# 模型库 ## 1. 图象分类 数据集：ImageNet1000类 ### 1.1 量化 | 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | TensorRT时延(V100, ms) | 下载 | |:--:|:---:|:--:|:--:|:--:|:--:| |MobileNetV1|-|70.99%/89.68%| 17 | -| [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | |MobileNetV1|quant_post|70.18%/89.25% (-0.81%/-0.43%)| 4.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_quant_post.tar) | |MobileNetV1|quant_aware|70.60%/89.57% (-0.39%/-0.11%)| 4.4 | -| [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_quant_aware.tar) | | MobileNetV2 | - |72.15%/90.65%| 15 | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | | MobileNetV2 | quant_post | 71.15%/90.11% (-1%/-0.54%)| 4.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_post.tar) | | MobileNetV2 | quant_aware |72.05%/90.63% (-0.1%/-0.02%)| 4.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_aware.tar) | |ResNet50|-|76.50%/93.00%| 99 | 2.71 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) |
分类模型Lite时延(ms) armv7 armv8
设备 模型类型 压缩策略 Thread 1 Thread 2 Thread 4 Thread 1 Thread 2 Thread 4
高通835 MobileNetV1 FP32 baseline 96.1942 53.2058 32.4468 88.4955 47.95 27.5189
quant_aware 60.8186 32.1931 16.4275 56.4311 29.5446 15.1053
quant_post 60.5615 32.4016 16.6596 56.5266 29.7178 15.1459
MobileNetV2 FP32 baseline 65.715 38.1346 25.155 61.3593 36.2038 22.849
quant_aware 48.3655 30.2021 21.9303 46.1487 27.3146 18.3053
quant_post 48.3495 30.3069 22.1506 45.8715 27.4105 18.2223
ResNet50 FP32 baseline 526.811 319.6486 205.8345 506.1138 335.1584 214.8936
quant_aware 475.4538 256.8672 139.699 461.7344 247.9506 145.9847
quant_post 476.0507 256.5963 139.7266 461.9176 248.3795 149.353
高通855 MobileNetV1 FP32 baseline 33.5086 19.5773 11.7534 31.3474 18.5382 10.0811
quant_aware 36.7067 21.628 11.0372 14.0238 8.199 4.2588
quant_post 37.0498 21.7081 11.0779 14.0947 8.1926 4.2934
MobileNetV2 FP32 baseline 25.0396 15.2862 9.6609 22.909 14.1797 8.8325
quant_aware 28.1583 18.3317 11.8103 16.9158 11.1606 7.4148
quant_post 28.1631 18.3917 11.8333 16.9399 11.1772 7.4176
ResNet50 FP32 baseline 185.3705 113.0825 87.0741 177.7367 110.0433 74.4114
quant_aware 327.6883 202.4536 106.243 243.5621 150.0542 78.4205
quant_post 328.2683 201.9937 106.744 242.6397 150.0338 79.8659
麒麟970 MobileNetV1 FP32 baseline 101.2455 56.4053 35.6484 94.8985 51.7251 31.9511
quant_aware 62.5012 32.1863 16.6018 57.7477 29.2116 15.0703
quant_post 62.4412 32.2585 16.6215 57.825 29.2573 15.1206
MobileNetV2 FP32 baseline 70.4176 42.0795 25.1939 68.9597 39.2145 22.6617
quant_aware 52.9961 31.5323 22.1447 49.4858 28.0856 18.7287
quant_post 53.0961 31.7987 21.8334 49.383 28.2358 18.3642
ResNet50 FP32 baseline 586.8943 344.0858 228.2293 573.3344 351.4332 225.8006
quant_aware 488.361 260.1697 142.416 479.5668 249.8485 138.1742
quant_post 489.6188 258.3279 142.6063 480.0064 249.5339 138.5284
### 1.2 剪裁 | 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | GFLOPs | 下载 | |:--:|:---:|:--:|:--:|:--:|:--:| | MobileNetV1 | Baseline | 70.99%/89.68% | 17 | 1.11 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_uniform-50.tar) | | MobileNetV1 | sensitive -30% | 70.4%/89.3% (-0.59%/-0.38%) | 12 | 0.74 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-30.tar) | | MobileNetV1 | sensitive -50% | 69.8% / 88.9% (-1.19%/-0.78%) | 9 | 0.56 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-50.tar) | | MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_uniform-50.tar) | | ResNet34 | - | 72.15%/90.65% | 84 | 7.36 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_auto-55.tar) | ### 1.3 蒸馏 | 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | 下载 | |:--:|:---:|:--:|:--:|:--:| | MobileNetV1 | student | 70.99%/89.68% | 17 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | |ResNet50_vd|teacher|79.12%/94.44%| 99 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_distilled.tar) | | MobileNetV2 | student | 72.15%/90.65% | 15 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | | MobileNetV2 | ResNet50_vd distill | 74.28%/91.53% (+2.13%/+0.88%) | 15 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_distilled.tar) | | ResNet50 | student | 76.50%/93.00% | 99 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | |ResNet101|teacher|77.56%/93.64%| 173 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | | ResNet50 | ResNet101 distill | 77.29%/93.65% (+0.79%/+0.65%) | 99 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_distilled.tar) | !!! note "Note" [1]：带_vd后缀代表该预训练模型使用了Mixup，Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412) ## 2. 目标检测 ### 2.1 量化 数据集： COCO 2017 | 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积（MB） | TensorRT时延(V100, ms) | 下载 | | :----------------------------: | :---------: | :----: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |:----------: | | MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.1 | 95 | - | [下载链接](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 | - | [下载链接](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 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) | | R34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 162 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | R34-YOLOv3 | quant_post | COCO | 8 | 35.7 (-0.5) | - | - | 42.7 | - | [下载链接](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 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) | | R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | - | - | 177 | 18.56 |[下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_quant_aware.tar) | 数据集：WIDER-FACE | 模型 | 压缩方法 | Image/GPU | 输入尺寸 | Easy/Medium/Hard | 模型体积（MB） | 下载 | | :------------: | :---------: | :-------: | :------: | :-----------------------------: | :------------: | :----------------------------------------------------------: | | BlazeFace | - | 8 | 640 | 91.5/89.2/79.7 | 815 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_aware.tar) | | BlazeFace-Lite | - | 8 | 640 | 90.9/88.5/78.1 | 711 | [下载链接](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 | [下载链接]((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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_aware.tar) | | BlazeFace-NAS | - | 8 | 640 | 83.7/80.7/65.8 | 244 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_aware.tar) | ### 2.2 剪裁 数据集：Pasacl VOC & COCO 2017 | 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积(MB) | GFLOPs (608*608) | 下载 | | :----------------------------: | :---------------: | :--------: | :-------: | :------------: | :------------: | :------------: | :----------: | :--------------: | :----------------------------------------------------------: | | MobileNet-V1-YOLOv3 | Baseline | Pascal VOC | 8 | 76.2 | 76.7 | 75.3 | 94 | 40.49 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_v1_prune.tar) | | R50-dcn-YOLOv3 | - | COCO | 8 | 39.1 | - | - | 177 | 89.60 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_prune578.tar) | | R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | - | - | 177 | 89.60 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_prune578.tar) | ### 2.3 蒸馏 数据集：Pasacl VOC & COCO 2017 | 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积（MB） | 下载 | | :-----------------: | :---------------------: | :--------: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------------------------------------------------------: | | MobileNet-V1-YOLOv3 | - | Pascal VOC | 8 | 76.2 | 76.7 | 75.3 | 94 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) | | ResNet34-YOLOv3 | - | Pascal VOC | 8 | 82.6 | 81.9 | 80.1 | 162 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar) | | MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.0 | 95 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | ResNet34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 163 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar) | ## 3. 图像分割 数据集：Cityscapes ### 3.1 量化 | 模型 | 压缩方法 | mIoU | 模型体积（MB） | 下载 | | :--------------------: | :---------: | :-----------: | :------------: | :----------------------------------------------------------: | | DeepLabv3+/MobileNetv1 | - | 63.26 | 6.6 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1.tar ) | | DeepLabv3+/MobileNetv1 | quant_post | 58.63 (-4.63) | 1.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1_2049x1025_quant_post.tar) | | DeepLabv3+/MobileNetv1 | quant_aware | 62.03 (-1.23) | 1.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1_2049x1025_quant_aware.tar) | | DeepLabv3+/MobileNetv2 | - | 69.81 | 7.4 | [下载链接](https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz) | | DeepLabv3+/MobileNetv2 | quant_post | 67.59 (-2.22) | 2.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv2_2049x1025_quant_post.tar) | | DeepLabv3+/MobileNetv2 | quant_aware | 68.33 (-1.48) | 2.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv2_2049x1025_quant_aware.tar) |
图像分割模型Lite时延(ms), 输入尺寸769x769 armv7 armv8
设备 模型类型 压缩策略 Thread 1 Thread 2 Thread 4 Thread 1 Thread 2 Thread 4
高通835 Deeplabv3- MobileNetV1 FP32 baseline 1227.9894 734.1922 527.9592 1109.96 699.3818 479.0818
quant_aware 848.6544 512.785 382.9915 752.3573 455.0901 307.8808
quant_post 840.2323 510.103 371.9315 748.9401 452.1745 309.2084
Deeplabv3-MobileNetV2 FP32 baseline 1282.8126 793.2064 653.6538 1193.9908 737.1827 593.4522
quant_aware 976.0495 659.0541 513.4279 892.1468 582.9847 484.7512
quant_post 981.44 658.4969 538.6166 885.3273 586.1284 484.0018
高通855 Deeplabv3-MobileNetV1 FP32 baseline 568.8748 339.8578 278.6316 420.6031 281.3197 217.5222
quant_aware 608.7578 347.2087 260.653 241.2394 177.3456 143.9178
quant_post 609.0142 347.3784 259.9825 239.4103 180.1894 139.9178
Deeplabv3-MobileNetV2 FP32 baseline 639.4425 390.1851 322.7014 477.7667 339.7411 262.2847
quant_aware 703.7275 497.689 417.1296 394.3586 300.2503 239.9204
quant_post 705.7589 474.4076 427.2951 394.8352 297.4035 264.6724
麒麟970 Deeplabv3-MobileNetV1 FP32 baseline 1682.1792 1437.9774 1181.0246 1261.6739 1068.6537 690.8225
quant_aware 1062.3394 1248.1014 878.3157 774.6356 710.6277 528.5376
quant_post 1109.1917 1339.6218 866.3587 771.5164 716.5255 500.6497
Deeplabv3-MobileNetV2 FP32 baseline 1771.1301 1746.0569 1222.4805 1448.9739 1192.4491 760.606
quant_aware 1320.2905 921.4522 676.0732 1145.8801 821.5685 590.1713
quant_post 1320.386 918.5328 672.2481 1020.753 820.094 591.4114
### 3.2 剪裁 | 模型 | 压缩方法 | mIoU | 模型体积（MB） | GFLOPs | 下载 | | :-------: | :---------------: | :-----------: | :------------: | :----: | :----------------------------------------------------------: | | fast-scnn | baseline | 69.64 | 11 | 14.41 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape.tar) | | fast-scnn | uniform -17.07% | 69.58 (-0.06) | 8.5 | 11.95 | [下载链接](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 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape_sensitive-47.tar) |