![]() Vitis AI 3.5 Model Zoo |
Task | Market Specialization | Application | Framework | Vitis-AI Model Name Zoo Name | License Restriction(s) | Copyleft Model Zoo | Model Architecture | Model Research Publication | Dataset | Dataset URL | Input Dims(HWC) | FP32 Floating-Point Accuracy | Quantized Accuracy | Ops (G) | Percentage Pruned | MLPerf? | VEK280 1* C20B14CU1 @ 300MHz AIE fclk=1.18GHz E2E throughput (fps) Single Thread | VEK280 1* C20B14CU1 @ 300MHz AIE fclk=1.18GHz E2E throughput (fps) Multi Thread | V70 1* C20B14CU1 @ 300MHz AIE fclk = 1.00GHz E2E throughput (fps) Multi Thread |
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Task | Market Specialization | Application | Framework | Vitis-AI Model Name Zoo Name | License Restriction(s) | Copyleft Model Zoo | Model Architecture | Model Research Publication | Dataset | Dataset URL | Input Dims(HWC) | FP32 Floating-Point Accuracy | Quantized Accuracy | Ops (G) | Percentage Pruned | MLPerf? | VEK280
1* C20B14CU1 @ 300MHz
AIE fclk=1.18GHz
E2E throughput (fps)
Single Thread | VEK280
1* C20B14CU1 @ 300MHz
AIE fclk=1.18GHz
E2E throughput (fps)
Multi Thread | V70
1* C20B14CU1 @ 300MHz
AIE fclk = 1.00GHz
E2E throughput (fps)
Multi Thread |
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Industrial Vision / Robotics | Interest Point Detection and Description | TensorFlow | tf_superpoint_3.5 | No | 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 | 54.53 | 123.68 | / | ||||
Depth Estimation | Industrial Vision / Robotics | Binocular depth estimation | PyTorch | pt_fadnet_0.65_3.5 | No | 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% | / | / | / | ||
Depth Estimation | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_fadnet_3.5 | No | 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 | / | / | / | |||
Depth Estimation | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_fadnetv2_0.51_3.5 | No | 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% | 7.91 | 16.68 | / | ||
Depth Estimation | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_fadnetv2_3.5 | No | 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 | 8.47 | 19.15 | / | |||
Image Classification | General | PyTorch | pt_inceptionv3_0.3_3.5 | Non-Commercial Use Only | No | 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% | 835.80 | 1776.02 | 1289.55 | ||
Image Classification | General | PyTorch | pt_inceptionv3_0.4_3.5 | Non-Commercial Use Only | No | 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% | 855.50 | 1935.91 | 1366.46 | ||
Image Classification | General | PyTorch | pt_inceptionv3_0.5_3.5 | Non-Commercial Use Only | No | 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% | 909.83 | 2181.70 | 1482.21 | ||
Image Classification | General | PyTorch | pt_inceptionv3_0.6_3.5 | Non-Commercial Use Only | No | 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% | 966.01 | 2415.77 | 1684.68 | ||
Image Classification | General | PyTorch | pt_inceptionv3_3.5 | Non-Commercial Use Only | No | 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 | 794.99 | 1462.85 | 1143.74 | |||
Image Classification | General | PyTorch | pt_OFA-depthwise-res50_3.5 | Non-Commercial Use Only | No | 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 | 336.85 | 527.26 | 12925.70 | |||
Image Classification | General | PyTorch | pt_OFA-resnet50_0.88_3.5 | Non-Commercial Use Only | No | 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% | 2549.20 | 6595.03 | 7780.10 | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_0.74_3.5 | Non-Commercial Use Only | No | 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% | 2014.20 | 5274.48 | 3609.05 | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_0.45_3.5 | Non-Commercial Use Only | No | 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% | 1515.01 | 3636.90 | 5265.23 | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_0.60_3.5 | Non-Commercial Use Only | No | 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% | 1562.12 | 4064.77 | 3165.36 | ||
