# 模型库和基线 ## 测试环境 - Python 2.7.1 - PaddlePaddle >=1.5 - CUDA 9.0 - cuDNN >=7.4 - NCCL 2.1.2 ## 通用设置 - 所有模型均在COCO17数据集中训练和测试。 - 除非特殊说明，所有ResNet骨干网络采用[ResNet-B](https://arxiv.org/pdf/1812.01187)结构。 - 对于RCNN和RetinaNet系列模型，训练阶段仅使用水平翻转作为数据增强，测试阶段不使用数据增强。 - **推理时间(fps)**: 推理时间是在一张Tesla V100的GPU上通过'tools/eval.py'测试所有验证集得到，单位是fps(图片数/秒), cuDNN版本是7.5，包括数据加载、网络前向执行和后处理, batch size是1。 ## 训练策略 - 我们采用和[Detectron](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#training-schedules)相同的训练策略。 - 1x 策略表示：在总batch size为16时，初始学习率为0.02，在6万轮和8万轮后学习率分别下降10倍，最终训练9万轮。在总batch size为8时，初始学习率为0.01，在12万轮和16万轮后学习率分别下降10倍，最终训练18万轮。 - 2x 策略为1x策略的两倍，同时学习率调整位置也为1x的两倍。 ## ImageNet预训练模型 Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型均通过标准的Imagenet-1k数据集训练得到。[下载链接](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#supported-models-and-performances) - 注：ResNet50模型通过余弦学习率调整策略训练得到。[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) ## 基线 ### Faster & Mask R-CNN | 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP | 下载 | | :------------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----: | :-----------------------------------------------------: | | ResNet50 | Faster | 1 | 1x | 12.747 | 35.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar) | | ResNet50 | Faster | 1 | 2x | 12.686 | 37.1 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_2x.tar) | | ResNet50 | Mask | 1 | 1x | 11.615 | 36.5 | 32.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_1x.tar) | | ResNet50 | Mask | 1 | 2x | 11.494 | 38.2 | 33.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_2x.tar) | | ResNet50-vd | Faster | 1 | 1x | 12.575 | 36.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar) | | ResNet50-FPN | Faster | 2 | 1x | 22.273 | 37.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_1x.tar) | | ResNet50-FPN | Faster | 2 | 2x | 22.297 | 37.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_2x.tar) | | ResNet50-FPN | Mask | 1 | 1x | 15.184 | 37.9 | 34.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_1x.tar) | | ResNet50-FPN | Mask | 1 | 2x | 15.881 | 38.7 | 34.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_2x.tar) | | ResNet50-FPN | Cascade Faster | 2 | 1x | 17.507 | 40.9 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_r50_fpn_1x.tar) | | ResNet50-FPN | Cascade Mask | 1 | 1x | - | 41.3 | 35.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_mask_rcnn_r50_fpn_1x.tar) | | ResNet50-vd-FPN | Faster | 2 | 2x | 21.847 | 38.9 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar) | | ResNet50-vd-FPN | Mask | 1 | 2x | 15.825 | 39.8 | 35.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_2x.tar) | | CBResNet50-vd-FPN | Faster | 2 | 1x | - | 39.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_cbr50_vd_dual_fpn_1x.tar) | | ResNet101 | Faster | 1 | 1x | 9.316 | 38.3 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar) | | ResNet101-FPN | Faster | 1 | 1x | 17.297 | 38.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) | | ResNet101-FPN | Faster | 1 | 2x | 17.246 | 39.1 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) | | ResNet101-FPN | Mask | 1 | 1x | 12.983 | 39.5 | 35.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_fpn_1x.tar) | | ResNet101-vd-FPN | Faster | 1 | 1x | 17.011 | 40.5 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_1x.tar) | | ResNet101-vd-FPN | Faster | 1 | 2x | 16.934 | 40.8 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_2x.tar) | | ResNet101-vd-FPN | Mask | 1 | 1x | 13.105 | 41.4 | 36.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_vd_fpn_1x.tar) | | ResNeXt101-vd-FPN | Faster | 1 | 1x | 8.815 | 42.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_1x.tar) | | ResNeXt101-vd-FPN | Faster | 1 | 2x | 8.809 | 41.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_2x.tar) | | ResNeXt101-vd-FPN | Mask | 1 | 1x | 7.689 | 42.9 | 37.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_x101_vd_64x4d_fpn_1x.tar) | | ResNeXt101-vd-FPN | Mask | 1 | 2x | 7.859 | 42.6 | 37.