# Model Zoo ## 1. Image Classification Dataset：ImageNet1000 ### 1.1 Quantization | Model | Method | Top-1/Top-5 Acc | Model Size（MB） | TensorRT latency(V100, ms) | Download | |:--:|:---:|:--:|:--:|:--:|:--:| |MobileNetV1|-|70.99%/89.68%| 17 | -| [model](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | |MobileNetV1|quant_post|70.18%/89.25% (-0.81%/-0.43%)| 4.4 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_quant_post.tar) | |MobileNetV1|quant_aware|70.60%/89.57% (-0.39%/-0.11%)| 4.4 | -| [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_quant_aware.tar) | | MobileNetV2 | - |72.15%/90.65%| 15 | - | [model](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | | MobileNetV2 | quant_post | 71.15%/90.11% (-1%/-0.54%)| 4.0 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_post.tar) | | MobileNetV2 | quant_aware |72.05%/90.63% (-0.1%/-0.02%)| 4.0 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_aware.tar) | |ResNet50|-|76.50%/93.00%| 99 | 2.71 | [model](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | |ResNet50|quant_post|76.33%/93.02% (-0.17%/+0.02%)| 25.1| 1.19 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_post.tar) | |ResNet50|quant_aware| 76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) | PaddleLite latency(ms) | Device | Model | Method | armv7 Thread 1 | armv7 Thread 2 | armv7 Thread 4 | armv8 Thread 1 | armv8 Thread 2 | armv8 Thread 4 | | ------- | ----------- | ------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | | Qualcomm 835 | MobileNetV1 | FP32 baseline | 96.1942 | 53.2058 | 32.4468 | 88.4955 | 47.95 | 27.5189 | | Qualcomm 835 | MobileNetV1 | quant_aware | 60.8186 | 32.1931 | 16.4275 | 56.4311 | 29.5446 | 15.1053 | | Qualcomm 835 | MobileNetV1 | quant_post | 60.5615 | 32.4016 | 16.6596 | 56.5266 | 29.7178 | 15.1459 | | Qualcomm 835 | MobileNetV2 | FP32 baseline | 65.715 | 38.1346 | 25.155 | 61.3593 | 36.2038 | 22.849 | | Qualcomm 835 | MobileNetV2 | quant_aware | 48.3655 | 30.2021 | 21.9303 | 46.1487 | 27.3146 | 18.3053 | | Qualcomm 835 | MobileNetV2 | quant_post | 48.3495 | 30.3069 | 22.1506 | 45.8715 | 27.4105 | 18.2223 | | Qualcomm 835 | ResNet50 | FP32 baseline | 526.811 | 319.6486 | 205.8345 | 506.1138 | 335.1584 | 214.8936 | | Qualcomm 835 | ResNet50 | quant_aware | 475.4538 | 256.8672 | 139.699 | 461.7344 | 247.9506 | 145.9847 | | Qualcomm 835 | ResNet50 | quant_post | 476.0507 | 256.5963 | 139.7266 | 461.9176 | 248.3795 | 149.353 | | Qualcomm 855 | MobileNetV1 | FP32 baseline | 33.5086 | 19.5773 | 11.7534 | 31.3474 | 18.5382 | 10.0811 | | Qualcomm 855 | MobileNetV1 | quant_aware | 36.7067 | 21.628 | 11.0372 | 14.0238 | 8.199 | 4.2588 | | Qualcomm 855 | MobileNetV1 | quant_post | 37.0498 | 21.7081 | 11.0779 | 14.0947 | 8.1926 | 4.2934 | | Qualcomm 855 | MobileNetV2 | FP32 baseline | 25.0396 | 15.2862 | 9.6609 | 22.909 | 14.1797 | 8.8325 | | Qualcomm 855 | MobileNetV2 | quant_aware | 28.1583 | 18.3317 | 11.8103 | 16.9158 | 11.1606 | 7.4148 | | Qualcomm 855 | MobileNetV2 | quant_post | 28.1631 | 18.3917 | 11.8333 | 16.9399 | 11.1772 | 7.4176 | | Qualcomm 855 | ResNet50 | FP32 baseline | 185.3705 | 113.0825 | 87.0741 | 177.7367 | 110.0433 | 74.4114 | | Qualcomm 855 | ResNet50 | quant_aware | 327.6883 | 202.4536 | 106.243 | 243.5621 | 150.0542 | 78.4205 | | Qualcomm 855 | ResNet50 | quant_post | 328.2683 | 201.9937 | 106.744 | 242.6397 | 150.0338 | 79.8659 | | Kirin 970 | MobileNetV1 | FP32 baseline | 101.2455 | 56.4053 | 35.6484 | 94.8985 | 51.7251 | 31.9511 | | Kirin 970 | MobileNetV1 | quant_aware | 62.5012 | 32.1863 | 16.6018 | 57.7477 | 29.2116 | 15.0703 | | Kirin 970 | MobileNetV1 | quant_post | 62.4412 | 32.2585 | 16.6215 | 57.825 | 29.2573 | 15.1206 | | Kirin 970 | MobileNetV2 | FP32 baseline | 70.4176 | 42.0795 | 25.1939 | 68.9597 | 39.2145 | 22.6617 | | Kirin 970 | MobileNetV2 | quant_aware | 52.9961 | 31.5323 | 22.1447 | 49.4858 | 28.0856 | 18.7287 | | Kirin 970 | MobileNetV2 | quant_post | 53.0961 | 31.7987 | 21.8334 | 49.383 | 28.2358 | 18.3642 | | Kirin 970 | ResNet50 | FP32 baseline | 586.8943 | 344.0858 | 228.2293 | 573.3344 | 351.4332 | 225.8006 | | Kirin 970 | ResNet50 | quant_aware | 488.361 | 260.1697 | 142.416 | 479.5668 | 249.8485 | 138.1742 | | Kirin 970 | ResNet50 | quant_post | 489.6188 | 258.3279 | 142.6063 | 480.0064 | 249.5339 | 138.5284 | ### 1.2 Pruning PaddleLite: env: Qualcomm SnapDragon 845 + armv8 criterion: time cost in Thread1/Thread2/Thread4 PaddleLite version: v2.3 |Model | Method | Top-1/Top-5 Acc | ModelSize(MB) | GFLOPs |PaddleLite cost(ms)|TensorRT speed(FPS)| download | |:--:|:---:|:--:|:--:|:--:|:--:|:--:|:--:| | MobileNetV1 | Baseline | 70.99%/89.68% | 17 | 1.11 |66.052\35.8014\19.5762|-| [download](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | | MobileNetV1 | uniform -50% | 69.4%/88.66% (-1.59%/-1.02%) | 9 | 0.56 | 33.5636\18.6834\10.5076|-|[download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_uniform-50.tar) | | MobileNetV1 | sensitive -30% | 70.4%/89.3% (-0.59%/-0.38%) | 12 | 0.74 | 46.5958\25.3098\13.6982|-|[download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-30.tar) | | MobileNetV1 | sensitive -50% | 69.8% / 88.9% (-1.19%/-0.78%) | 9 | 0.56 |37.9892\20.7882\11.3144|-| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-50.tar) | | MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 |41.7874\23.375\13.3998|-| [download](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | | MobileNetV2 | uniform -50% | 65.79%/86.11% (-6.35%/-4.47%) | 11 | 0.296 |23.8842\13.8698\8.5572|-| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_uniform-50.tar) | | ResNet34 | - | 72.15%/90.65% | 84 | 7.36 |217.808\139.943\96.7504|342.32| [download](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) | | ResNet34 | uniform -50% | 70.99%/89.95% (-1.36%/-0.87%) | 41 | 3.67 |114.787\75.0332\51.8438|452.41| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_uniform-50.tar) | | ResNet34 | auto -55.05% | 70.24%/89.63% (-2.04%/-1.06%) | 33 | 3.31 |105.924\69.3222\48.0246|457.25| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_auto-55.tar) | ### 1.3 Distillation | Model | Method | Top-1/Top-5 Acc | Model Size（MB） | Download | |:--:|:---:|:--:|:--:|:--:| | MobileNetV1 | student | 70.99%/89.68% | 17 | [model](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | |ResNet50_vd|teacher|79.12%/94.44%| 99 | [model](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | |MobileNetV1|ResNet50_vd[1](#trans1) distill|72.77%/90.68% (+1.78%/+1.00%)| 17 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_distilled.tar) | | MobileNetV2 | student | 72.15%/90.65% | 15 | [model](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | | MobileNetV2 | ResNet50_vd distill | 74.28%/91.53% (+2.13%/+0.88%) | 15 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_distilled.tar) | | ResNet50 | student | 76.50%/93.00% | 99 | [model](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | |ResNet101|teacher|77.56%/93.64%| 173 | [model](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | | ResNet50 | ResNet101 distill | 77.29%/93.65% (+0.79%/+0.65%) | 99 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_distilled.tar) | Note: The `_vd` suffix indicates that the pre-trained model uses Mixup. Please refer to the detailed introduction: [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412) ### 1.4 NAS | Model | Method | Top-1/Top-5 Acc | Volume（MB） | GFLOPs | Download | |:--:|:---:|:--:|:--:|:--:|:--:| | MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 | [model](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | | MobileNetV2_NAS | SANAS | 71.518%/90.208% (-0.632%/-0.442%) | 14 | 0.295 | [model](https://paddlemodels.cdn.bcebos.com/PaddleSlim/MobileNetV2_sanas.tar) | Dataset: