diff --git a/configs/ppyolo/README.md b/configs/ppyolo/README.md index 179fe725d75db2e6e8e593d7b4e1ea441adbe97f..79bc593ad5fdaeda62aedb9b500cc6751553b649 100644 --- a/configs/ppyolo/README.md +++ b/configs/ppyolo/README.md @@ -32,20 +32,22 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度： ## 模型库 +### PP-YOLO模型 + | 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 | |:------------------------:|:-------:|:-------------:|:----------:| :-------:| :----: | :------------: | :---------------------: | :------: | :------: | | YOLOv4(AlexyAB) | - | - | CSPDarknet | 608 | 43.5 | 62 | 105.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) | | YOLOv4(AlexyAB) | - | - | CSPDarknet | 512 | 43.0 | 83 | 138.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) | | YOLOv4(AlexyAB) | - | - | CSPDarknet | 416 | 41.2 | 96 | 164.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) | | YOLOv4(AlexyAB) | - | - | CSPDarknet | 320 | 38.0 | 123 | 199.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) | -| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 45.2 | 72.9 | 155.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | -| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 44.4 | 89.9 | 188.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | -| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.5 | 109.1 | 215.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | -| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 39.3 | 132.2 | 242.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | +| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 45.2 | 72.9 | 155.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | +| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 44.4 | 89.9 | 188.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | +| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.5 | 109.1 | 215.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | +| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 39.3 | 132.2 | 242.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | **注意:** -- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用test-dev2017左右测试集。 +- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用test-dev2017左右测试集，`Box AP`为`mAP(IoU=0.5:0.95)`评估结果。 - PP-YOLO模型训练过程中使用8GPU，每GPU batch size为24进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。 - PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。 - PP-YOLO模型推理速度测试数据为使用`tools/export_model.py`脚本导出模型后，使用`deploy/python/infer.py`脚本中的`--run_benchnark`参数使用Paddle预测库进行推理速度benchmark测试结果, 且测试的均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。 @@ -53,6 +55,16 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度： - YOLOv4(AlexyAB)模型精度和V100 FP32推理速度数据使用[YOLOv4 github库](https://github.com/AlexeyAB/darknet)提供的单卡V100上精度速度测试数据，V100 TensorRT FP16推理速度为使用[AlexyAB/darknet]库中tkDNN配置于单卡V100上的测试结果。 - YOLOv4(AlexyAB)行`模型下载`和`配置文件`为PaddleDetection复现的YOLOv4模型，目前评估精度已对齐，支持finetune，训练精度对齐中，可参见[PaddleDetection YOLOv4 模型](../yolov4/README.md) +### PP-YOLO tiny模型 + +| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 | +|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :----: | :------------: | :---------------------: | :------: | :------: | +| PP-YOLO tiny | 4 | 32 | ResNet18vd | 416 | 47.0 | 401.6 | 724.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml) | +| PP-YOLO tiny | 4 | 32 | ResNet18vd | 320 | 43.7 | 478.5 | 791.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml) | + +- PP-YOLO tiny模型使用COCO数据集中train2017作为训练集，使用val2017左右测试集，`Box AP`为`mAP(IoU=0.5)`评估结果。 +- PP-YOLO tiny模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。 +- PP-YOLO tiny模型推理速度测试环境配置和测试方法与PP-YOLO模型一致。 ## 使用说明 diff --git a/configs/ppyolo/ppyolo_lb.yml b/configs/ppyolo/ppyolo_lb.yml deleted file mode 100644 index 12a5c38c27f714a0231a55531af24ca0e43039db..0000000000000000000000000000000000000000 --- a/configs/ppyolo/ppyolo_lb.yml +++ /dev/null @@ -1,92 +0,0 @@ -architecture: YOLOv3 -use_gpu: true -max_iters: 250000 -log_smooth_window: 100 -log_iter: 100 -save_dir: output -snapshot_iter: 10000 -metric: COCO -pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar -weights: output/ppyolo_lb/model_final -num_classes: 80 -use_fine_grained_loss: true -use_ema: true -ema_decay: 0.9998 - -YOLOv3: - backbone: ResNet - yolo_head: YOLOv3Head - use_fine_grained_loss: true - -ResNet: - norm_type: sync_bn - freeze_at: 0 - freeze_norm: false - norm_decay: 0. - depth: 50 - feature_maps: [3, 4, 5] - variant: d - dcn_v2_stages: [5] - -YOLOv3Head: - anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] - anchors: [[10, 13], [16, 30], [33, 23], - [30, 61], [62, 45], [59, 119], - [116, 90], [156, 198], [373, 326]] - norm_decay: 0. - coord_conv: true - iou_aware: true - iou_aware_factor: 0.4 - scale_x_y: 1.05 - spp: true - yolo_loss: YOLOv3Loss - nms: MatrixNMS - drop_block: true - -YOLOv3Loss: - batch_size: 24 - ignore_thresh: 0.7 - scale_x_y: 1.05 - label_smooth: false - use_fine_grained_loss: true - iou_loss: IouLoss - iou_aware_loss: IouAwareLoss - -IouLoss: - loss_weight: 2.5 - max_height: 608 - max_width: 608 - -IouAwareLoss: - loss_weight: 1.0 - max_height: 608 - max_width: 608 - -MatrixNMS: - background_label: -1 - keep_top_k: 100 - normalized: false - score_threshold: 0.01 - post_threshold: 0.01 - -LearningRate: - base_lr: 0.01 - schedulers: - - !PiecewiseDecay - gamma: 0.1 - milestones: - - 150000 - - 200000 - - !LinearWarmup - start_factor: 0. - steps: 4000 - -OptimizerBuilder: - optimizer: - momentum: 0.9 - type: Momentum - regularizer: - factor: 0.0005 - type: L2 - -_READER_: 'ppyolo_reader_lb.yml' diff --git a/configs/ppyolo/ppyolo_reader_lb.yml b/configs/ppyolo/ppyolo_reader_lb.yml deleted file mode 100644 index 295ddbaf9f265b0c9ee2e752f49983890518596a..0000000000000000000000000000000000000000 --- a/configs/ppyolo/ppyolo_reader_lb.yml +++ /dev/null @@ -1,110 +0,0 @@ -TrainReader: - inputs_def: - fields: ['image', 'gt_bbox', 'gt_class', 'gt_score'] - num_max_boxes: 50 - dataset: - !COCODataSet - image_dir: train2017 - anno_path: annotations/instances_train2017.json - dataset_dir: dataset/coco - with_background: false - sample_transforms: - - !DecodeImage - to_rgb: True - with_mixup: True - - !MixupImage - alpha: 1.5 - beta: 1.5 - - !ColorDistort {} - - !RandomExpand - fill_value: [123.675, 116.28, 103.53] - - !RandomCrop {} - - !RandomFlipImage - is_normalized: false - - !NormalizeBox {} - - !PadBox - num_max_boxes: 50 - - !BboxXYXY2XYWH {} - batch_transforms: - - !RandomShape - sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] - random_inter: True - - !NormalizeImage - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] - is_scale: True - is_channel_first: false - - !Permute - to_bgr: false - channel_first: True - # Gt2YoloTarget is only used when use_fine_grained_loss set as true, - # this operator will be deleted automatically if use_fine_grained_loss - # is set as false - - !Gt2YoloTarget - anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] - anchors: [[10, 13], [16, 30], [33, 23], - [30, 61], [62, 45], [59, 119], - [116, 90], [156, 198], [373, 326]] - downsample_ratios: [32, 16, 8] - batch_size: 24 - shuffle: true - mixup_epoch: 25000 - drop_last: true - worker_num: 8 - bufsize: 4 - use_process: true - -EvalReader: - inputs_def: - fields: ['image', 'im_size', 'im_id'] - num_max_boxes: 50 - dataset: - !COCODataSet - image_dir: val2017 - anno_path: annotations/instances_val2017.json - dataset_dir: dataset/coco - with_background: false - sample_transforms: - - !DecodeImage - to_rgb: True - - !ResizeImage - target_size: 608 - interp: 2 - - !NormalizeImage - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] - is_scale: True - is_channel_first: false - - !PadBox - num_max_boxes: 50 - - !Permute - to_bgr: false - channel_first: True - batch_size: 8 - drop_empty: false - worker_num: 8 - bufsize: 4 - -TestReader: - inputs_def: - image_shape: [3, 608, 608] - fields: ['image', 'im_size', 'im_id'] - dataset: - !ImageFolder - anno_path: annotations/instances_val2017.json - with_background: false - sample_transforms: - - !DecodeImage - to_rgb: True - - !ResizeImage - target_size: 608 - interp: 2 - - !NormalizeImage - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] - is_scale: True - is_channel_first: false - - !Permute - to_bgr: false - channel_first: True - batch_size: 1 diff --git a/configs/ppyolo/ppyolo_tiny.yml b/configs/ppyolo/ppyolo_tiny.yml index 07ce536ba69d886020daf1666511c9e22b38e23a..c054d5f5d574dc7419b07cbc8e4a22113f0778ae 100755 --- a/configs/ppyolo/ppyolo_tiny.yml +++ b/configs/ppyolo/ppyolo_tiny.yml @@ -33,8 +33,6 @@ YOLOv3Head: [81, 82], [135, 169], [344, 319]] norm_decay: 0. conv_block_num: 0 - iou_aware: true - iou_aware_factor: 0.4 scale_x_y: 1.05 yolo_loss: YOLOv3Loss nms: MatrixNMS @@ -47,18 +45,12 @@ YOLOv3Loss: label_smooth: false use_fine_grained_loss: true iou_loss: IouLoss - iou_aware_loss: IouAwareLoss IouLoss: loss_weight: 2.5 max_height: 608 max_width: 608 -IouAwareLoss: - loss_weight: 1.0 - max_height: 608 - max_width: 608 - MatrixNMS: background_label: -1 keep_top_k: 100