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_x101_vd_64x4d_fpn_2x.tar) | | SENet154-vd-FPN | Faster | 1 | 1.44x | 3.408 | 42.9 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_se154_vd_fpn_s1x.tar) | | SENet154-vd-FPN | Mask | 1 | 1.44x | 3.233 | 44.0 | 38.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_se154_vd_fpn_s1x.tar) | | ResNet101-vd-FPN | CascadeClsAware Faster | 2 | 1x | - | 44.7(softnms) | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cls_aware_r101_vd_fpn_1x_softnms.tar) | ### Deformable 卷积网络v2 | 骨架网络 | 网络类型 | 卷积 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | Mask AP | 下载 | | :------------------- | :------------- | :-----: |:--------: | :-----: | :-----------: |:----: | :-----: | :----------------------------------------------------------: | | ResNet50-FPN | Faster | c3-c5 | 2 | 1x | 19.978 | 41.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_fpn_1x.tar) | | ResNet50-vd-FPN | Faster | c3-c5 | 2 | 2x | 19.222 | 42.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar) | | ResNet101-vd-FPN | Faster | c3-c5 | 2 | 1x | 14.477 | 44.1 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r101_vd_fpn_1x.tar) | | ResNeXt101-vd-FPN | Faster | c3-c5 | 1 | 1x | 7.209 | 45.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x.tar) | | ResNet50-FPN | Mask | c3-c5 | 1 | 1x | 14.53 | 41.9 | 37.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_dcn_r50_fpn_1x.tar) | | ResNet50-vd-FPN | Mask | c3-c5 | 1 | 2x | 14.832 | 42.9 | 38.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_dcn_r50_vd_fpn_2x.tar) | | ResNet101-vd-FPN | Mask | c3-c5 | 1 | 1x | 11.546 | 44.6 | 39.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_dcn_r101_vd_fpn_1x.tar) | | ResNeXt101-vd-FPN | Mask | c3-c5 | 1 | 1x | 6.45 | 46.2 | 40.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x.tar) | | ResNet50-FPN | Cascade Faster | c3-c5 | 2 | 1x | - | 44.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_fpn_1x.tar) | | ResNet101-vd-FPN | Cascade Faster | c3-c5 | 2 | 1x | - | 46.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_1x.tar) | | ResNeXt101-vd-FPN | Cascade Faster | c3-c5 | 2 | 1x | - | 47.3 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x.tar) | | SENet154-vd-FPN | Cascade Mask | c3-c5 | 1 | 1.44x | - | 51.9 | 43.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_s1x.tar) | | ResNet200-vd-FPN-Nonlocal | CascadeClsAware Faster | c3-c5 | 1 | 2.5x | - | 51.7%(softnms) | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | | CBResNet200-vd-FPN-Nonlocal | Cascade Faster | c3-c5 | 1 | 2.5x | - | 53.3%(softnms) | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cbr200_vd_fpn_dcnv2_nonlocal_softnms.tar) | #### 注意事项: - Deformable卷积网络v2(dcn_v2)参考自论文[Deformable ConvNets v2](https://arxiv.org/abs/1811.11168). - `c3-c5`意思是在resnet模块的3到5阶段增加`dcn`. - 详细的配置文件在[configs/dcn](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn) ### Group Normalization | 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 | Box AP | Mask AP | 下载 | | :------------------- | :------------- |:--------: | :-----: | :----: | :-----: | :----------------------------------------------------------: | | ResNet50-FPN | Faster | 2 | 2x | 39.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_gn_2x.tar) | | ResNet50-FPN | Mask | 1 | 2x | 40.1 | 35.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_gn_2x.tar) | #### 注意事项: - Group Normalization参考论文[Group Normalization](https://arxiv.org/abs/1803.08494). - 详细的配置文件在[configs/gn](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gn) ### YOLO v3 | 骨架网络 | 预训练数据集 | 输入尺寸 | 加入deformable卷积 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 | | :----------- | :--: | :-----: | :-----: |:------------: |:----: | :-------: | :----: | :-------: | | DarkNet53 (paper) | ImageNet | 608 | 否 | 8 | 270e | - | 33.0 | - | | DarkNet53 (paper) | ImageNet | 416 | 否 | 8 | 270e | - | 31.0 | - | | DarkNet53 (paper) | ImageNet | 320 | 否 | 8 | 270e | - | 28.2 | - | | DarkNet53 | ImageNet | 608 | 否 | 8 | 270e | 45.571 | 38.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | | DarkNet53 | ImageNet | 416 | 否 | 8 | 270e | - | 37.