diff --git a/configs/mot/deepsort/detector/README.md b/configs/mot/deepsort/detector/README.md index 8dd73801fb39751629cb93bc993ac9d7ac2d446e..dd2cd68e9669c25a1f16345b7c795dce71ca063f 100644 --- a/configs/mot/deepsort/detector/README.md +++ b/configs/mot/deepsort/detector/README.md @@ -16,7 +16,7 @@ English | [简体中文](README_cn.md) **Notes:** - The above models are trained with **MOT17-half train** set, it can be downloaded from this [link](https://dataset.bj.bcebos.com/mot/MOT17.zip). - **MOT17-half train** set is a dataset composed of pictures and labels of the first half frame of each video in MOT17 Train dataset (7 sequences in total). **MOT17-half val set** is used for evaluation, which is composed of the second half frame of each video. They can be downloaded from this [link](https://paddledet.bj.bcebos.com/data/mot/mot17half/annotations.zip). Download and unzip it in the `dataset/mot/MOT17/images/`folder. - - YOLOv3 is trained with the same pedestrian dataset as `configs/pedestrian/pedestrian_yolov3_darknet.yml`, which is not open yet. + - YOLOv3 is trained with the same pedestrian dataset as `configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml`, which is not open yet. - For pedestrian tracking, please use pedestrian detector combined with pedestrian ReID model. For vehicle tracking, please use vehicle detector combined with vehicle ReID model. - High quality detected boxes are required for DeepSORT tracking, so the post-processing settings such as NMS threshold of these models are different from those in pure detection tasks. diff --git a/configs/mot/deepsort/detector/README_cn.md b/configs/mot/deepsort/detector/README_cn.md index 6ebe7de7949d1db3d4c4f72db5ad8147f12e1f3d..9110096bb0fde783dccd3f97b3177f02ab28f610 100644 --- a/configs/mot/deepsort/detector/README_cn.md +++ b/configs/mot/deepsort/detector/README_cn.md @@ -17,7 +17,7 @@ **注意:** - 以上模型均可采用**MOT17-half train**数据集训练，数据集可以从[此链接](https://dataset.bj.bcebos.com/mot/MOT17.zip)下载。 - **MOT17-half train**是MOT17的train序列(共7个)每个视频的前一半帧的图片和标注组成的数据集，而为了验证精度可以都用**MOT17-half val**数据集去评估，它是每个视频的后一半帧组成的，数据集可以从[此链接](https://paddledet.bj.bcebos.com/data/mot/mot17half/annotations.zip)下载，并解压放在`dataset/mot/MOT17/images/`文件夹下。 - - YOLOv3和`configs/pedestrian/pedestrian_yolov3_darknet.yml`是相同的pedestrian数据集训练的，此数据集暂未开放。 + - YOLOv3和`configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml`是相同的pedestrian数据集训练的，此数据集暂未开放。 - 行人跟踪请使用行人检测器结合行人ReID模型。车辆跟踪请使用车辆检测器结合车辆ReID模型。 - 用于DeepSORT跟踪时需要高质量的检出框，因此这些模型的NMS阈值等后处理设置会与纯检测任务的设置不同。 diff --git a/configs/pphuman/README.md b/configs/pphuman/README.md index e583668ab7b722a4def52e579eaa121a980ea354..dcaa9543e1390b674e07859006ceeb3b810bed61 100644 --- a/configs/pphuman/README.md +++ b/configs/pphuman/README.md @@ -2,19 +2,24 @@ # PP-YOLOE Human 检测模型 -PaddleDetection团队提供了针对行人的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。 +PaddleDetection团队提供了针对行人的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。PP-Human中使用模型为业务数据集模型，我们同时提供CrowdHuman训练配置，可以使用开源数据进行训练。 其中整理后的COCO格式的CrowdHuman数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/crowdhuman.zip)，检测类别仅一类 `pedestrian(1)`，原始数据集[下载链接](http://www.crowdhuman.org/download.html)。 | 模型 | 数据集 | mAPval
0.5:0.95 | mAPval
