# 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。 ## 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 | Box AP | Mask AP | 下载 | | :------------------- | :------------- | :-----: | :-----: | :----: | :-----: | :----------------------------------------------------------: | | ResNet50 | Faster | 1 | 1x | 35.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar) | | ResNet50 | Faster | 1 | 2x | 37.1 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_2x.tar) | | ResNet50 | Mask | 1 | 1x | 36.5 | 32.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_1x.tar) | | ResNet50 | Mask | 1 | 2x | 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 | 36.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar) | | ResNet50-FPN | Faster | 2 | 1x | 37.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_1x.tar) | | ResNet50-FPN | Faster | 2 | 2x | 37.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_2x.tar) | | ResNet50-FPN | Mask | 1 | 1x | 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 | 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 | 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 | 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 | 39.8 | 35.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_2x.tar) | | ResNet101 | Faster | 1 | 1x | 38.3 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar) | | ResNet101-FPN | Faster | 1 | 1x | 38.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) | | ResNet101-FPN | Faster | 1 | 2x | 39.1 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) | | ResNet101-FPN | Mask | 1 | 1x | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 44.0 | 38.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_se154_vd_fpn_s1x.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 | Box AP | Mask AP | 下载 | | :------------------- | :------------- | :-----: |:--------: | :-----: | :----: | :-----: | :----------------------------------------------------------: | | ResNet50-FPN | Faster | c3-c5 | 2 | 1x | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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) | #### 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/models/tree/develop/PaddleCV/PaddleDetection/configs/dcn) ### Yolo v3 | 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 | | :----------- | :--: | :-----: | :-----: | :----: | :-------: | | DarkNet53 | 608 | 8 | 270e | 38.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | | DarkNet53 | 416 | 8 | 270e | 37.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | | DarkNet53 | 320 | 8 | 270e | 34.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | | MobileNet-V1 | 608 | 8 | 270e | 29.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | MobileNet-V1 | 416 | 8 | 270e | 29.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | MobileNet-V1 | 320 | 8 | 270e | 27.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | ResNet34 | 608 | 8 | 270e | 36.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | ResNet34 | 416 | 8 | 270e | 34.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | ResNet34 | 320 | 8 | 270e | 31.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.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 | Box AP | 下载 | | :----------- | :--: | :-----: | :-----: | :----: | :-------: | | DarkNet53 | 608 | 8 | 270e | 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 | 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 | 82.6 | [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:** 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。 ### 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 | Box AP | 下载 | | :----------: | :--: | :-------: | :-----: | :----: | :-------: | | VGG16 | 300 | 8 | 40万 | 25.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300.tar) | | VGG16 | 512 | 8 | 40万 | 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 | Box AP | 下载 | | :----------- | :--: | :-----: | :-----: | :----: | :-------: | | MobileNet v1 | 300 | 32 | 120e | 73.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) | | VGG16 | 300 | 8 | 240e | 77.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300_voc.tar) | | VGG16 | 512 | 8 | 240e | 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。