VCK190 | ZCU102 | ZCU104 | KV260 | VCK5000 | U200 | U200 | U250 | ||||||||||||||||||||||||||||||
Vitis AI Model Zoo Details &
Performance Copyright 2022 Advanced Micro Devices, Inc. |
1* DPUCVDX8G 192 AIEs (C32B6CU1L2S2) @ 1250MHz | 3* DPUCZDX8G B4096 @ 281MHz Note: data is applicable to board revision 0432055-05, see Vitis AI release 2.5 for board revision 0432055-04 performance metrics |
2 * DPUCZDX8G B4096 @ 300MHz | 1* DPUCZDX8G B4096F @ 300MHz | 4PE DPUCVDX8H @350MHz Gen4x8 | 6PE DPUCVDX8H-aieDWC @350MHz Gen4x8 | 6PE DPUCVDX8H-aieMISC @350MHz Gen4x8 | 8PE DPUCVDX8H @350MHz Gen4x8 | 2* DPUCADF8H @300MHz | 2* DPUCADF8H @350MHz xilinx_u200_xdma_201830_2 shell |
4* DPUCADF8H @300MHz |
4* DPUCADX8G @350Mhz |
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Task | Market Specialization | Application | Framework | Vitis-AI Model Name Zoo Name | License Restriction(s) | Model Architecture | Model Research Publication | Dataset | Dataset URL | Input Dims(HWC) | FP32 Floating-Point Accuracy | Quantized Accuracy | Ops (G) | Percentage Pruned | MLPerf? | E2E throughput (fps) Single Thread |
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E2E latency (ms) Thread num =20 |
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E2E latency (ms) Thread num =20 |
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Semantic Segmentation | Medical Imaging | Medical Segmentation | PyTorch | pt_3D-UNET_kits19_128_128_128_1065.44G | 3D-UNET | https://arxiv.org/abs/1606.06650 | KiTS19 | https://kits19.grand-challenge.org/data/ | 128*128*128 | 0.8824 | 0.8774 | 1065.44 | 0.04 | 0.06 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
NLP | Question and Answering | PyTorch | pt_bert-base_SQuAD_384_70.66G | BERT | https://arxiv.org/abs/1810.04805 | SQuAD | https://rajpurkar.github.io/SQuAD-explorer/ | 384 | 0.8848 | 0.837 | 70.66 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
NLP | Question and Answering | PyTorch | pt_bert-large_SQuAD_384_246.42G | BERT | https://arxiv.org/abs/1810.04805 | SQuAD | https://rajpurkar.github.io/SQuAD-explorer/ | 384 | 0.9059 | 0.866 | 246.42 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
NLP | Question and Answering | PyTorch | pt_bert-tiny_SQuAD_384_453M | BERT | https://arxiv.org/abs/1810.04805 | SQuAD | https://rajpurkar.github.io/SQuAD-explorer/ | 384 | 0.5231 | 0.5125 | 0.45 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Semantic Segmentation | Medical Imaging | Medical Segmentation | PyTorch | pt_CFLOW_LIDC_128_128_10.42G | cFlow | https://arxiv.org/abs/2006.02683 | LIDC | https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=1966254 | 128*128*3 | 0.7285 | 0.7277 | 10.42 | 90.79 | 97.06 | 62.45 | 187.89 | 66.24 | 144.71 | 66.95 | 73.86 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | Automotive | ADAS Image-lidar fusion based 3D Detection | PyTorch | pt_CLOCs_kitti | CLOCs | https://arxiv.org/abs/2009.00784 | KITTI | http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d | 2d detection (YOLOX): 384*1248*3 3d detection (PointPillars): 12000*100*4 fusionnet: 800*1000*4 |
2d detection: Mod Car bbox AP@0.70: 89.40 3d detection: Mod Car bev@0.7 :85.50 Mod Car 3d@0.7 :70.01 Mod Car bev@0.5 :89.69 Mod Car 3d@0.5 :89.48 fusionnet: Mod Car bev@0.7 :87.58 Mod Car 3d@0.7 :73.04 Mod Car bev@0.5 :93.98 Mod Car 3d@0.5 :93.56 |
2d detection: Mod Car bbox AP@0.70: 89.50 3d detection: Mod Car bev@0.7 :85.50 Mod Car 3d@0.7 :70.01 Mod Car bev@0.5 :89.69 Mod Car 3d@0.5 :89.48 fusionnet: Mod Car bev@0.7 :87.58 Mod Car 3d@0.7 :73.04 Mod Car bev@0.5 :93.98 Mod Car 3d@0.5 :93.56 |
41 | 8.11 | 14.56 | 2.85 | 10.47 | 2.89 | 10.32 | 3.08 | 9.24 | 11.5 | --- | 16.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Super Resolution | Medical Imaging | Super Resolution | PyTorch | pt_DRUNet_Kvasir_528_608_0.4G | DRUNET | https://arxiv.org/abs/1803.00232 | Kvasir | https://datasets.simula.no/kvasir/ | 528*608*3 | PSNR = 34.57 | PSNR = 34.06(QAT) | 0.4 | 258.28 | 461.86 | 60.71 | 190.51 | 63.63 | 151.94 | 64.97 | 77.69 | 89.0 | 150.2 | 133.1 | 200.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | Automotive | ADAS 2D Segmentation | PyTorch | pt_ENet_cityscapes_512_1024_11.3G | Enet | https://arxiv.org/abs/1606.02147 | Cityscapes | https://www.cityscapes-dataset.com/ | 512*1024 | 0.6442 | 0.6315 | 8.6 | 25.76 | 54.1 | 10.08 | 37.70 | 10.42 | 40.20 | 11.15 | 29.67 | 65.1 | 155.3 | 89.7 | 175.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | Smart Cities | Face Mask Detection | PyTorch | pt_face-mask-detection_512_512_0.67G | AMD Custom | AMD Custom | AMD Custom | AMD Custom | 512*512*3 | 0.886 | 0.881 | 0.59 | 453.97 | 935.97 | 115.18 | 398.69 | 120.47 | 326.86 | 123.36 | 170.20 | 676.9 | 1410.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Depth Estimation | Industrial Vision / Robotics | Binocular depth estimation | PyTorch | pt_fadnet_sceneflow_576_960_0.65_154G | FADNet | https://arxiv.org/abs/2003.10758 | Sceneflow | https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html | 576*960*3 | EPE: 0.823 | EPE: 1.158 | 154 | 65.00% | 8.83 | 15.99 | 1.76 | 2.70 | 2.63 | 5.37 | 2.75 | 4.34 | 9.9 | 13.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Depth Estimation | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_fadnet_sceneflow_576_960_441G | FADNet | https://arxiv.org/abs/2003.10758 | Sceneflow | https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html | 576*960*3 | EPE: 0.926 | EPE: 1.169 | 441 | 7.67 | 13.48 | 1.20 | 1.65 | 1.66 | 3.48 | 1.71 | 2.27 | 9.5 | 12.4 | 11.0 | 13.