0.5 | 下载 | 配置文件 | |:---------|:-------:|:------:|:------:| :----: | :------:| |PP-YOLOE-s| CrowdHuman | 42.5 | 77.9 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_36e_crowdhuman.pdparams) | [配置文件](./ppyoloe_crn_s_36e_crowdhuman.yml) | |PP-YOLOE-l| CrowdHuman | 48.0 | 81.9 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_36e_crowdhuman.pdparams) | [配置文件](./ppyoloe_crn_l_36e_crowdhuman.yml) | +|PP-YOLOE-s| 业务数据集 | 53.2 | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | [配置文件](./ppyoloe_crn_s_36e_pphuman.yml) | +|PP-YOLOE-l| 业务数据集 | 57.8 | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | [配置文件](./ppyoloe_crn_l_36e_pphuman.yml) | **注意:** - PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lrnew = lrdefault * (batch_sizenew * GPU_numbernew) / (batch_sizedefault * GPU_numberdefault)** 调整学习率。 - 具体使用教程请参考[ppyoloe](../ppyoloe#getting-start)。 +# YOLOv3 Human 检测模型 + +请参考[Human_YOLOv3页面](./pedestrian_yolov3/README_cn.md) # PP-YOLOE 香烟检测模型 基于PP-YOLOE模型的香烟检测模型，是实现PP-Human中的基于检测的行为识别方案的一环，如何在PP-Human中使用该模型进行吸烟行为识别，可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/pphuman_action.md)。该模型检测类别仅包含香烟一类。由于数据来源限制，目前暂无法直接公开训练数据。该模型使用了小目标数据集VisDrone上的权重(参照[visdrone](../visdrone))作为预训练模型，以提升检测效果。 @@ -23,6 +28,47 @@ PaddleDetection团队提供了针对行人的基于PP-YOLOE的检测模型，用 |:---------|:-------:|:------:|:------:| :----: | :------:| | PP-YOLOE-s | 香烟业务数据集 | 39.7 | 79.5 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_smoking_visdrone.yml) | +# PP-HGNet 打电话识别模型 +基于PP-HGNet模型实现了打电话行为识别，详细可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/pphuman_action.md)。该模型基于[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/PP-HGNet.md#3.3)套件进行训练。此处提供预测模型下载： + +| 模型 | 数据集 | Acc | 下载 | 配置文件 | +|:---------|:-------:|:------:| :----: | :------:| +| PP-HGNet | 业务数据集 | 86.85 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | - | + +# HRNet 人体关键点模型 +人体关键点模型与ST-GCN模型一起完成[基于骨骼点的行为识别](../../deploy/pipeline/docs/tutorials/pphuman_action.md)方案。关键点模型采用HRNet模型，关于关键点模型相关详细资料可以查看关键点专栏页面[KeyPoint](../keypoint/README.md)。此处提供训练模型下载链接。 + +| 模型 | 数据集 | APval
0.5:0.95 | 下载 | 配置文件 | +|:---------|:-------:|:------:| :----: | :------:| +| HRNet | 业务数据集 | 87.1 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) | [配置文件](./hrnet_w32_256x192.yml) | + + +# ST-GCN 骨骼点行为识别模型 +人体关键点模型与[ST-GCN](https://arxiv.org/abs/1801.07455)模型一起完成[基于骨骼点的行为识别](../../deploy/pipeline/docs/tutorials/pphuman_action.md)方案。 +ST-GCN模型基于[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/applications/PPHuman)完成训练。 +此处提供预测模型下载链接。 + +| 模型 | 数据集 | APval
0.5:0.95 | 下载 | 配置文件 | +|:---------|:-------:|:------:| :----: | :------:| +| ST-GCN | 业务数据集 | 87.1 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/applications/PPHuman/configs/stgcn_pphuman.yaml) | + +# PP-TSM 视频分类模型 +基于`PP-TSM`模型完成了[基于视频分类的行为识别](../../deploy/pipeline/docs/tutorials/pphuman_action.md)方案。 +PP-TSM模型基于[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo/tree/develop/applications/FightRecognition)完成训练。 +此处提供预测模型下载链接。 + +| 模型 | 数据集 | Acc | 下载 | 配置文件 | +|:---------|:-------:|:------:| :----: | :------:| +| PP-TSM | 组合开源数据集 | 89.06 |[下载链接](https://videotag.bj.bcebos.com/PaddleVideo-release2.3/ppTSM_fight.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleVideo/tree/develop/applications/FightRecognition/pptsm_fight_frames_dense.yaml) | + +# PP-HGNet、PP-LCNet 属性识别模型 +基于PP-HGNet、PP-LCNet 模型实现了行人属性识别，详细可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/pphuman_attribute.md)。该模型基于[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/PP-LCNet.md)套件进行训练。