diff --git a/docs/appendix/model_zoo.md b/docs/appendix/model_zoo.md index 53789b20134fa0581c093db9f707c4d833809f1b..16d51ad69cc5ed7d28907687f8d1f88392a42c30 100644 --- a/docs/appendix/model_zoo.md +++ b/docs/appendix/model_zoo.md @@ -36,6 +36,7 @@ | 模型 | 模型大小 | 预测时间(毫秒) | BoxAP（%） | |:-------|:-----------|:-------------|:----------| +|[FasterRCNN-ResNet18-FPN](https://bj.bcebos.com/paddlex/pretrained_weights/faster_rcnn_r18_fpn_1x.tar) | 173.2M | - | 32.6 | |[FasterRCNN-ResNet50](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar)|136.0MB| 197.715 | 35.2 | |[FasterRCNN-ResNet50_vd](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar)| 136.1MB | 475.700 | 36.4 | |[FasterRCNN-ResNet101](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar)| 212.5MB | 582.911 | 38.3 | @@ -55,6 +56,7 @@ | 模型 | 模型大小 | 预测时间(毫秒) | BoxAP (%) | MaskAP (%) | |:-------|:-----------|:-------------|:----------|:----------| +|[MaskRCNN-ResNet18-FPN](https://bj.bcebos.com/paddlex/pretrained_weights/mask_rcnn_r18_fpn_1x.tar) | 189.1MB | - | 33.6 | 30.5 | |[MaskRCNN-ResNet50](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_2x.tar) | 143.9MB | 87 | 38.2 | 33.4 | |[MaskRCNN-ResNet50-FPN](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_2x.tar)| 177.7MB | 63.9 | 38.7 | 34.7 | |[MaskRCNN-ResNet50_vd-FPN](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_2x.tar) | 177.7MB | 63.1 | 39.8 || 35.4 | diff --git a/docs/train/instance_segmentation.md b/docs/train/instance_segmentation.md index ec14c87fb9dffdcb1e5099a2f2397de02336123f..8f6a1ec1d48a1cb239df0e24d68c3457def3ff70 100644 --- a/docs/train/instance_segmentation.md +++ b/docs/train/instance_segmentation.md @@ -10,9 +10,9 @@ PaddleX目前提供了MaskRCNN实例分割模型结构,多种backbone模型， | 模型(点击获取代码) | Box MMAP/Seg MMAP | 模型大小 | GPU预测速度 | Arm预测速度 | 备注 | | :---------------- | :------- | :------- | :--------- | :--------- | :----- | -| [MaskRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py) | 38.7%/34.7% | 170.0MB | 160.185ms | - | 模型精度高，适用于服务端部署 | -| [MaskRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r18_fpn.py) | -/- | 120.0MB | - | - | 模型精度高，适用于服务端部署 | -| [MaskRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_hrnet_fpn.py) | 38.7%/34.7% | 116.MB | - | - | 模型精度高，预测速度快，适用于服务端部署 | +| [MaskRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py) | 38.7%/34.7% | 177.7MB | 160.185ms | - | 模型精度高，适用于服务端部署 | +| [MaskRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r18_fpn.py) | 33.6/30.5 | 189.1MB | - | - | 模型精度高，适用于服务端部署 | +| [MaskRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_hrnet_fpn.py) | 38.7%/34.7% | 120.7MB | - | - | 模型精度高，预测速度快，适用于服务端部署 | ## 开始训练 diff --git a/docs/train/object_detection.md b/docs/train/object_detection.md index 6fac169be47631a79361d1ddbf61284132aaf22f..8a8ddcde3b15f233ac8286091ef71cfba81b7643 100644 --- a/docs/train/object_detection.md +++ b/docs/train/object_detection.md @@ -13,8 +13,8 @@ PaddleX目前提供了FasterRCNN和YOLOv3两种检测结构,多种backbone模型 | [YOLOv3-MobileNetV1](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_mobilenetv1.py) | 29.3% | 99.2MB | 15.442ms | - | 模型小，预测速度快，适用于低性能或移动端设备 | | [YOLOv3-MobileNetV3](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_mobilenetv3.py) | 31.6% | 100.7MB | 143.322ms | - | 模型小，移动端上预测速度有优势 | | [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_darknet53.py) | 38.9 | 249.2MB | 42.672ms | - | 模型较大，预测速度快，适用于服务端 | -| [FasterRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r50_fpn.py) | 37.2% | 136.0MB | 197.715ms | - | 模型精度高，适用于服务端部署 | -| [FasterRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r18_fpn.py) | - | - | - | - | 模型精度高，适用于服务端部署 | +| [FasterRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r50_fpn.py) | 37.2% | 167.7MB | 197.715ms | - | 模型精度高，适用于服务端部署 | +| [FasterRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r18_fpn.py) | 32.6% | 173.2MB | - | - | 模型精度高，适用于服务端部署 | | [FasterRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_hrnet_fpn.py) | 36.0% | 115.MB | 81.592ms | - | 模型精度高，预测速度快，适用于服务端部署 | diff --git a/paddlex/cv/models/utils/pretrain_weights.py b/paddlex/cv/models/utils/pretrain_weights.py index 3e7baec66066bac39ac04e7cb7704eab32951d69..0d969981a5fae2ae015beed74e852fa06514ec79 100644 --- a/paddlex/cv/models/utils/pretrain_weights.py +++ b/paddlex/cv/models/utils/pretrain_weights.py @@ -88,6 +88,8 @@ coco_pretrain = { 'https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar', 'YOLOv3_ResNet50_vd_COCO': 'https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar', + 'FasterRCNN_ResNet18_COCO': + 'https://bj.bcebos.com/paddlex/pretrained_weights/faster_rcnn_r18_fpn_1x.tar', 'FasterRCNN_ResNet50_COCO': 'https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_2x.tar', 'FasterRCNN_ResNet50_vd_COCO': @@ -98,6 +100,8 @@ coco_pretrain = { 'https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_2x.tar', 'FasterRCNN_HRNet_W18_COCO': 'https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_2x.tar', + 'MaskRCNN_ResNet18_COCO': + 'https://bj.bcebos.com/paddlex/pretrained_weights/mask_rcnn_r18_fpn_1x.tar', 'MaskRCNN_ResNet50_COCO': 'https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_2x.tar', 'MaskRCNN_ResNet50_vd_COCO': @@ -136,9 +140,10 @@ def get_pretrain_weights(flag, class_name, backbone, save_dir): return flag warning_info = "{} does not support to be finetuned with weights pretrained on the {} dataset, so pretrain_weights is forced to be set to {}" if flag == 'COCO': - if class_name == "FasterRCNN" and backbone in ['ResNet18'] or \ - class_name == "MaskRCNN" and backbone in ['ResNet18'] or \ - class_name == 'DeepLabv3p' and backbone in ['Xception41', 'MobileNetV2_x0.25', 'MobileNetV2_x0.5', 'MobileNetV2_x1.5', 'MobileNetV2_x2.0']: + if class_name == 'DeepLabv3p' and backbone in [ + 'Xception41', 'MobileNetV2_x0.25', 'MobileNetV2_x0.5', + 'MobileNetV2_x1.5', 'MobileNetV2_x2.0' + ]: model_name = '{}_{}'.format(class_name, backbone) logging.warning(warning_info.format(model_name, flag, 'IMAGENET')) flag = 'IMAGENET'