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Depth Estimation | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_fadnetv2_sceneflow_576_960_0.51_201G | FADNet | https://arxiv.org/abs/2003.10758 | Sceneflow | https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html | 576*960*3 | EPE: 0.878 | EPE: 1.185 | 201 | 51.00% | 9.62 | 18.73 | 2.39 | 4.57 | 2.72 | 7.89 | 2.85 | 4.51 | 12.3 | 16.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Depth Estimation | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_fadnetv2_sceneflow_576_960_412G | FADNet | https://arxiv.org/abs/2003.10758 | Sceneflow | https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html | 576*960*3 | EPE: 0.877 | EPE: 1.183 | 412 | 9.07 | 16.96 | 1.58 | 2.44 | 1.85 | 4.84 | 1.89 | 2.59 | 11.2 | 14.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | Automotive | ADAS 2D Segmentation | PyTorch | pt_HRNet_cityscapes_1024_2048_378G | HRNet | https://arxiv.org/abs/1908.07919 | Cityscapes | https://www.cityscapes-dataset.com/ | 1024*2048*3 | 0.8104 | 0.8061 | 378 | 5.24 | 5.77 | 0.60 | 0.60 | --- | --- | --- | --- | 5.6 | 7.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | PyTorch | pt_inceptionv3_imagenet_299_299_0.3_8G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.775/0.936 | 0.772/0.935 | 8 | 30.00% | 646.06 | 1027.29 | 75.98 | 180.84 | 80.57 | 158.60 | 81.38 | 87.08 | 564.0 | 1064.0 | 813.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_inceptionv3_imagenet_299_299_0.4_6.8G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.768/0.931 | 0.764/0.929 | 6.8 | 40.00% | 677.85 | 1114.32 | 85.61 | 209.04 | 90.63 | 181.72 | 91.75 | 99.12 | 605.4 | 1146.9 | 870.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_inceptionv3_imagenet_299_299_0.5_5.7G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.757/0.921 | 0.752/0.918 | 5.7 | 50.00% | 726.77 | 1251.31 | 97.45 | 241.08 | 103.03 | 209.16 | 104.42 | 114.12 | 654.7 | 1210.8 | 934.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_inceptionv3_imagenet_299_299_0.6_4.5G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.739/0.911 | 0.732/0.908 | 4.5 | 60.00% | 785.05 | 1428.95 | 114.80 | 287.61 | 121.16 | 250.05 | 122.73 | 136.16 | 731.5 | 1299.0 | 1020.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_inceptionv3_imagenet_299_299_11.4G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.775/0.936 | 0.771/0.935 | 11.4 | 597.49 | 909.77 | 60.73 | 148.29 | 64.49 | 126.35 | 64.64 | 68.30 | 490.9 | 911.8 | 711.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | General | PyTorch | pt_MaskRCNN_coco_800_800_240G | Mask R-CNN | https://arxiv.org/abs/1703.06870 | COCO | https://cocodataset.org/#download | 800*800*3 | 0.345 | 0.313 | 240 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Pose Detection | Smart Cities | Pose Estimation | PyTorch | pt_movenet_coco_192_192_0.5G | MoveNet | https://arxiv.org/abs/2105.04154 | COCO | https://cocodataset.org/#download | 192*192*3 | 0.7972 | 0.7984 | 0.5 | 244.39 | 437.2 | 93.96 | 391.31 | 94.99 | 372.12 | 102.03 | 352.26 | 2831.5 | 3234.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | PyTorch | pt_OFA-depthwise-res50_imagenet_176_176_2.49G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 176*176*3 | 0.7633/0.9292 | 0.7629/0.9306 | 2.49 | 312.19 | 452.42 | 106.19 | 378.58 | 108.42 | 346.07 | 113.75 | 269.17 | 3273.4 | 3722.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Super Resolution | Medical Imaging | Super Resolution | PyTorch | pt_OFA-rcan_DIV2K_360_640_45.7G | OFA-RCAN | DIV2K | https://data.vision.ee.ethz.ch/cvl/DIV2K/ | 360*640*3 | (Set5) PSNR/SSIM= 37.654/0.959 (Set14) PSNR/SSIM= 33.169/ 0.914 (B100) PSNR/SSIM= 31.891/ 0.897 (Urban100) PSNR/SSIM = 30.978/0.917 |
(Set5) PSNR/SSIM= 37.384/0.956 (Set14) PSNR/SSIM= 33.012/ 0.911 (B100) PSNR/SSIM= 31.785/ 0.894 (Urban100) PSNR/SSIM = 30.839/0.913 |
45.7 | 59.3 | 81.52 | 16.95 | 28.10 | 17.88 | 28.73 | 18.02 | 19.19 | 33.1 | 49.7 | 3182.6 | 66.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Image Classification | General | PyTorch | pt_OFA-resnet50_imagenet_160_160_0.88_1.8G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 160*160*3 | 0.752/0.918 | 0.744/0.918 | 1.8 | 88.00% | 2021.49 | 3844.81 | 185.33 | 370.78 | 197.28 | 354.37 | 198.42 | 214.23 | 2154.1 | 4046.2 | 3182.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_imagenet_192_192_0.74_3.6G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 192*192*3 | 0.777/0.937 | 0.770/0.933 | 3.6 | 74.00% | 1492.4 | 2680.56 | 130.10 | 274.11 | 138.56 | 253.23 | 139.90 | 148.62 | 1412.3 | 2820.1 | 2083.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_imagenet_224_224_0.45_8.2G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.795/0.945 | 0.784/0.941 | 8.2 | 45.00% | 1026.47 | 1664.48 | 73.67 | 158.80 | 78.42 | 143.52 | 78.93 | 82.26 | 868.1 | 1779.5 | 1282.5 | 2295.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_imagenet_224_224_0.60_6.0G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.791/0.943 | 0.780/0.939 | 6 | 60.00% | 1106.85 | 1872.56 | 90.77 | 179.15 | 96.99 | 170.59 | 97.45 | 102.59 | 914.7 | 1730.2 | 1284.9 | 2229.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_imagenet_224_224_15.0G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.799/0.948 | 0.789/0.944 | 15 | 819.15 | 1174.91 | 47.99 | 99.23 | 51.38 | 91.96 | 51.41 | 52.84 | 673.8 | 1201.8 | 971.7 | 1507.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | General | PyTorch | pt_OFA-yolo_coco_640_640_0.3_34.72G | OFA-YOLO | COCO | https://cocodataset.org/#download | 640*640*3 | 0.42 | 0.401 | 34.72 | 30.00% | 145.44 | 253.67 | 21.53 | 55.96 | 22.75 | 48.96 | 22.89 | 26.37 | 272.6 | 380.1 | 362.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | PyTorch | pt_OFA-yolo_coco_640_640_0.5_24.62G | OFA-YOLO | COCO | https://cocodataset.org/#download | 640*640*3 | 0.392 | 0.378 | 24.62 | 60.00% | 166.68 | 297.72 | 27.68 | 72.57 | 29.13 | 64.46 | 29.50 | 35.42 | 316.8 | 429.0 | 404.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | PyTorch | pt_OFA-yolo_coco_640_640_48.88G | OFA-YOLO | COCO | https://cocodataset.org/#download | 640*640*3 | 0.436 | 0.421 | 48.88 | 127.8 | 221.97 | 16.87 | 43.95 | 17.91 | 37.63 | 17.85 | 20.07 | 227.0 | 256.2 | 249.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||||