此处提供预测模型下载链接. + +| 模型 | 数据集 | mA | 下载 | 配置文件 | +|:---------|:-------:|:------:| :----: | :------:| +| PP-HGNet_small | 业务数据集 | 95.4 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_small_person_attribute_954_infer.zip) | - | +| PP-LCNet | 业务数据集 | 94.5 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/configs/PULC/person_attribute/PPLCNet_x1_0.yaml) | + ## 引用 ``` diff --git a/configs/pphuman/hrnet_w32_256x192.yml b/configs/pphuman/hrnet_w32_256x192.yml new file mode 100644 index 0000000000000000000000000000000000000000..37782b7483430a75dc3f54582ccab66dd8d4655b --- /dev/null +++ b/configs/pphuman/hrnet_w32_256x192.yml @@ -0,0 +1,142 @@ +use_gpu: true +log_iter: 5 +save_dir: output +snapshot_epoch: 10 +weights: output/hrnet_w32_256x192/model_final +epoch: 210 +num_joints: &num_joints 17 +pixel_std: &pixel_std 200 +metric: KeyPointTopDownCOCOEval +num_classes: 1 +train_height: &train_height 256 +train_width: &train_width 192 +trainsize: &trainsize [*train_width, *train_height] +hmsize: &hmsize [48, 64] +flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]] + + +#####model +architecture: TopDownHRNet +pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/Trunc_HRNet_W32_C_pretrained.pdparams + +TopDownHRNet: + backbone: HRNet + post_process: HRNetPostProcess + flip_perm: *flip_perm + num_joints: *num_joints + width: &width 32 + loss: KeyPointMSELoss + +HRNet: + width: *width + freeze_at: -1 + freeze_norm: false + return_idx: [0] + +KeyPointMSELoss: + use_target_weight: true + + +#####optimizer +LearningRate: + base_lr: 0.0005 + schedulers: + - !PiecewiseDecay + milestones: [170, 200] + gamma: 0.1 + - !LinearWarmup + start_factor: 0.001 + steps: 1000 + +OptimizerBuilder: + optimizer: + type: Adam + regularizer: + factor: 0.0 + type: L2 + + +#####data +TrainDataset: + !KeypointTopDownCocoDataset + image_dir: train2017 + anno_path: annotations/person_keypoints_train2017.json + dataset_dir: dataset/coco + num_joints: *num_joints + trainsize: *trainsize + pixel_std: *pixel_std + use_gt_bbox: True + + +EvalDataset: + !KeypointTopDownCocoDataset + image_dir: val2017 + anno_path: annotations/person_keypoints_val2017.json + dataset_dir: dataset/coco + bbox_file: bbox.json + num_joints: *num_joints + trainsize: *trainsize + pixel_std: *pixel_std + use_gt_bbox: True + image_thre: 0.0 + + +TestDataset: + !ImageFolder + anno_path: dataset/coco/keypoint_imagelist.txt + +worker_num: 2 +global_mean: &global_mean [0.485, 0.456, 0.406] +global_std: &global_std [0.229, 0.224, 0.225] +TrainReader: + sample_transforms: + - RandomFlipHalfBodyTransform: + scale: 0.5 + rot: 40 + num_joints_half_body: 8 + prob_half_body: 0.3 + pixel_std: *pixel_std + trainsize: *trainsize + upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + flip_pairs: *flip_perm + - TopDownAffine: + trainsize: *trainsize + - ToHeatmapsTopDown: + hmsize: *hmsize + sigma: 2 + batch_transforms: + - NormalizeImage: + mean: *global_mean + std: *global_std + is_scale: true + - Permute: {} + batch_size: 64 + shuffle: true + drop_last: false + +EvalReader: + sample_transforms: + - TopDownAffine: + trainsize: *trainsize + batch_transforms: + - NormalizeImage: + mean: *global_mean + std: *global_std + is_scale: true + - Permute: {} + batch_size: 16 + +TestReader: + inputs_def: + image_shape: [3, *train_height, *train_width] + sample_transforms: + - Decode: {} + - TopDownEvalAffine: + trainsize: *trainsize + - NormalizeImage: + mean: *global_mean + std: *global_std + is_scale: true + - Permute: {} + batch_size: 1 + fuse_normalize: false #whether to fuse nomalize layer into model while export model diff --git a/configs/pedestrian/README.md b/configs/pphuman/pedestrian_yolov3/README.md similarity index 81% rename from configs/pedestrian/README.md rename to configs/pphuman/pedestrian_yolov3/README.md index f9ba42a1985cb3dcad00d6a3b621d24f37e338ac..2f5517edd36ee1a74e98574646175a331689100e 