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Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms
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# tkDNN tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier, Nano and several discrete GPUs. The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training. If you use tkDNN in your research, please cite the [following paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9212130&casa_token=sQTJXi7tJNoAAAAA:BguH9xCIY48MxbtDS3LXzIXzO-9sWArm7Hd7y7BwaLmqRuM_Gx8bOYizFPNMNtpo5K0kB-P-). For use in commercial solutions, write at [email protected] and [email protected] or refer to https://hipert.unimore.it/ . ``` @inproceedings{verucchi2020systematic, title={A Systematic Assessment of Embedded Neural Networks for Object Detection}, author={Verucchi, Micaela and Brilli, Gianluca and Sapienza, Davide and Verasani, Mattia and Arena, Marco and Gatti, Francesco and Capotondi, Alessandro and Cavicchioli, Roberto and Bertogna, Marko and Solieri, Marco}, booktitle={2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, volume={1}, pages={937--944}, year={2020}, organization={IEEE} } ``` ### What's new #### 20 July 2021 - [x] Support to sematic segmentation [README](docs/README_seg.md) - [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md) #### 24 November 2021 - [x] Support to sematic segmentation on cuda 11 - [x] Support to TensorRT8. (thanks to [Harshvardhan Chandirasekar](https://github.com/perseusdg)) #### 30 March 2022 - [x] Support to monocular depth esitmation [README](docs/README_depth.md) (thanks to [Harshvardhan Chandirasekar](https://github.com/perseusdg)) ## FPS Results Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5); * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 ); * Xavier NX, Jetpack 4.4 (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ). * Tx2, Jetpack 4.2 (CUDA 10.0, CUDNN 7.3.1, tensorrt 5.0.6 ); * Jetson Nano, Jetpack 4.4 (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ). | Platform | Network | FP32, B=1 | FP32, B=4 | FP16, B=1 | FP16, B=4 | INT8, B=1 | INT8, B=4 | | :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | | RTX 2080Ti | yolo4 320 | 118.59 | 237.31 | 207.81 | 443.32 | 262.37 | 530.93 | | RTX 2080Ti | yolo4 416 | 104.81 | 162.86 | 169.06 | 293.78 | 206.93 | 353.26 | | RTX 2080Ti | yolo4 512 | 92.98 | 132.43 | 140.36 | 215.17 | 165.35 | 254.96 | | RTX 2080Ti | yolo4 608 | 63.77 | 81.53 | 111.39 | 152.89 | 127.79 | 184.72 | | AGX Xavier | yolo4 320 | 26.78 | 32.05 | 57.14 | 79.05 | 73.15 | 97.56 | | AGX Xavier | yolo4 416 | 19.96 | 21.52 | 41.01 | 49.00 | 50.81 | 60.61 | | AGX Xavier | yolo4 512 | 16.58 | 16.98 | 31.12 | 33.84 | 37.82 | 41.28 | | AGX Xavier | yolo4 608 | 9.45 | 10.13 | 21.92 | 23.36 | 27.05 | 28.93 | | Xavier NX | yolo4 320 | 14.56 | 16.25 | 30.14 | 41.15 | 42.13 | 53.42 | | Xavier NX | yolo4 416 | 10.02 | 10.60 | 22.43 | 25.59 | 29.08 | 32.94 | | Xavier NX | yolo4 512 | 8.10 | 8.32 | 15.78 | 17.13 | 20.51 | 22.46 | | Xavier NX | yolo4 608 | 5.26 | 5.18 | 11.54 | 12.06 | 15.09 | 15.82 | | Tx2 | yolo4 320 | 11.18 | 12.07 | 15.32 | 16.31 | - | - | | Tx2 | yolo4 416 | 7.30 | 7.58 | 9.45 | 9.90 | - | - | | Tx2 | yolo4 512 | 5.96 | 5.95 | 7.22 | 7.23 | - | - | | Tx2 | yolo4 608 | 3.63 |
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matched fp:0763a61720f53dc9, llm:description: 'Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms' (repo description)
matched fp:0763a61720f53dc9, llm:description: 'Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms' (repo description)
matched fp:0763a61720f53dc9, llm:description: 'Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms' (repo description)