Industrial Vision / Robotics | Industrial Vision / Robotics | Production Recognition | PyTorch | pt_pmg_grocerystore_224_224_2.28G | PMG | https://arxiv.org/abs/2003.03836 | Grocery Store | Website removed, perhaps email Jingtian Peng (pjt@pinlandata.com) | 224*224*3 | 0.723 | 0.706 | 2.28 | 1739.6 | 3507.28 | 155.90 | 401.18 | 165.74 | 333.04 | 166.73 | 178.26 | 1998.4 | 3528.9 | 2933.5 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | Automotive | ADAS 3D Detection | PyTorch | pt_pointpillars_kitti_12000_100_11.2G | PointPillars | https://arxiv.org/abs/1812.05784 | KITTI | http://www.cvlibs.net/datasets/kitti/ | 12000*100*4 | Car 3D AP@0.5(easy, moderate, hard) | Car 3D AP@0.5(easy, moderate, hard) | 11.2 | 24.93 | 34.34 | 19.41 | 51.66 | 19.94 | 49.52 | 22.41 | 32.22 | 3.1 | --- | 3.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Industrial Vision / Robotics | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_psmnet_sceneflow_576_960_0.68_696G | PSMNet | https://arxiv.org/abs/1803.08669 | Sceneflow | https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html | 576*960*3 | EPE: 0.961 | EPE: 1.022 | 696 | 68.00% | 0.36 | 0.71 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_resnet50_imagenet_224_224_0.3_5.8G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.760/0.929 | 0.757/0.928 | 5.8 | 30.00% | 1397.32 | 2891.67 | 92.49 | 201.54 | 98.15 | 181.92 | 99.42 | 104.44 | 1529.9 | 3496.4 | 2293.8 | 4645.5 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_resnet50_imagenet_224_224_0.4_4.9G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.755/0.926 | 0.752/0.925 | 4.9 | 40.00% | 1451.8 | 3090.3 | 98.23 | 208.74 | 104.70 | 192.07 | 105.48 | 111.57 | 1581.4 | 3716.5 | 2371.4 | 4938.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_resnet50_imagenet_224_224_0.5_4.1G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.748/0.921 | 0.745/0.920 | 4.1 | 50.00% | 1518.3 | 3407.21 | 107.45 | 225.57 | 114.20 | 208.34 | 115.46 | 122.66 | 1639.4 | 3974.6 | 2458.4 | 5295.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_resnet50_imagenet_224_224_0.6_3.3G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.742/0.917 | 0.738/0.915 | 3.3 | 60.00% | 1589.65 | 3769.9 | 119.80 | 242.85 | 127.69 | 230.02 | 129.03 | 137.74 | 1730.5 | 4383.3 | 2596.6 | 5821.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_resnet50_imagenet_224_224_0.7_2.5G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.726/0.908 | 0.720/0.906 | 2.5 | 70.00% | 1675.54 | 4455.75 | 130.54 | 273.82 | 139.04 | 254.32 | 140.61 | 151.27 | 1820.0 | 4819.4 | 2728.4 | 6335.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | PyTorch | pt_resnet50_imagenet_224_224_8.2G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.761/0.929 | 0.760/0.928 | 8.2 | 1345.7 | 2688.35 | 79.92 | 189.67 | 84.74 | 161.92 | 85.51 | 89.34 | 1510.5 | 3417.8 | 2266.2 | 4548.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | Automotive | ADAS 3D Point Cloud Segmentation | PyTorch | pt_salsanext_semantic-kitti_64_2048_0.6_20.4G | SalsaNext | https://arxiv.org/abs/2003.03653 | KITTI | http://www.cvlibs.net/datasets/kitti/ | 64*2048*3 | Acc avg 0.8860, IoU avg 0.5100 | Acc avg 0.8350, IoU avg 0.4540 | 20.4 | 60.00% | 30.07 | 63.74 | 9.52 | 42.39 | 9.73 | 40.56 | 10.42 | 36.87 | 103.0 | 202.8 | 149.5 | 256.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Semantic Segmentation | Automotive | ADAS 3D Point Cloud Segmentation | PyTorch | pt_salsanextv2_semantic-kitti_64_2048_0.75_33.27G | SalsaNextv2 | https://arxiv.org/abs/2003.03653 | KITTI | http://www.cvlibs.net/datasets/kitti/ | 64*2048*5 | mIou: 54.2% | mIou: 54.2% | 32 | 75.00% | 21.82 | 56.53 | 5.95 | 14.36 | 6.27 | 15.71 | 6.39 | 12.07 | 58.7 | 87.0 | 77.1 | 97.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Semantic Segmentation | Automotive | ADAS 2D Segmentation | PyTorch | pt_SemanticFPN-mobilenetv2_cityscapes_512_1024_10G | Semantic FPN | https://arxiv.org/abs/1901.02446 | Cityscapes | https://www.cityscapes-dataset.com/ | 512*1024*3 | 0.687 | 0.682 | 5.4 | 27.19 | 55.73 | 10.52 | 54.75 | 10.72 | 53.02 | 11.23 | 32.90 | 690.2 | 1040.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | Automotive | ADAS 2D Segmentation | PyTorch | pt_SemanticFPN-resnet18_cityscapes_256_512_10.56G | Semantic FPN | https://arxiv.org/abs/1901.02446 | Cityscapes | https://www.cityscapes-dataset.com/ | 256*512*3 | 0.629 | 0.623 | 10 | 110.34 | 223.63 | 35.51 | 172.84 | 36.36 | 151.41 | 37.94 | 86.80 | 198.6 | 206.8 | 926.6 | 1072.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Super Resolution | Medical Imaging | Super Resolution | PyTorch | pt_SESR-S_DIV2K_360_640_10.2G | SESR-S | https://arxiv.org/abs/1801.10319 | DIV2K | https://data.vision.ee.ethz.ch/cvl/DIV2K/ | 360*640*3 | (Set5) PSNR/SSIM= 37.309/0.958 (Set14) PSNR/SSIM= 32.894/ 0.911 (B100) PSNR/SSIM= 31.663/ 0.893 (Urban100) PSNR/SSIM = 30.276/0.908 |