100644 --- a/configs/pedestrian/README.md +++ b/configs/pphuman/pedestrian_yolov3/README.md @@ -5,7 +5,7 @@ We provide some models implemented by PaddlePaddle to detect objects in specific | Task | Algorithm | Box AP | Download | Configs | |:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:| -| Pedestrian Detection | YOLOv3 | 51.8 | [model](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/pedestrian/pedestrian_yolov3_darknet.yml) | +| Pedestrian Detection | YOLOv3 | 51.8 | [model](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [config](./pedestrian_yolov3_darknet.yml) | ## Pedestrian Detection @@ -36,15 +36,15 @@ Users can employ the model to conduct the inference: ``` export CUDA_VISIBLE_DEVICES=0 -python -u tools/infer.py -c configs/pedestrian/pedestrian_yolov3_darknet.yml \ +python -u tools/infer.py -c configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml \ -o weights=https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams \ - --infer_dir configs/pedestrian/demo \ + --infer_dir configs/pphuman/pedestrian_yolov3/demo \ --draw_threshold 0.3 \ - --output_dir configs/pedestrian/demo/output + --output_dir configs/pphuman/pedestrian_yolov3/demo/output ``` Some inference results are visualized below: -![](../../docs/images/PedestrianDetection_001.png) +![](../../../docs/images/PedestrianDetection_001.png) -![](../../docs/images/PedestrianDetection_004.png) +![](../../../docs/images/PedestrianDetection_004.png) diff --git a/configs/pedestrian/README_cn.md b/configs/pphuman/pedestrian_yolov3/README_cn.md similarity index 80% rename from configs/pedestrian/README_cn.md rename to configs/pphuman/pedestrian_yolov3/README_cn.md index a1d8b86dbf941427ec4a56e2b99b6fb7cc6a2004..6907e0563a015085ee7cc8494a8fcbccee4c7a0e 100644 --- a/configs/pedestrian/README_cn.md +++ b/configs/pphuman/pedestrian_yolov3/README_cn.md @@ -5,7 +5,7 @@ | 任务 | 算法 | 精度(Box AP) | 下载 | 配置文件 | |:---------------------|:---------:|:------:| :---------------------------------------------------------------------------------: | :------:| -| 行人检测 | YOLOv3 | 51.8 | [下载链接](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/pedestrian/pedestrian_yolov3_darknet.yml) | +| 行人检测 | YOLOv3 | 51.8 | [下载链接](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml) | ## 行人检测（Pedestrian Detection） @@ -37,15 +37,15 @@ IOU=.5-.95时的AP为 0.518。 ``` export CUDA_VISIBLE_DEVICES=0 -python -u tools/infer.py -c configs/pedestrian/pedestrian_yolov3_darknet.yml \ +python -u tools/infer.py -c configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml \ -o weights=https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams \ - --infer_dir configs/pedestrian/demo \ + --infer_dir configs/pphuman/pedestrian_yolov3/demo \ --draw_threshold 0.3 \ - --output_dir configs/pedestrian/demo/output + --output_dir configs/pphuman/pedestrian_yolov3/demo/output ``` 预测结果示例： -![](../../docs/images/PedestrianDetection_001.png) +![](../../../docs/images/PedestrianDetection_001.png) -![](../../docs/images/PedestrianDetection_004.png) +![](../../../docs/images/PedestrianDetection_004.png) diff --git a/configs/pedestrian/demo/001.png b/configs/pphuman/pedestrian_yolov3/demo/001.png similarity index 100% rename from configs/pedestrian/demo/001.png rename to configs/pphuman/pedestrian_yolov3/demo/001.png diff --git a/configs/pedestrian/demo/002.png b/configs/pphuman/pedestrian_yolov3/demo/002.png similarity index 100% rename from configs/pedestrian/demo/002.png rename to configs/pphuman/pedestrian_yolov3/demo/002.png diff --git