Image Classification | General | PyTorch | pt_OFA-resnet50_3.5 | Non-Commercial Use Only | No | 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 | / | / | / | |||
Image Classification | General | PyTorch | pt_resnet50_0.3_3.5 | Non-Commercial Use Only | No | 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% | 1628.45 | 4247.61 | 4132.65 | ||
Image Classification | General | PyTorch | pt_resnet50_0.4_3.5 | Non-Commercial Use Only | No | 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% | 1666.52 | 4268.98 | 4401.23 | ||
Image Classification | General | PyTorch | pt_resnet50_0.5_3.5 | Non-Commercial Use Only | No | 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% | 1701.21 | 4265.80 | 4671.20 | ||
Image Classification | General | PyTorch | pt_resnet50_0.6_3.5 | Non-Commercial Use Only | No | 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% | 1744.41 | 4272.31 | 5126.95 | ||
Image Classification | General | PyTorch | pt_resnet50_0.7_3.5 | Non-Commercial Use Only | No | 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% | 1807.90 | 4243.46 | 5842.50 | ||
Image Classification | General | PyTorch | pt_resnet50_3.5 | Non-Commercial Use Only | No | 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 | 1708.50 | 4432.04 | 3792.31 | |||
Image Classification | General | PyTorch | pt_squeezenet_3.5 | Non-Commercial Use Only | No | 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 | 3768.00 | 8598.33 | 4500.98 | |||
Image Classification | Automotive | Vehicle Classification | PyTorch | pt_vehicle-color-classification_3.5 | No | 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 | 2593.27 | 6145.02 | 9679.80 | |||
Image Classification | Automotive | Vehicle Classification | PyTorch | pt_vehicle-make-classification_3.5 | No | AMD Custom | AMD Custom | VMMR | https://github.com/faezetta/VMMRdb | 224*224*3 | 0.9536 | 0.9522 | 3.64 | 2589.52 | 6151.85 | 9686.00 | |||
Image Classification | Automotive | Vehicle Classification | PyTorch | pt_vehicle-type-classification_3.5 | No | AMD Custom | AMD Custom | CarBodyStyle | https://www.kaggle.com/datasets/darshan1504/car-body-style-dataset | 224*224*3 | 0.8482 | 0.8467 | 3.64 | 2603.06 | 6187.62 | 9690.45 | |||
Image Classification | General | TensorFlow | tf_efficientnet-edgetpu-L_3.5 | Non-Commercial Use Only | No | 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 | 573.78 | 847.17 | 757.68 | |||
Image Classification | General | TensorFlow | tf_efficientnet-edgetpu-M_3.5 | Non-Commercial Use Only | No | 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 | 1433.33 | 3312.43 | 2843.53 | |||
Image Classification | General | TensorFlow | tf_efficientnet-edgetpu-S_3.5 | Non-Commercial Use Only | No | 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 | 1679.22 | 4246.41 | 3654.75 | |||
Image Classification | General | TensorFlow | tf_inceptionv1_0.09_3.5 | Non-Commercial Use Only | No | 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% | 1617.07 | 4037.49 | 3258.85 | ||
Image Classification | General | TensorFlow | tf_inceptionv1_0.16_3.5 | Non-Commercial Use Only | No | 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% | 1635.10 | 4069.94 | 3008.47 | ||
Image Classification | General | TensorFlow | tf_inceptionv1_3.5 | Non-Commercial Use Only | No | Inception-v1 | https://arxiv.org/abs/1801.04381 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6976 | 0.6794 | 3 | 1712.47 | 4424.24 | 3692.12 | |||
Image Classification | General | TensorFlow | tf_inceptionv3_0.2_3.5 | Non-Commercial Use Only | No | 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% | 803.14 | 1642.87 | 1234.00 | ||