(Set5) PSNR/SSIM= 36.813/0.954 (Set14) PSNR/SSIM= 32.607/ 0.906 (B100) PSNR/SSIM= 31.443/ 0.889 (Urban100) PSNR/SSIM = 29.901/0.899 |
10.2 | 355.37 | 686.99 | 87.98 | 142.63 | 94.44 | 145.93 | 95.22 | 105.35 | 107.6 | 183.9 | 160.7 | 234.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | Automotive | ADAS Instance Segmentation | PyTorch | pt_SOLO_coco_640_640_107G | SOLO | https://arxiv.org/abs/1912.04488 | COCO | https://cocodataset.org/#download | 640*640*3 | 0.242 | 0.212 | 107 | 4.38 | 7.44 | 1.45 | 4.97 | 1.46 | 4.86 | 1.45 | 4.42 | 55.1 | --- | 59.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | PyTorch | pt_squeezenet_imagenet_224_224_703.5M | Non-Commercial Use Only | SqueezeNet | https://arxiv.org/abs/1602.07360 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.582/0.806 | 0.582/0.806 | 0.7035 | 3123.72 | 5827.04 | 572.15 | 1550.79 | 593.28 | 1326.09 | 610.11 | 760.15 | 3197.4 | 6658.9 | 4323.2 | 7664.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | Automotive | Vehicle Classification | PyTorch | pt_vehicle-color-classification_VCoR_224_224_3.64G | AMD Custom | AMD Custom | VCoR | https://www.kaggle.com/datasets/landrykezebou/vcor-vehicle-color-recognition-dataset | 224*224*3 | 0.8885 | 0.882 | 3.64 | 209.07 | 561.84 | 224.76 | 463.20 | 227.34 | 249.04 | 3474.4 | 6617.8 | 5209.3 | 7772.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||||
Image Classification | Automotive | Vehicle Classification | PyTorch | pt_vehicle-make-classification_VMMR_224_224_3.64G | AMD Custom | AMD Custom | VMMR | https://github.com/faezetta/VMMRdb | 224*224*3 | 0.9536 | 0.9522 | 3.64 | 2172.56 | 5564.65 | 211.96 | 562.15 | 224.74 | 463.56 | 227.20 | 248.93 | 3474.4 | 6615.8 | 5207.9 | 7767.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | Automotive | Vehicle Classification | PyTorch | pt_vehicle-type-classification_CarBodyStyle_224_224_3.64G | AMD Custom | AMD Custom | CarBodyStyle | https://www.kaggle.com/datasets/darshan1504/car-body-style-dataset | 224*224*3 | 0.8482 | 0.8467 | 3.64 | 2178.87 | 5554.38 | 212.16 | 561.66 | 225.04 | 464.09 | 227.43 | 249.11 | 3471.5 | 6611.1 | 5208.7 | 7770.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Super Resolution | Medical Imaging | Super Resolution | PyTorch | pt_xilinxSR_360_640_DIV2K_364.88G | AMD Custom | AMD Custom | DIV2K | https://data.vision.ee.ethz.ch/cvl/DIV2K/ | 360*640*3 | 29.04dB | 28.66dB | 364.88 | 2171.43 | 5443.07 | 2.31 | 2.31 | 2.47 | 4.35 | 2.47 | 2.57 | 8.4 | 11.4 | 11.3 | 14.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | General | PyTorch | pt_yolov4csp_coco_640_640_121G | YOLOv4-CSP | https://arxiv.org/abs/2004.10934 | COCO | https://cocodataset.org/#download | 640*640*3 | 0.47 | 0.463 | 121 | 79.43 | 118.99 | 7.39 | 20.21 | 7.85 | 16.13 | 7.89 | 8.37 | 127.9 | 173.6 | 167.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | PyTorch | pt_yolov5-large_coco_640_640_109.6G | YOLOv5l | COCO | https://cocodataset.org/#download | 640*640*3 | 0.472 | 0.455 | 109.6 | 87.65 | 149.56 | 8.71 | 24.03 | 9.26 | 19.04 | 9.31 | 9.89 | 152.7 | 195.5 | 191.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||||
Object Detection | General | PyTorch | pt_yolov5-nano_coco_640_640_4.6G | YOLOv5n | COCO | https://cocodataset.org/#download | 640*640*3 | 0.27 | 0.262 | 4.6 | 224.53 | 397.55 | 73.14 | 201.43 | 75.06 | 196.93 | 78.12 | 137.51 | 656.7 | 868.3 | 834.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||||
Object Detection | General | PyTorch | pt_yolov5s6_coco_1280_1280_17G | YOLOv5s6 | COCO | https://cocodataset.org/#download | 1280*1280 | 0.436 | 0.42 | 17 | 44.51 | 70.67 | 10.68 | 26.09 | 11.20 | 25.43 | 11.42 | 14.79 | 126.4 | 158.3 | 152.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||||
Object Detection | General | PyTorch | pt_yolov6m_coco_640_640_82.4G | YOLOv6m | https://arxiv.org/abs/2209.02976 | COCO | https://cocodataset.org/#download | 640*640*3 | 0.483 | 0.475 | 82.4 | 126.8 | 218.45 | 6.24 | 27.31 | 6.46 | 24.53 | 18.07 | 20.12 | 205.6 | 273.7 | 275.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | PyTorch | pt_yolox-nano_coco_416_416_1G | YOLOX-Nano | https://arxiv.org/abs/2107.08430 | COCO | https://cocodataset.org/#download | 416*416*3 | 0.22 | 0.21 | 1 | 621.49 | 1268.68 | 186.09 | 539.49 | 193.20 | 463.41 | 198.11 | 275.72 | 1372.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
NLP | Question and Answering | TensorFlow | tf_bert-base_SQuAD_128_22.34G | BERT | https://arxiv.org/abs/1810.04805 | SQuAD | https://rajpurkar.github.io/SQuAD-explorer/ | 128 | 0.8694 | 0.8656 | 22.34 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | TensorFlow | tf_efficientdet-d2_coco_768_768_11.06G | EfficientDet-d2 | https://arxiv.org/abs/1911.09070 | COCO | https://cocodataset.org/#download | 768*768*3 | 0.413 | 0.327 | 11.06 | 18.05 | 39.9 | 3.51 | 6.84 | 3.71 | 7.24 | 3.81 | 4.97 | 17.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Image Classification | General | TensorFlow | tf_efficientnet-edgetpu-L_imagenet_300_300_19.36G | Non-Commercial Use Only | EfficientNet-EdgeTPU Large | https://arxiv.org/abs/2003.02838 | ILSVRC2012 | https://www.image-net.org/download.php | 300*300*3 | 0.8026/0.9514 | 0.7996/0.9491 | 19.36 | 388.38 | 498.97 | 34.99 | 91.52 | 37.31 | 73.46 | 37.48 | 38.62 | 391.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_efficientnet-edgetpu-M_imagenet_240_240_7.34G | Non-Commercial Use Only | EfficientNet-EdgeTPU Medium | https://arxiv.org/abs/2003.02838 | ILSVRC2012 | https://www.image-net.org/download.php | 240*240*3 | 0.7862/0.9440 | 0.7798/0.9406 | 7.34 | 880.5 | 1379.54 | 80.03 | 210.70 | 85.28 | 169.51 | 85.58 | 89.99 | 957.1 | 1334.