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | | DarkNet53 | ImageNet | 320 | 否 | 8 | 270e | - | 34.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | | MobileNet-V1 | ImageNet | 608 | 否 | 8 | 270e | 78.302 | 29.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | MobileNet-V1 | ImageNet | 416 | 否 | 8 | 270e | - | 29.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | MobileNet-V1 | ImageNet | 320 | 否 | 8 | 270e | - | 27.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | ResNet34 | ImageNet | 608 | 否 | 8 | 270e | 63.356 | 36.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | ResNet34 | ImageNet | 416 | 否 | 8 | 270e | - | 34.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | ResNet34 | ImageNet | 320 | 否 | 8 | 270e | - | 31.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | ResNet50_vd | ImageNet | 608 | 是 | 8 | 270e | - | 39.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar) | | ResNet50_vd | Object365 | 608 | 是 | 8 | 270e | - | 41.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar) | ### YOLO v3 基于Pasacl VOC数据集 | 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 | | :----------- | :--: | :-----: | :-----: |:------------: |:----: | :-------: | | DarkNet53 | 608 | 8 | 270e | 54.977 | 83.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet_voc.tar) | | DarkNet53 | 416 | 8 | 270e | - | 83.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet_voc.tar) | | DarkNet53 | 320 | 8 | 270e | - | 82.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet_voc.tar) | | MobileNet-V1 | 608 | 8 | 270e | 104.291 | 76.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) | | MobileNet-V1 | 416 | 8 | 270e | - | 76.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) | | MobileNet-V1 | 320 | 8 | 270e | - | 75.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) | | ResNet34 | 608 | 8 | 270e | 82.247 | 82.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar) | | ResNet34 | 416 | 8 | 270e | - | 81.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar) | | ResNet34 | 320 | 8 | 270e | - | 80.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar) | #### 注意事项: - 上表中也提供了原论文[YOLOv3](https://arxiv.org/abs/1804.02767)中YOLOv3-DarkNet53的精度，我们的实现版本主要从在bounding box的宽度和高度回归上使用了L1损失，图像mixup和label smooth等方法优化了其精度。 - YOLO v3在8卡，总batch size为64下训练270轮。数据增强包括：mixup, 随机颜色失真，随机剪裁，随机扩张，随机插值法，随机翻转。YOLO v3在训练阶段对minibatch采用随机reshape，可以采用相同的模型测试不同尺寸图片，我们分别提供了尺寸为608/416/320大小的测试结果。deformable卷积作用在骨架网络5阶段。 ### RetinaNet | 骨架网络 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 | | :---------------: | :-----: | :-----: | :----: | :-------: | | ResNet50-FPN | 2 | 1x | 36.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r50_fpn_1x.tar) | | ResNet101-FPN | 2 | 1x | 37.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r101_fpn_1x.tar) | | ResNeXt101-vd-FPN | 1 | 1x | 40.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_x101_vd_64x4d_fpn_1x.tar) | **注意事项:** RetinaNet系列模型中，在总batch size为16下情况下，初始学习率改为0.01。 ### SSD | 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略|推理时间(fps) | Box AP | 下载 | | :----------: | :--: | :-----: | :-----: |:------------: |:----: | :-------: | | VGG16 | 300 | 8 | 40万 | 81.613 | 25.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300.tar) | | VGG16 | 512 | 8 | 40万 | 46.007 | 29.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_512.tar) | **注意事项:** VGG-SSD在总batch size为32下训练40万轮。 ### SSD 基于Pascal VOC数据集 | 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 | | :----------- | :--: | :-----: | :-----: | :------------: |:----: | :-------: | | MobileNet v1 | 300 | 32 | 120e | 159.543 | 73.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) | | VGG16 | 300 | 8 | 240e | 117.279 | 77.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300_voc.tar) | | VGG16 | 512 | 8 | 240e | 65.975 | 80.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_512_voc.tar) | **注意事项:** MobileNet-SSD在2卡，总batch size为64下训练120周期。VGG-SSD在总batch size为32下训练240周期。数据增强包括：随机颜色失真，随机剪裁，随机扩张，随机翻转。 ## 人脸检测 详细请参考[人脸检测模型](../configs/face_detection)。 ## 基于Open Images V5数据集的物体检测 详细请参考[Open Images V5数据集基线模型](OIDV5_BASELINE_MODEL.md)。