Image Classification | General | TensorFlow | tf_inceptionv3_0.4_3.5 | Non-Commercial Use Only | No | 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% | 866.69 | 1926.97 | 1355.14 | ||
Image Classification | General | TensorFlow | tf_inceptionv3_3.5 | Non-Commercial Use Only | No | 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 | 808.24 | 1504.90 | 1296.26 | |||
Image Classification | General | TensorFlow | tf_inceptionv4_0.2_3.5 | Non-Commercial Use Only | No | 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% | 470.43 | 664.16 | 410.59 | ||
Image Classification | General | TensorFlow | tf_inceptionv4_0.4_3.5 | Non-Commercial Use Only | No | 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% | 504.48 | 737.84 | 441.69 | ||
Image Classification | General | TensorFlow | tf_inceptionv4_3.5 | Non-Commercial Use Only | No | 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 | 471.90 | 646.84 | 403.08 | |||
Image Classification | General | TensorFlow | tf_mlperf_resnet50_3.5 | Non-Commercial Use Only | No | 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 | 1724.03 | 4515.75 | 3792.32 | ||
Image Classification | General | TensorFlow | tf_mobilenetv1-0.25_3.5 | Non-Commercial Use Only | No | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 128*128*3 | 0.4144 | 0.3464 | 0.027 | 4044.64 | 8381.29 | 63108.60 | |||
Image Classification | General | TensorFlow | tf_mobilenetv1-1.0_0.11_3.5 | Non-Commercial Use Only | No | 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% | 2074.67 | 4270.96 | 14905.70 | ||
Image Classification | General | TensorFlow | tf_mobilenetv1-1.0_0.12_3.5 | Non-Commercial Use Only | No | 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% | 2076.09 | 4208.04 | 14936.70 | ||
Image Classification | General | TensorFlow | tf_mobilenetv1-1.0_3.5 | Non-Commercial Use Only | No | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7102 | 0.678 | 1.14 | 2350.44 | 5167.04 | 14581.60 | |||
Image Classification | General | TensorFlow | tf_mobilenetv2-1.0_3.5 | Non-Commercial Use Only | No | MobileNetV2 | https://arxiv.org/abs/1801.04381 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7013 | 0.6767 | 62 | 2030.30 | 4236.92 | 11544.90 | |||
Image Classification | General | TensorFlow | tf_mobilenetv2-1.4_3.5 | Non-Commercial Use Only | No | MobileNetV2 | https://arxiv.org/abs/1801.04381 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7411 | 0.7194 | 1.16 | 1874.75 | 4207.31 | 8081.16 | |||
Image Classification | General | TensorFlow | tf_resnetv1-101_0.35_3.5 | Non-Commercial Use Only | No | 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% | 1493.91 | 3478.36 | 2966.77 | ||
Image Classification | General | TensorFlow | tf_resnetv1-101_0.57_3.5 | Non-Commercial Use Only | No | 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% | 1584.16 | 4139.32 | 3638.47 | ||
Image Classification | General | TensorFlow | tf_resnetv1-101_3.5 | Non-Commercial Use Only | No | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.764 | 0.756 | 14.4 | 1444.95 | 3009.90 | 2542.30 | |||
Image Classification | General | TensorFlow | tf_resnetv1-152_0.51_3.5 | Non-Commercial Use Only | No | 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% | 1350.73 | 2832.52 | 2394.19 | ||
Image Classification | General | TensorFlow | tf_resnetv1-152_0.6_3.5 | Non-Commercial Use Only | No | 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% | 1411.05 | 3077.83 | 2614.17 | ||
Image Classification | General | TensorFlow | tf_resnetv1-152_3.5 | Non-Commercial Use Only | No | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7681 | 0.7463 | 21.83 | 1209.95 | 2155.49 | 1792.50 | |||
Image Classification | General | TensorFlow | tf_resnetv1-50_0.38_3.5 | Non-Commercial Use Only | No | 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% | 1716.20 | 4259.59 | 5038.76 | ||
