and Philipp Krähenbühl. "Tracking objects as points." European Conference on Computer Vision. Springer, Cham, 2020.
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