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_efficientnet-edgetpu-S_imagenet_224_224_4.72G | Non-Commercial Use Only | EfficientNet-EdgeTPU Small | https://arxiv.org/abs/2003.02838 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7702/0.9377 | 0.7660/0.9337 | 4.72 | 1193.07 | 2167.06 | 115.12 | 318.97 | 122.57 | 250.53 | 123.37 | 131.58 | 1629.8 | 2476.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Industrial Vision / Robotics | Industrial Vision / Robotics | Hierarchical Localization | TensorFlow | tf_HFNet_mixed_960_960_20.09G | HFNet | https://arxiv.org/abs/1812.03506 | Aachen - RobotCar Seasons - CMU Seasons - HPatches - SfM - Google Landmarks - Berkeley Deep Drive | https://github.com/ethz-asl/hfnet/blob/master/doc/datasets.md | 960*960*3 | Day: 76.2/83.6/90.0, Night: 58.2/68.4/80.6 | Day: 74.2/82.4/89.2, Night: 54.1/66.3/73.5 | 20.09 | 9.36 | 22.31 | 3.54 | 16.34 | 3.21 | 14.66 | 3.39 | 11.31 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_inceptionresnetv2_imagenet_299_299_26.35G | Non-Commercial Use Only | Inception-ResNet-v2 | https://arxiv.org/abs/1602.07261 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.8037 | 0.7946 | 26.35 | 382.69 | 491.89 | 23.98 | 54.46 | 25.57 | 47.41 | 25.64 | 26.18 | 292.2 | 484.4 | 424.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_inceptionv1_imagenet_224_224_0.09_2.73G | Non-Commercial Use Only | Inception-v1 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.691 | 0.6818 | 2.73 | 9.00% | 1325.4 | 2572.2 | 199.62 | 514.88 | 212.59 | 440.75 | 213.46 | 240.82 | 1605.6 | 3770.7 | 2278.0 | 4549.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_inceptionv1_imagenet_224_224_0.16_2.52G | Non-Commercial Use Only | Inception-v1 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6826 | 0.6746 | 2.52 | 16.00% | 1348.5 | 2653.8 | 207.68 | 546.57 | 221.04 | 463.18 | 224.21 | 251.95 | 1627.5 | 3818.4 | 2313.2 | 4670.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_inceptionv1_imagenet_224_224_3G | Non-Commercial Use Only | Inception-v1 | https://arxiv.org/abs/1801.04381 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6976 | 0.6794 | 3 | 1290.72 | 2436.64 | 192.60 | 503.71 | 203.77 | 422.16 | 206.40 | 229.56 | 1524.4 | 3457.4 | 2151.0 | 4177.5 | 2.2 | 1834 | --- | --- | --- | 1.1 | 3631.7 | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_inceptionv2_imagenet_224_224_3.88G | Non-Commercial Use Only | Inception-v2 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7399 | 7331 | 3.88 | 821.15 | 1184.49 | 93.55 | 247.54 | 99.43 | 198.89 | 99.94 | 105.08 | 206.8 | 326.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_inceptionv3_imagenet_299_299_0.2_9.1G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7786 | 0.7668 | 9.1 | 20.00% | 622.61 | 969.58 | 68.46 | 162.85 | 72.79 | 142.37 | 72.94 | 78.05 | 536.5 | 992.1 | 768.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_inceptionv3_imagenet_299_299_0.4_6.9G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7669 | 0.7561 | 6.9 | 40.00% | 676.71 | 1109.65 | 85.11 | 208.04 | 90.25 | 180.73 | 90.99 | 98.52 | 566.4 | 931.3 | 746.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_inceptionv3_imagenet_299_299_11.45G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7798 | 0.7735 | 11.45 | 585.44 | 884.52 | 60.59 | 147.04 | 64.33 | 125.69 | 64.68 | 68.37 | 494.6 | 921.9 | 712.6 | --- | 18.4 | 218 | --- | --- | --- | 9.2 | 434.9 | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_inceptionv4_imagenet_299_299_0.2_19.56G | Non-Commercial Use Only | Inception-v4 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7974 | 0.7882 | 19.56 | 20.00% | 351.25 | 438.34 | 33.09 | 77.43 | 35.29 | 67.23 | 35.36 | 36.48 | 280.9 | 446.6 | 369.4 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_inceptionv4_imagenet_299_299_0.4_14.79G | Non-Commercial Use Only | Inception-v4 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.792 | 0.782 | 14.79 | 40.00% | 383.1 | 490.94 | 40.41 | 93.96 | 43.05 | 82.23 | 43.20 | 44.82 | 325.6 | 582.3 | 453.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_inceptionv4_imagenet_299_299_24.55G | Non-Commercial Use Only | Inception-v4 | https://arxiv.org/abs/1602.07261 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.8018 | 0.7928 | 24.55 | 335.35 | 414.49 | 28.97 | 71.12 | 30.87 | 59.68 | 30.92 | 31.76 | 277.8 | 506.1 | 396.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | General | TensorFlow | tf_mlperf_resnet34_coco_1200_1200_433G | ResNet34 | https://arxiv.org/abs/1512.03385 | COCO | https://cocodataset.org/#download | 1200*1200*3 | 0.225 | 0.215 | 433 | Y | 11.88 | 20.95 | 1.85 | 7.15 | 1.89 | 5.23 | 1.87 | 2.64 | 50.3 | 77.1 | 70.2 | 91.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_mlperf_resnet50_imagenet_224_224_8.19G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7652 | 0.7606 | 8.19 | Y | 1342.12 | 2676.67 | 80.54 | 190.30 | 85.54 | 163.16 | 86.11 | 90.08 | 1476.8 | 3400.5 | 2213.0 | 4521.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_mobilenetEdge0.75_imagenet_224_224_624M | Non-Commercial Use Only | mobilenetEdge0.75 | No paper, based on TensorFlow model | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7201 | 0.6489 | 0.624 | 1885.25 | 4887.82 | 262.66 | 735.87 | 277.72 | 608.29 | 281.97 | 327.53 | 3151.4 | 5646.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_mobilenetEdge1.0_imagenet_224_224_990M | Non-Commercial Use Only | mobilenetEdge1.0 | No paper, based on TensorFlow model | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7227 | 0.6775 | 0.99 | 1808.15 | 4790.46 | 216.97 | 597.48 | 229.50 | 487.18 | 233.63 | 263.33 | 2943.6 | 5141.