Cifar10 | Model | Method | Acc | Params（MB） | Download | |:---:|:--:|:--:|:--:|:--:| | Darts | - | 97.135% | 3.767 | - | | Darts_SA(Based on Darts) | SANAS | 97.276%(+0.141%) | 3.344(-11.2%) | - | Note: The token of MobileNetV2_NAS is [4, 4, 5, 1, 1, 2, 1, 1, 0, 2, 6, 2, 0, 3, 4, 5, 0, 4, 5, 5, 1, 4, 8, 0, 0]. The token of Darts_SA is [5, 5, 0, 5, 5, 10, 7, 7, 5, 7, 7, 11, 10, 12, 10, 0, 5, 3, 10, 8]. ## 2. Object Detection ### 2.1 Quantization Dataset： COCO 2017 | Model | Method | Dataset | Image/GPU | Input 608 Box AP | Input 416 Box AP | Input 320 Box AP | Model Size（MB） | TensorRT latency(V100, ms) | Download | | :----------------------------: | :---------: | :----: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |:----------: | | MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.1 | 95 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | MobileNet-V1-YOLOv3 | quant_post | COCO | 8 | 27.9 (-1.4)| 28.0 (-1.3) | 26.0 (-1.0) | 25 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) | | MobileNet-V1-YOLOv3 | quant_aware | COCO | 8 | 28.1 (-1.2)| 28.2 (-1.1) | 25.8 (-1.2) | 26.3 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) | | R34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 162 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | R34-YOLOv3 | quant_post | COCO | 8 | 35.7 (-0.5) | - | - | 42.7 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar) | | R34-YOLOv3 | quant_aware | COCO | 8 | 35.2 (-1.0) | 33.3 (-1.0) | 30.3 (-1.1)| 44 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) | | R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | - | - | 177 | 18.56 |[model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar) | | R50-dcn-YOLOv3 obj365_pretrain | quant_aware | COCO | 8 | 40.6 (-0.8) | 37.5 | 34.1 | 66 | 14.64 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_quant_aware.tar) | Dataset：WIDER-FACE | Model | Method | Image/GPU | Input Size | Easy/Medium/Hard | Model Size（MB） | Download | | :------------: | :---------: | :-------: | :--------: | :-----------------------------: | :--------------: | :----------------------------------------------------------: | | BlazeFace | - | 8 | 640 | 91.5/89.2/79.7 | 815 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | | BlazeFace | quant_post | 8 | 640 | 87.8/85.1/74.9 (-3.7/-4.1/-4.8) | 228 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_post.tar) | | BlazeFace | quant_aware | 8 | 640 | 90.5/87.9/77.6 (-1.0/-1.3/-2.1) | 228 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_aware.tar) | | BlazeFace-Lite | - | 8 | 640 | 90.9/88.5/78.1 | 711 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | | BlazeFace-Lite | quant_post | 8 | 640 | 89.4/86.7/75.7 (-1.5/-1.8/-2.4) | 211 | [model]((https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_post.tar)) | | BlazeFace-Lite | quant_aware | 8 | 640 | 89.7/87.3/77.0 (-1.2/-1.2/-1.1) | 211 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_aware.tar) | | BlazeFace-NAS | - | 8 | 640 | 83.7/80.7/65.8 | 244 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | | BlazeFace-NAS | quant_post | 8 | 640 | 81.6/78.3/63.6 (-2.1/-2.4/-2.2) | 71 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_post.tar) | | BlazeFace-NAS | quant_aware | 8 | 640 | 83.1/79.7/64.2 (-0.6/-1.0/-1.6) | 71 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_aware.tar) | ### 2.2 Pruning Dataset：Pasacl VOC & COCO 2017 | Model | Method | Dataset | Image/GPU | Input 608 Box AP | Input 416 Box AP | Input 320 Box AP | Model Size(MB) | GFLOPs (608*608) | Download | | :----------------------------: | :---------------: | :--------: | :-------: | :--------------: | :--------------: | :--------------: | :------------: | :--------------: | :----------------------------------------------------------: | | MobileNet-V1-YOLOv3 | Baseline | Pascal VOC | 8 | 76.2 | 76.7 | 75.3 | 94 | 40.49 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) | | MobileNet-V1-YOLOv3 | sensitive -52.88% | Pascal VOC | 8 | 77.6 (+1.4) | 77.7 (1.0) | 75.5 (+0.2) | 31 | 19.08 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_v1_voc_prune.tar) | | MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.0 | 95 | 41.35 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | MobileNet-V1-YOLOv3 | sensitive -51.77% | COCO | 8 | 26.0 (-3.3) | 25.1 (-4.2) | 22.6 (-4.4) | 32 | 19.94 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_v1_prune.tar) | | R50-dcn-YOLOv3 | - | COCO | 8 | 39.1 | - | - | 177 | 89.60 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar) | | R50-dcn-YOLOv3 | sensitive -9.37% | COCO | 8 | 39.3 (+0.2) | - | - | 150 | 81.20 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_prune.tar) | | R50-dcn-YOLOv3 | sensitive -24.68% | COCO | 8 | 37.3 (-1.8) | - | - | 113 | 67.48 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_prune578.tar) | | R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | - | - | 177 | 89.60 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar) | | R50-dcn-YOLOv3 obj365_pretrain | sensitive -9.37% | COCO | 8 | 40.5 (-0.9) | - | - | 150 | 81.20 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_prune.tar) | | R50-dcn-YOLOv3 obj365_pretrain | sensitive -24.68% | COCO | 8 | 37.8 (-3.3) | - | - | 113 | 67.48 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_prune578.tar) | ### 2.3 Distillation Dataset：Pasacl VOC & COCO 2017 | Model | Method | Dataset | Image/GPU | Input 608 Box AP | Input 416 Box AP | Input 320 Box AP | Model Size（MB） | Download | | :-----------------: | :---------------------: | :--------: | :-------: | :--------------: | :--------------: | :--------------: | :--------------: | :----------------------------------------------------------: | | MobileNet-V1-YOLOv3 | - | Pascal VOC | 8 | 76.2 | 76.7 | 75.3 | 94 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) | | ResNet34-YOLOv3 | - | Pascal VOC | 8 | 82.6 | 81.9 | 80.1 | 162 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar) | | MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill | Pascal VOC | 8 | 79.0 (+2.8) | 78.2 (+1.5) | 75.5 (+0.2) | 94 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar) | | MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.0 | 95 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | ResNet34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 163 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill | COCO | 8 | 31.4 (+2.1) | 30.0 (+0.7) | 27.1 (+0.1) | 95 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar) | ### 2.4 NAS Dataset: WIDER-FACE | Model | Method | Image/GPU | Input size | Easy/Medium/Hard | volume（KB） | latency（ms）| Download | | :------------: | :---------: | :-------: | :------: | :-----------------------------: | :------------: | :------------: | :----------------------------------------------------------: | | BlazeFace | - | 8 | 640 | 91.5/89.2/79.7 | 815 | 71.862 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | | BlazeFace-NAS | - | 8 | 640 | 83.7/80.7/65.8 | 244 | 21.117 |[model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | | BlazeFace-NASV2 | SANAS | 8 | 640 | 87.0/83.7/68.5 | 389 | 22.558 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | Note: latency is based on latency_855.txt, the file is test on 855 by PaddleLite。