Image Classification | General | TensorFlow | tf_resnetv1-50_0.65_3.5 | Non-Commercial Use Only | No | 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% | 1866.27 | 4261.81 | 6836.10 | ||
Image Classification | General | TensorFlow | tf_resnetv1-50_3.5 | Non-Commercial Use Only | No | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.752 | 0.7436 | 6.97 | 1792.38 | 4793.76 | 4329.41 | |||
Image Classification | General | TensorFlow | tf_resnetv2-101_3.5 | Non-Commercial Use Only | No | ResNet50V2 | https://arxiv.org/abs/1603.05027 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7695 | 0.7506 | 26.78 | 599.63 | 916.29 | 760.86 | |||
Image Classification | General | TensorFlow | tf_resnetv2-152_3.5 | Non-Commercial Use Only | No | ResNet50V2 | https://arxiv.org/abs/1603.05027 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7779 | 0.7432 | 40.47 | 488.57 | 679.61 | 564.92 | |||
Image Classification | General | TensorFlow | tf_resnetv2-50_3.5 | Non-Commercial Use Only | No | ResNet50V2 | https://arxiv.org/abs/1603.05027 | ILSVRC2012 | https://www.image-net.org/download.php | 299*299*3 | 0.7559 | 0.7445 | 13.1 | 776.70 | 1398.72 | 1160.64 | |||
Image Classification | General | TensorFlow | tf_vgg16_0.43_3.5 | Non-Commercial Use Only | No | 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% | 1179.57 | 2132.38 | 1958.96 | ||
Image Classification | General | TensorFlow | tf_vgg16_0.5_3.5 | Non-Commercial Use Only | No | 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% | 1254.81 | 2463.84 | 2190.40 | ||
Image Classification | General | TensorFlow | tf_vgg16_3.5 | Non-Commercial Use Only | No | VGG16 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7089 | 0.7069 | 30.96 | 554.19 | 690.98 | 619.22 | |||
Image Classification | General | TensorFlow | tf_vgg19_0.24_3.5 | Non-Commercial Use Only | No | 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% | 644.37 | 849.49 | 759.97 | ||
Image Classification | General | TensorFlow | tf_vgg19_0.39_3.5 | Non-Commercial Use Only | No | VGG19 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.6954 | 0.6903 | 23.78 | 846.90 | 1243.95 | 1020.87 | |||
Image Classification | General | TensorFlow | tf_vgg19_3.5 | Non-Commercial Use Only | No | VGG19 | https://arxiv.org/abs/1409.1556 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.71 | 0.7026 | 39.28 | 571.36 | 718.20 | 691.80 | |||
Image Classification | General | TensorFlow | tf_ViT_3.5 | Non-Commercial Use Only | No | ViT | https://arxiv.org/abs/2010.11929 | ILSVRC2012 | https://www.image-net.org/download.php | 352*352*3 | 0.8282 | 0.8254 | 21.3 | / | / | / | |||
Image Classification | General | TensorFlow 2 | tf2_efficientnet-b0_3.5 | Non-Commercial Use Only | No | 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 | / | / | / | |||
Image Classification | General | TensorFlow 2 | tf2_Efficientnet-lite_3.5 | Non-Commercial Use Only | No | EfficientNet lite | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7501 | 0.7444 | 0.77 | 2207.26 | 5138.35 | 9842.18 | ||||
Image Classification | General | TensorFlow 2 | tf2_inceptionv3_3.5 | Non-Commercial Use Only | No | 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 | 874.55 | 1761.14 | 1503.39 | |||
Image Classification | General | TensorFlow 2 | tf2_mobilenetv1_3.5 | Non-Commercial Use Only | No | MobileNetV1 | https://arxiv.org/abs/1704.04861 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7005 | 0.5603 | 1.15 | 2380.94 | 5257.80 | 14585.10 | |||
Image Classification | General | TensorFlow 2 | tf2_mobilenetv3_3.5 | Non-Commercial Use Only | No | 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 | / | / | / | |||