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_mobilenetv1_0.25_imagenet_128_128_27M | Non-Commercial Use Only | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 128*128*3 | 0.4144 | 0.3464 | 0.027 | 5013.8 | 10225.2 | 1316.79 | 4831.96 | 1341.92 | 4324.84 | 1445.63 | 2431.52 | 20846.7 | 22835.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_mobilenetv1_0.5_imagenet_160_160_150M | Non-Commercial Use Only | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 160*160*3 | 0.5903 | 0.5195 | 0.15 | 3607.19 | 7907.54 | 903.32 | 3467.80 | 935.17 | 2667.59 | 982.56 | 1473.14 | 9609.9 | 15667.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_mobilenetv1_1.0_imagenet_224_224_0.11_1.02G | Non-Commercial Use Only | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7056 | 0.6822 | 0.11 | 11.00% | 2010.66 | 4913.26 | 335.62 | 993.52 | 352.50 | 812.46 | 359.98 | 440.03 | 7205.6 | 8787.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_mobilenetv1_1.0_imagenet_224_224_0.12_1G | Non-Commercial Use Only | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.706 | 0.685 | 0.12 | 12.00% | 1996.61 | 4906.86 | 337.80 | 990.11 | 355.01 | 816.00 | 364.36 | 444.15 | 7208.1 | 8810.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_mobilenetv1_1.0_imagenet_224_224_1.14G | Non-Commercial Use Only | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7102 | 0.678 | 1.14 | 1998.32 | 4935.86 | 330.82 | 1038.05 | 346.87 | 806.06 | 356.72 | 431.28 | 6721.4 | 8607.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_mobilenetv2_1.0_imagenet_224_224_602M | Non-Commercial Use Only | MobileNetV2 | https://arxiv.org/abs/1801.04381 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7013 | 0.6767 | 62 | 1887.59 | 4930.31 | 270.02 | 764.41 | 284.71 | 621.04 | 290.01 | 338.08 | 3931.5 | 6565.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_mobilenetv2_1.4_imagenet_224_224_1.16G | Non-Commercial Use Only | MobileNetV2 | https://arxiv.org/abs/1801.04381 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7411 | 0.7194 | 1.16 | 1579.1 | 3680.09 | 191.45 | 504.48 | 203.00 | 419.48 | 205.72 | 228.14 | 3053.3 | 4966.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Semantic Segmentation | Automotive | ADAS 2D Segmentation | TensorFlow | tf_mobilenetv2_cityscapes_1024_2048_132.74G | MobileNetv2 | https://arxiv.org/abs/1801.04381 | Cityscapes | https://www.cityscapes-dataset.com/ | 1024*2048*3 | 0.6263 | 0.4578 | 132.74 | 5.34 | 11.82 | 1.75 | 5.34 | 1.81 | 5.51 | 1.90 | 3.31 | 16.6 | 16.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Super Resolution | Medical Imaging | Super Resolution | TensorFlow | tf_rcan_DIV2K_360_640_0.98_86.95G | RCAN | https://arxiv.org/abs/1807.02758 | DIV2K | https://data.vision.ee.ethz.ch/cvl/DIV2K/ | 360*640*3 | Set5 Y_PSNR : 37.640 SSIM : 0.959 |
Set5 Y_PSNR : 37.2495 SSIM : 0.9556 |
86.95 | 98.00% | 45.52 | 57.54 | 9.32 | 19.25 | 9.92 | 18.96 | 9.96 | 10.31 | 35.6 | 53.4 | 52.8 | 70.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Object Detection | General | TensorFlow | tf_refinedet_VOC_320_320_81.9G | RefineDet | https://arxiv.org/abs/1711.06897 | VOC2012 | http://host.robots.ox.ac.uk/pascal/VOC/voc2012/ | 320*320*3 | 0.8015 | 0.7999 | 81.9 | 92.92 | 224.04 | 11.34 | 35.42 | 10.88 | 25.97 | 11.06 | 13.14 | 196.3 | 304.0 | 286.8 | 397.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_EDD_320_320_0.5_41.42G | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd20 | 320*320*3 | 0.7798 | 0.7772 | 41.42 | 50.00% | 340.23 | 558.82 | 22.46 | 68.56 | 23.89 | 50.26 | 24.02 | 25.30 | 323.8 | 522.3 | 473.4 | 687.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_EDD_320_320_0.75_20.54G | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd20 | 320*320*3 | 0.7885 | 0.7826 | 20.54 | 75.00% | 444.46 | 900.11 | 39.51 | 125.98 | 41.86 | 92.29 | 42.30 | 46.48 | 410.0 | 632.6 | 562.1 | 794.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_EDD_320_320_0.85_12.32G | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd20 | 320*320*3 | 0.7898 | 0.7877 | 12.32 | 85.00% | 531.89 | 1280.02 | 59.46 | 198.22 | 62.73 | 146.08 | 63.71 | 73.53 | 518.8 | 941.0 | 769.9 | 1199.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_EDD_320_320_0.88_9.83G | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd20 | 320*320*3 | 0.7839 | 0.8002 | 9.83 | 88.00% | 548.39 | 1299.75 | 67.46 | 229.81 | 71.17 | 169.65 | 71.91 | 85.54 | 536.2 | 974.9 | 791.8 | 1247.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_EDD_320_320_81.28G | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd20 | 320*320*3 | 0.7866 | 0.7857 | 81.28 | 229.75 | 311.21 | 12.07 | 35.85 | 12.85 | 26.24 | 12.89 | 13.25 | 196.6 | 304.6 | 287.3 | 398.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_resnetv1_101_imagenet_224_224_0.35_9.4G | Non-Commercial Use Only | ResNet101 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7566 | 0.7488 | 9.4 | 35.00% | 1148.63 | 1985.98 | 58.62 | 141.33 | 62.62 | 119.40 | 62.99 | 65.06 | 1101.7 | 2509.2 | 1650.4 | 3340.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_resnetv1_101_imagenet_224_224_0.57_6.21G | Non-Commercial Use Only | ResNet101 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7502 | 0.7463 | 6.21 | 57.00% | 1322.39 | 2561.92 | 72.21 | 171.37 | 77.27 | 146.23 | 77.89 | 81.01 | 1222.9 | 3115.9 | 1834.2 | 4145.