The config of BlazeFace-NASV2 is in [there](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/face_detection/blazeface_nas_v2.yml). ## 3. Image Segmentation Dataset：Cityscapes ### 3.1 Quantization | Model | Method | mIoU | Model Size（MB） | Download | | :--------------------: | :---------: | :-----------: | :--------------: | :----------------------------------------------------------: | | DeepLabv3+/MobileNetv1 | - | 63.26 | 6.6 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1.tar ) | | DeepLabv3+/MobileNetv1 | quant_post | 58.63 (-4.63) | 1.8 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1_2049x1025_quant_post.tar) | | DeepLabv3+/MobileNetv1 | quant_aware | 62.03 (-1.23) | 1.8 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1_2049x1025_quant_aware.tar) | | DeepLabv3+/MobileNetv2 | - | 69.81 | 7.4 | [model](https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz) | | DeepLabv3+/MobileNetv2 | quant_post | 67.59 (-2.22) | 2.1 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv2_2049x1025_quant_post.tar) | | DeepLabv3+/MobileNetv2 | quant_aware | 68.33 (-1.48) | 2.1 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv2_2049x1025_quant_aware.tar) | Image segmentation model PaddleLite latency (ms), input size 769x769 | Device | Model | Method | armv7 Thread 1 | armv7 Thread 2 | armv7 Thread 4 | armv8 Thread 1 | armv8 Thread 2 | armv8 Thread 4 | | ------------ | ---------------------- | ------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | | Qualcomm 835 | Deeplabv3- MobileNetV1 | FP32 baseline | 1227.9894 | 734.1922 | 527.9592 | 1109.96 | 699.3818 | 479.0818 | | Qualcomm 835 | Deeplabv3- MobileNetV1 | quant_aware | 848.6544 | 512.785 | 382.9915 | 752.3573 | 455.0901 | 307.8808 | | Qualcomm 835 | Deeplabv3- MobileNetV1 | quant_post | 840.2323 | 510.103 | 371.9315 | 748.9401 | 452.1745 | 309.2084 | | Qualcomm 835 | Deeplabv3-MobileNetV2 | FP32 baseline | 1282.8126 | 793.2064 | 653.6538 | 1193.9908 | 737.1827 | 593.4522 | | Qualcomm 835 | Deeplabv3-MobileNetV2 | quant_aware | 976.0495 | 659.0541 | 513.4279 | 892.1468 | 582.9847 | 484.7512 | | Qualcomm 835 | Deeplabv3-MobileNetV2 | quant_post | 981.44 | 658.4969 | 538.6166 | 885.3273 | 586.1284 | 484.0018 | | Qualcomm 855 | Deeplabv3- MobileNetV1 | FP32 baseline | 568.8748 | 339.8578 | 278.6316 | 420.6031 | 281.3197 | 217.5222 | | Qualcomm 855 | Deeplabv3- MobileNetV1 | quant_aware | 608.7578 | 347.2087 | 260.653 | 241.2394 | 177.3456 | 143.9178 | | Qualcomm 855 | Deeplabv3- MobileNetV1 | quant_post | 609.0142 | 347.3784 | 259.9825 | 239.4103 | 180.1894 | 139.9178 | | Qualcomm 855 | Deeplabv3-MobileNetV2 | FP32 baseline | 639.4425 | 390.1851 | 322.7014 | 477.7667 | 339.7411 | 262.2847 | | Qualcomm 855 | Deeplabv3-MobileNetV2 | quant_aware | 703.7275 | 497.689 | 417.1296 | 394.3586 | 300.2503 | 239.9204 | | Qualcomm 855 | Deeplabv3-MobileNetV2 | quant_post | 705.7589 | 474.4076 | 427.2951 | 394.8352 | 297.4035 | 264.6724 | | Kirin 970 | Deeplabv3- MobileNetV1 | FP32 baseline | 1682.1792 | 1437.9774 | 1181.0246 | 1261.6739 | 1068.6537 | 690.8225 | | Kirin 970 | Deeplabv3- MobileNetV1 | quant_aware | 1062.3394 | 1248.1014 | 878.3157 | 774.6356 | 710.6277 | 528.5376 | | Kirin 970 | Deeplabv3- MobileNetV1 | quant_post | 1109.1917 | 1339.6218 | 866.3587 | 771.5164 | 716.5255 | 500.6497 | | Kirin 970 | Deeplabv3-MobileNetV2 | FP32 baseline | 1771.1301 | 1746.0569 | 1222.4805 | 1448.9739 | 1192.4491 | 760.606 | | Kirin 970 | Deeplabv3-MobileNetV2 | quant_aware | 1320.2905 | 921.4522 | 676.0732 | 1145.8801 | 821.5685 | 590.1713 | | Kirin 970 | Deeplabv3-MobileNetV2 | quant_post | 1320.386 | 918.5328 | 672.2481 | 1020.753 | 820.094 | 591.4114 | ### 3.2 Pruning | Model | Method | mIoU | Model Size（MB） | GFLOPs | Download | | :-------: | :---------------: | :-----------: | :--------------: | :----: | :----------------------------------------------------------: | | fast-scnn | baseline | 69.64 | 11 | 14.41 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape.tar) | | fast-scnn | uniform -17.07% | 69.58 (-0.06) | 8.5 | 11.95 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape_uniform-17.tar) | | fast-scnn | sensitive -47.60% | 66.68 (-2.96) | 5.7 | 7.55 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape_sensitive-47.tar) |