Image Classification | General | TensorFlow 2 | tf2_resnet50_3.5 | Non-Commercial Use Only | No | ResNet50 | https://arxiv.org/abs/1512.03385 | ILSVRC2012 | https://www.image-net.org/download.php | 224*224*3 | 0.7513 | 0.7423 | 7.76 | 1747.67 | 4653.05 | 3912.34 | |||
Industrial Vision / Robotics | Industrial Vision / Robotics | Stereo Depth Estimation | PyTorch | pt_psmnet_0.68_3.5 | No | 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% | / | / | / | ||
Industrial Vision / Robotics | Industrial Vision / Robotics | Hierarchical Localization | TensorFlow | tf_HFNet_3.5 | No | 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 | 10.71 | 24.04 | / | |||
NLP | Question and Answering | PyTorch | pt_bert-base_3.5 | No | 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_3.5 | No | 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_3.5 | No | BERT | https://arxiv.org/abs/1810.04805 | SQuAD | https://rajpurkar.github.io/SQuAD-explorer/ | 384 | 0.5231 | 0.5125 | 0.45 | / | / | / | ||||
NLP | Question and Answering | TensorFlow | tf_bert-base_3.5 | No | BERT | https://arxiv.org/abs/1810.04805 | SQuAD | https://rajpurkar.github.io/SQuAD-explorer/ | 128 | 0.8694 | 0.8656 | 22.34 | / | / | / | ||||
Object Detection | Smart Cities | Face Mask Detection | PyTorch | pt_face-mask-detection_3.5 | No | Yolo-fastest | https://doi.org/10.2352/EI.2023.35.11.HPCI-229 | Face-mask-detection | https://github.com/waittim/mask-detector/tree/master/modeling/data | 512*512*3 | 0.886 | 0.881 | 0.59 | 512.65 | 1024.67 | 2173.85 | |||
Object Detection | General | PyTorch | pt_OFA-yolo_0.3_3.5 | No | OFA-YOLO | COCO | https://cocodataset.org/#download | 640*640*3 | 0.42 | 0.401 | 34.72 | 30.00% | 173.53 | 383.53 | 323.62 | ||||
Object Detection | General | PyTorch | pt_OFA-yolo_0.5_3.5 | No | OFA-YOLO | COCO | https://cocodataset.org/#download | 640*640*3 | 0.392 | 0.378 | 24.62 | 60.00% | 193.87 | 406.21 | 456.91 | ||||
Object Detection | General | PyTorch | pt_OFA-yolo_3.5 | No | OFA-YOLO | COCO | https://cocodataset.org/#download | 640*640*3 | 0.436 | 0.421 | 48.88 | 165.66 | 370.41 | 295.25 | |||||
Object Detection | General | PyTorch | pt_yolov4csp_3.5 | Yes | YOLOv4-CSP | https://arxiv.org/abs/2004.10934 | COCO | https://cocodataset.org/#download | 640*640*3 | 0.47 | 0.463 | 121 | 76.11 | 112.35 | 88.08 | ||||
Object Detection | General | PyTorch | pt_yolov6m_3.5 | Yes | YOLOv6m | https://arxiv.org/abs/2209.02976 | COCO | https://cocodataset.org/#download | 640*640*3 | 0.483 | 0.475 | 82.4 | 36.84 | 49.61 | 279.67 | ||||
Object Detection | General | PyTorch | pt_yolox-nano_3.5 | No | YOLOX-Nano | https://arxiv.org/abs/2107.08430 | COCO | https://cocodataset.org/#download | 416*416*3 | 0.22 | 0.21 | 1 | 691.64 | 1409.42 | 1253.45 | ||||
Object Detection | General | PyTorch | pt_yolov7_3.5 | Yes | YOLOv7 | COCO | https://cocodataset.org/#download | 640*640*3 | 0.512 | 0.479 | 104.8 | 79.42 | 154.10 | 78.08 | |||||
Object Detection | General | TensorFlow | tf_efficientdet-d2_3.5 | No | EfficientDet-d2 | https://arxiv.org/abs/1911.09070 | COCO | https://cocodataset.org/#download | 768*768*3 | 0.413 | 0.327 | 11.06 | / | / | / | ||||
Object Detection | General | TensorFlow | tf_mlperf_resnet34_3.5 | No | ResNet34 | https://arxiv.org/abs/1512.03385 | COCO | https://cocodataset.org/#download | 1200*1200*3 | 0.225 | 0.215 | 433 | Y | 17.05 | 40.67 | 70.16 | |||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_0.5_3.5 | No | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd2020#files | 320*320*3 | 0.7798 | 0.7772 | 41.42 | 50.00% | 393.94 | 868.81 | 683.10 | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_0.75_3.5 | No | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd2020#files | 320*320*3 | 0.7885 | 0.7826 | 20.54 | 75.00% | 475.20 | 1099.97 | 1101.20 | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_0.85_3.5 | No | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd2020#files | 320*320*3 | 0.7898 | 0.7877 | 12.32 | 85.00% | 534.28 | 1167.51 | 1561.50 | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_0.88_3.5 | No | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd2020#files | 320*320*3 | 0.7839 | 0.8002 | 9.83 | 88.00% | 624.73 | 1465.31 | 1769.71 | ||