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_resnetv1_101_imagenet_224_224_14.4G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.764 | 0.756 | 14.4 | 1055.21 | 1727.18 | 47.09 | 122.51 | 50.11 | 97.64 | 50.33 | 51.67 | 1026.8 | 2230.2 | 1539.4 | 2965.2 | 8.5 | 472 | --- | --- | --- | 4.24 | 941.9 | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_resnetv1_152_imagenet_224_224_0.51_10.68G | Non-Commercial Use Only | ResNet152 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7591 | 0.7558 | 10.68 | 51.00% | 1024.26 | 1635.92 | 47.12 | 113.09 | 50.33 | 94.98 | 50.66 | 51.98 | 845.9 | 2078.8 | 1268.6 | 2765.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_resnetv1_152_imagenet_224_224_0.6_8.82G | Non-Commercial Use Only | ResNet152 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7568 | 0.7545 | 8.82 | 60.00% | 1092.61 | 1851 | 51.51 | 122.09 | 55.07 | 103.36 | 55.46 | 57.05 | 882.7 | 2300.3 | 1323.7 | 3064.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_resnetv1_152_imagenet_224_224_21.83G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7681 | 0.7463 | 21.83 | 827.25 | 1200.09 | 32.01 | 84.68 | 34.14 | 66.64 | 34.20 | 34.81 | 734.0 | 1586.1 | 1100.5 | 2108.8 | 12.7 | 316 | --- | --- | --- | 6.35 | 630.3 | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_resnetv1_50_imagenet_224_224_0.38_4.3G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7442 | 0.7375 | 0.38 | 38.00% | 1514.42 | 3375.93 | 111.69 | 239.19 | 118.83 | 219.14 | 120.41 | 128.00 | 1714.8 | 4007.8 | 2573.7 | 5350.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_resnetv1_50_imagenet_224_224_0.65_2.45G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7279 | 0.7167 | 2.45 | 65.00% | 1738.45 | 4565.52 | 141.57 | 310.89 | 151.29 | 282.07 | 153.44 | 165.89 | 2034.9 | 5472.2 | 3053.1 | 6914.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_resnetv1_50_imagenet_224_224_6.97G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.752 | 0.7436 | 6.97 | 1427.03 | 3022.13 | 90.27 | 214.04 | 95.98 | 183.32 | 96.63 | 101.53 | 1644.7 | 3732.8 | 2466.9 | 4972.4 | 4.2 | 947 | --- | --- | --- | 2.13 | 1881.6 | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_resnetv2_101_imagenet_299_299_26.78G | Non-Commercial Use Only | ResNet50V2 | https://arxiv.org/abs/1603.05027 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7695 | 0.7506 | 26.78 | 409.14 | 531.02 | 23.61 | 57.07 | 25.18 | 47.89 | 25.25 | 25.83 | 236.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_resnetv2_152_imagenet_299_299_40.47G | Non-Commercial Use Only | ResNet50V2 | https://arxiv.org/abs/1603.05027 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7779 | 0.7432 | 40.47 | 327.64 | 402.36 | 16.08 | 38.96 | 17.17 | 32.62 | 17.22 | 17.45 | 165.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_resnetv2_50_imagenet_299_299_13.1G | Non-Commercial Use Only | ResNet50V2 | https://arxiv.org/abs/1603.05027 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7559 | 0.7445 | 13.1 | 542.98 | 783.3 | 45.08 | 102.48 | 48.06 | 90.77 | 48.40 | 50.37 | 415.6 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | General | TensorFlow | tf_ssdinceptionv2_coco_300_300_9.62G | SSD inception-v2 | No paper, based on TensorFlow model | COCO | https://cocodataset.org/#download | 300*300*3 | 0.239 | 0.236 | 9.62 | 271.95 | 418.21 | 39.64 | 108.56 | 42.00 | 90.00 | 42.47 | 47.57 | 104.6 | 164.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | TensorFlow | tf_ssdlite_mobilenetv2_coco_300_300_1.5G | SSDLite MobileNetV2 | No paper, based on TensorFlow model | COCO | https://cocodataset.org/#download | 300*300*3 | 0.217 | 0.209 | 1.5 | 408.32 | 519.63 | 106.47 | 327.82 | 110.74 | 285.14 | 113.46 | 160.49 | 1816.6 | 2266.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | TensorFlow | tf_ssdmobilenetv1_coco_300_300_2.47G | SSD MobileNetV1 | No paper, based on TensorFlow model | COCO | https://cocodataset.org/#download | 300*300*3 | 0.208 | 0.21 | 2.47 | 431.06 | 522.66 | 111.86 | 364.84 | 115.62 | 315.33 | 119.29 | 173.64 | 2231.6 | 2284.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | TensorFlow | tf_ssdmobilenetv2_coco_300_300_3.75G | SSD MobileNetV2 | No paper, based on TensorFlow model | COCO | https://cocodataset.org/#download | 300*300*3 | 0.215 | 0.211 | 3.75 | 395.81 | 515 | 81.93 | 223.90 | 85.41 | 200.20 | 87.45 | 112.89 | 1259.7 | 1847.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | TensorFlow | tf_ssdresnet50v1_fpn_coco_640_640_178.4G | SSD ResNet50v1 FPN | https://arxiv.org/abs/1708.02002 | COCO | https://cocodataset.org/#download | 640*640*3 | 0.301 | 0.29 | 178.4 | 11.07 | 12.21 | 2.92 | 5.27 | 2.94 | 5.26 | 2.98 | 5.41 | 83.8 | 103.1 | 99.8 | 113.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Industrial Vision / Robotics | Interest Point Detection and Description | TensorFlow | tf_superpoint_mixed_480_640_52.4G | SuperPoint | https://arxiv.org/abs/2107.03601 | COCO 2014 | https://cocodataset.org/#download | 480*640*3 | 83.4 (thr=3) | 84.3 (thr=3) | 52.4 | 49.84 | 105.91 | 12.54 | 53.25 | 10.96 | 40.27 | 11.43 | 20.54 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Image Classification | General | TensorFlow | tf_vgg16_imagenet_224_224_0.43_17.67G | Non-Commercial Use Only | VGG16 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6929 | 0.6823 | 17.67 | 43.00% | 836.16 | 1212.72 | 43.47 | 106.20 | 46.38 | 88.45 | 46.48 | 47.55 | 586.7 | 962.0 | 873.0 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_vgg16_imagenet_224_224_0.5_15.64G | Non-Commercial Use Only | VGG16 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6857 | 0.6729 | 15.64 | 50.00% | 890.42 | 1328.27 | 47.21 | 117.01 | 50.38 | 96.83 | 50.47 | 51.75 | 634.5 | 1041.8 | 939.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_vgg16_imagenet_224_224_30.96G | Non-Commercial Use Only | VGG16 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7089 | 0.7069 | 30.96 | 505.43 | 621.19 | 20.50 | 43.40 | 21.84 | 38.43 | 21.79 | 22.02 | 325.8 | 501.5 | 475.5 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_vgg19_imagenet_224_224_0.24_29.79G | Non-Commercial Use Only | VGG19 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7075 | 0.7022 | 29.79 | 24.00% | 539.513 | 674.046 | 22.36 | 50.73 | 23.85 | 43.58 | 23.85 | 24.13 | 357.8 | 558.6 | 527.5 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||