Object Detection | Medical Imaging | Medical Detection | TensorFlow | tf_RefineDet-Medical_3.5 | No | RefineDet | https://arxiv.org/abs/1711.06897 | EDD2020 | https://ieee-dataport.org/competitions/endoscopy-disease-detection-and-segmentation-edd2020#files | 320*320*3 | 0.7866 | 0.7857 | 81.28 | 277.93 | 445.91 | 339.95 | |||
Object Detection | General | TensorFlow | tf_ssdmobilenetv1_3.5 | No | SSD MobileNetV1 | No paper, based on TensorFlow model | COCO | https://cocodataset.org/#download | 300*300*3 | 0.208 | 0.21 | 2.47 | 903.54 | 1932.60 | 6100.36 | ||||
Object Detection | General | TensorFlow | tf_ssdmobilenetv2_3.5 | No | SSD MobileNetV2 | No paper, based on TensorFlow model | COCO | https://cocodataset.org/#download | 300*300*3 | 0.215 | 0.211 | 3.75 | 818.32 | 1904.04 | 2952.77 | ||||
Object Detection | General | TensorFlow | tf_yolov3_3.5 | No | 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 | 319.75 | 528.49 | 421.89 | ||||
Object Detection | General | TensorFlow | tf_yolov4-416_3.5 | No | YOLOv4 | https://arxiv.org/abs/2004.10934 | COCO | https://cocodataset.org/#download | 416*416*3 | 0.477 | 0.393 | 60.3 | 192.54 | 350.05 | 232.00 | ||||
Object Detection | General | TensorFlow | tf_yolov4-512_3.5 | No | YOLOv4 | https://arxiv.org/abs/2004.10934 | COCO | https://cocodataset.org/#download | 512*512*3 | 0.487 | 0.412 | 91.2 | 133.80 | 225.89 | 96.77 | ||||
Object Detection | General | TensorFlow 2 | tf2_yolov3_3.5 | No | YOLOv3 | https://arxiv.org/abs/1804.02767 | COCO | https://cocodataset.org/#download | 416*416*3 | 0.377 | 0.331 | 65.9 | 267.82 | 521.20 | 414.54 | ||||
Pose Detection | Smart Cities | Pose Estimation | PyTorch | pt_movenet_3.5 | No | MoveNet | https://arxiv.org/abs/2105.04154 | COCO | https://cocodataset.org/#download | 192*192*3 | 0.7972 | 0.7984 | 0.5 | 239.12 | 428.21 | 8326.82 | |||
Semantic Segmentation | Medical Imaging | Medical Segmentation | PyTorch | pt_3D-UNET_3.5 | No | 3D-UNET | https://arxiv.org/abs/1606.06650 | KiTS19 | https://kits19.grand-challenge.org/data/ | 128*128*128 | 0.8824 | 0.8774 | 1065.44 | / | / | / | |||
Semantic Segmentation | Automotive | ADAS 2D Segmentation | PyTorch | pt_HRNet_3.5 | No | HRNet | https://arxiv.org/abs/1908.07919 | Cityscapes | https://www.cityscapes-dataset.com/ | 1024*2048*3 | 0.8104 | 0.8061 | 378 | / | / | / | |||
Semantic Segmentation | Automotive | ADAS 3D Detection | PyTorch | pt_pointpillars_3.5 | No | 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 | 55.96 | 70.06 | 187.11 | |||
Semantic Segmentation | Medical Imaging | Medical Segmentation | TensorFlow 2 | tf2_2D-UNET_3.5 | No | 2D-UNET | BraTS | https://drive.google.com/file/d/1A2IU8Sgea1h3fYLpYtFb2v7NYdMjvEhU/view | 144*144*3 | Dice 0.8749 | Dice 0.8735 | 24.6 | 752.19 | 1563.61 | 1177.35 | ||||
Super Resolution | Medical Imaging | Super Resolution | PyTorch | pt_OFA-rcan_3.5 | No | 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 | 62.83 | 100.65 | 53.00 | ||||
Super Resolution | Medical Imaging | Super Resolution | PyTorch | pt_SESR-S_3.5 | No | 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 | 262.49 | 576.34 | 298.31 | |||
Super Resolution | Medical Imaging | Super Resolution | PyTorch | pt_xilinxSR_3.5 | No | AMD Custom | AMD Custom | DIV2K | https://data.vision.ee.ethz.ch/cvl/DIV2K/ | 360*640*3 | 29.04dB | 28.66dB | 364.88 | / | / | / | |||
Super Resolution | Medical Imaging | Super Resolution | TensorFlow | tf_rcan_0.98_3.5 | No | 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% | 59.33 | 83.34 | 43.08 |