Image Classification | General | TensorFlow | tf_vgg19_imagenet_224_224_0.39_23.78G | Non-Commercial Use Only | VGG19 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6954 | 0.6903 | 23.78 | 656.458 | 865.695 | 31.36 | 75.28 | 33.49 | 63.06 | 33.50 | 34.08 | 451.2 | 719.0 | 667.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_vgg19_imagenet_224_224_39.28G | Non-Commercial Use Only | VGG19 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.71 | 0.7026 | 39.28 | 459.81 | 553.86 | 17.65 | 38.87 | 18.82 | 33.75 | 18.77 | 18.94 | 291.0 | 442.9 | 422.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow | tf_ViT_imagenet_352_352_21.3G | Non-Commercial Use Only | ViT | https://arxiv.org/abs/2010.11929 | ILSVRC2012 | https://www.image-net.org/download.php | 352*352*3 | 0.8282 | 0.8254 | 21.3 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | General | TensorFlow | tf_yolov3_voc_416_416_65.63G | YOLOv3 | https://arxiv.org/abs/1804.02767 | VOC2012 | http://host.robots.ox.ac.uk/pascal/VOC/voc2012/ | 416*416*3 | 0.7846 | 0.7744 | 65.63 | 214.94 | 287.23 | 13.58 | 37.82 | 14.47 | 29.16 | 14.50 | 14.90 | 275.4 | 388.3 | 380.5 | 478.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | TensorFlow | tf_yolov4_coco_416_416_60.3G | YOLOv4 | https://arxiv.org/abs/2004.10934 | COCO | https://cocodataset.org/#download | 416*416*3 | 0.477 | 0.393 | 60.3 | 140.1 | 211.66 | 13.51 | 35.81 | 14.35 | 29.04 | 14.36 | 15.34 | 235.2 | --- | 305.5 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Object Detection | General | TensorFlow | tf_yolov4_coco_512_512_91.2G | YOLOv4 | https://arxiv.org/abs/2004.10934 | COCO | https://cocodataset.org/#download | 512*512*3 | 0.487 | 0.412 | 91.2 | 104.15 | 152.76 | 10.24 | 26.40 | 10.92 | 21.82 | 10.90 | 11.65 | 134.5 | --- | 155.8 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Semantic Segmentation | Medical Imaging | Medical Cell Nuclear Segmentation | TensorFlow 2 | tf2_2d-unet_nuclei_128_128_5.31G | Unet | https://arxiv.org/abs/1505.04597 | Nuclei | https://www.kaggle.com/code/advaitsave/tensorflow-2-nuclei-segmentation-unet/notebook | 128*128*3 | 0.3968 | 0.3968 | 5.31 | 1499.58 | 2938.48 | 156.64 | 436.97 | 166.15 | 352.09 | 167.88 | 182.93 | 855.9 | 1209.4 | 1167.5 | 1448.5 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow 2 | tf2_efficientnet-b0_imagenet_224_224_0.36G | Non-Commercial Use Only | EfficientNet-B0 | https://arxiv.org/abs/1905.11946 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7690/0.9320 | 0.7515/0.9273 | 0.78 | 1084.77 | 1810.3 | 77.92 | 156.48 | 83.21 | 146.49 | 83.52 | 87.18 | 285.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow 2 | tf2_Efficientnet-lite_imagenet_224_224_0.77G | Non-Commercial Use Only | EfficientNet lite | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7501 | 0.7444 | 0.77 | 1667.34 | 4406.25 | 202.27 | 580.00 | 213.93 | 458.15 | 218.02 | 243.88 | 2456.8 | 3898.1 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||
Semantic Segmentation | Automotive | ADAS 2D Segmentation | TensorFlow 2 | tf2_erfnet_cityscapes_512_1024_54G | ERFNet | http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf | Cityscapes | https://www.cityscapes-dataset.com/ | 512*1024*3 | 0.5298 | 0.5167 | 54 | 20.54 | 46.26 | 7.43 | 24.47 | 7.68 | 25.56 | 8.10 | 15.36 | 59.4 | 113.3 | 76.1 | 118.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow 2 | tf2_inceptionv3_imagenet_299_299_11.5G | Non-Commercial Use Only | Inception-v3 | https://arxiv.org/abs/1512.00567 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7753 | 0.7694 | 11.5 | 644.66 | 1033.2 | 59.90 | 147.49 | 63.60 | 125.13 | 64.04 | 67.53 | 487.0 | 958.6 | 710.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow 2 | tf2_mobilenetv1_imagenet_224_224_1.15G | Non-Commercial Use Only | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7005 | 0.5603 | 1.15 | 2013.04 | 4941.36 | 325.09 | 1017.95 | 341.54 | 796.33 | 350.74 | 422.62 | 6708.6 | 8575.9 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow 2 | tf2_mobilenetv3_imagenet_224_224_132M | Non-Commercial Use Only | MobileNetV3 | https://arxiv.org/abs/1905.02244 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6756/0.8728 | 0.6536/0.8544 | 0.132 | 2019.91 | 4904.55 | 343.79 | 1073.70 | 365.02 | 847.76 | 375.17 | 458.98 | 1429.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Image Classification | General | TensorFlow 2 | tf2_resnet50_imagenet_224_224_7.76G | Non-Commercial Use Only | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7513 | 0.7423 | 7.76 | 1434.64 | 3026.6 | 89.16 | 213.59 | 95.05 | 182.46 | 95.37 | 100.06 | 1644.3 | 3732.7 | 2465.7 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |||
Object Detection | General | TensorFlow 2 | tf2_yolov3_coco_416_416_65.9G | YOLOv3 | https://arxiv.org/abs/1804.02767 | COCO | https://cocodataset.org/#download | 416*416*3 | 0.377 | 0.331 | 65.9 | 189.42 | 286.57 | 13.27 | 37.53 | 14.11 | 29.02 | 14.16 | 14.80 | 274.3 | 382.9 | 376.0 | 472.2 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ||||