a/configs/pedestrian/demo/003.png b/configs/pphuman/pedestrian_yolov3/demo/003.png similarity index 100% rename from configs/pedestrian/demo/003.png rename to configs/pphuman/pedestrian_yolov3/demo/003.png diff --git a/configs/pedestrian/demo/004.png b/configs/pphuman/pedestrian_yolov3/demo/004.png similarity index 100% rename from configs/pedestrian/demo/004.png rename to configs/pphuman/pedestrian_yolov3/demo/004.png diff --git a/configs/pedestrian/pedestrian.json b/configs/pphuman/pedestrian_yolov3/pedestrian.json similarity index 100% rename from configs/pedestrian/pedestrian.json rename to configs/pphuman/pedestrian_yolov3/pedestrian.json diff --git a/configs/pedestrian/pedestrian_yolov3_darknet.yml b/configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml similarity index 91% rename from configs/pedestrian/pedestrian_yolov3_darknet.yml rename to configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml index eb860dcfab7a52410f40fa67762b895677873b7b..f34439adbc02a3450412b9bb661a1cd5b9568967 100644 --- a/configs/pedestrian/pedestrian_yolov3_darknet.yml +++ b/configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml @@ -26,4 +26,4 @@ EvalDataset: TestDataset: !ImageFolder - anno_path: configs/pedestrian/pedestrian.json + anno_path: configs/pphuman/pedestrian_yolov3/pedestrian.json diff --git a/configs/pphuman/ppyoloe_crn_l_36e_pphuman.yml b/configs/pphuman/ppyoloe_crn_l_36e_pphuman.yml new file mode 100644 index 0000000000000000000000000000000000000000..c1ac43ede159ad8f6086abc18ca83aac3c2ff4a2 --- /dev/null +++ b/configs/pphuman/ppyoloe_crn_l_36e_pphuman.yml @@ -0,0 +1,55 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + '../ppyoloe/_base_/optimizer_300e.yml', + '../ppyoloe/_base_/ppyoloe_crn.yml', + '../ppyoloe/_base_/ppyoloe_reader.yml', +] +log_iter: 100 +snapshot_epoch: 4 +weights: output/ppyoloe_crn_l_36e_pphuman/model_final + +pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams +depth_mult: 1.0 +width_mult: 1.0 + +num_classes: 1 +TrainDataset: + !COCODataSet + image_dir: "" + anno_path: annotations/train.json + dataset_dir: dataset/pphuman + data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] + +EvalDataset: + !COCODataSet + image_dir: "" + anno_path: annotations/val.json + dataset_dir: dataset/pphuman + +TestDataset: + !ImageFolder + anno_path: annotations/val.json + dataset_dir: dataset/pphuman + +TrainReader: + batch_size: 8 + +epoch: 36 +LearningRate: + base_lr: 0.001 + schedulers: + - !CosineDecay + max_epochs: 43 + - !LinearWarmup + start_factor: 0. + epochs: 1 + +PPYOLOEHead: + static_assigner_epoch: -1 + nms: + name: MultiClassNMS + nms_top_k: 1000 + keep_top_k: 100 + score_threshold: 0.01 + nms_threshold: 0.6 diff --git a/configs/pphuman/ppyoloe_crn_s_36e_pphuman.yml b/configs/pphuman/ppyoloe_crn_s_36e_pphuman.yml new file mode 100644 index 0000000000000000000000000000000000000000..34911e2fe96cf7278f8dde9029f3028d4adf900c --- /dev/null +++ b/configs/pphuman/ppyoloe_crn_s_36e_pphuman.yml @@ -0,0 +1,55 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + '../ppyoloe/_base_/optimizer_300e.yml', + '../ppyoloe/_base_/ppyoloe_crn.yml', + '../ppyoloe/_base_/ppyoloe_reader.yml', +] +log_iter: 100 +snapshot_epoch: 4 +weights: output/ppyoloe_crn_s_36e_pphuman/model_final + +pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams +depth_mult: 0.33 +width_mult: 0.50 + +num_classes: 1 +TrainDataset: + !COCODataSet + image_dir: "" + anno_path: annotations/train.json + dataset_dir: dataset/pphuman + data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] + +EvalDataset: + !COCODataSet + image_dir: "" + anno_path: annotations/val.json + dataset_dir: dataset/pphuman + +TestDataset: + !ImageFolder + anno_path: annotations/val.json + dataset_dir: dataset/pphuman + +TrainReader: + batch_size: 8 + +epoch: 36 +LearningRate: + base_lr: 0.001 + schedulers: + - !CosineDecay + max_epochs: 43 + - !LinearWarmup + start_factor: 0. + epochs: 1 + +PPYOLOEHead: + static_assigner_epoch: -1 + nms: + name: MultiClassNMS + nms_top_k: 1000 + keep_top_k: 100 + score_threshold: 0.01 + nms_threshold: 0.6 diff --git a/configs/ppvehicle/README.md b/configs/ppvehicle/README.md index 5559009056a56b727b516ea942c41f67929f4be4..a0a6ee28c0714cc43ab795e68ac25d2236b6d0fd 100644 --- a/configs/ppvehicle/README.md +++ b/configs/ppvehicle/README.md @@ -1,6 +1,6 @@ 简体中文 | [English](README.md) -# PP-YOLOE Vehicle 检测模型 +## PP-YOLOE Vehicle 检测模型 PaddleDetection团队提供了针对自动驾驶场景的基于PP-YOLOE的检测模型，用户可以下载模型进行使用，主要包含5个数据集(BDD100K-DET、BDD100K-MOT、UA-DETRAC、PPVehicle9cls、PPVehicle)。其中前3者为公开数据集，后两者为整合数据集。 - BDD100K-DET具体类别为10类，包括`pedestrian(1), rider(2), car(3), truck(4), bus(5), train(6), motorcycle(7), bicycle(8), traffic light(9), traffic sign(10)`。 @@ -35,6 +35,28 @@ label_list.txt里的一行记录一个对应种类，如下所示： vehicle ``` +## YOLOv3 Vehicle 检测模型 + +请参考[Vehicle_YOLOv3页面](./vehicle_yolov3/README_cn.md) + +## PP-OCRv3 车牌识别模型 + +车牌识别采用Paddle自研超轻量级模型PP-OCRv3_det、PP-OCRv3_rec。在[CCPD数据集](https://github.com/detectRecog/CCPD)（CCPD2019+CCPD2020车牌数据集）上进行了fine-tune。模型训练基于[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/applications/%E8%BD%BB%E9%87%8F%E7%BA%A7%E8%BD%A6%E7%89%8C%E8%AF%86%E5%88%AB.md)完成，我们提供了预测模型下载： + +| 模型 | 数据集 | 精度 | 下载 | 配置文件 | +|:---------|:-------:|:------:| :----: | :------:| +| PP-OCRv3_det | CCPD组合数据集 | hmean:0.979 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz) | [配置文件](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | +| PP-OCRv3_rec | CCPD组合数据集 | acc:0.773 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | [配置文件](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) | + +## PP-LCNet 车牌属性模型 + +车牌属性采用Paddle自研超轻量级模型PP-LCNet。在[VeRi数据集](https://www.v7labs.com/open-datasets/veri-dataset)进行训练。模型训练基于[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/en/PULC/PULC_vehicle_attribute_en.md)完成，我们提供了预测模型下载： + +| 模型 | 数据集 | 精度 | 下载 | 配置文件 | +|:---------|:-------:|:------:| :----: | :------:| +| PP-LCNet_x1_0 | VeRi数据集 | 90.81 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/ppcls/configs/PULC/vehicle_attribute/PPLCNet_x1_0.yaml) | + + ## 引用 ``` @InProceedings{bdd100k, diff --git a/configs/vehicle/README.md b/configs/ppvehicle/vehicle_yolov3/README.md similarity index 83% rename from configs/vehicle/README.md rename to configs/ppvehicle/vehicle_yolov3/README.md index 5e20c6ffa86dcbc6e7d9fa8fbf57a9ae98eccb74..4ad29f751314e47ba1cddf5413f0fb2ce91ab94c 100644 --- a/configs/vehicle/README.md +++ b/configs/ppvehicle/vehicle_yolov3/README.md @@ -5,7 +5,7 @@ We provide some models implemented by PaddlePaddle to detect objects in specific | Task | Algorithm | Box AP | Download | Configs | |:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:| -| Vehicle Detection | YOLOv3 | 54.5 | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vehicle/vehicle_yolov3_darknet.yml) | +| Vehicle Detection | YOLOv3 | 54.5 | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](./vehicle_yolov3_darknet.yml) | ## Vehicle Detection @@ -39,15 +39,15 @@ Users can employ the model to conduct the inference: ``` export CUDA_VISIBLE_DEVICES=0 -python -u tools/infer.py -c configs/vehicle/vehicle_yolov3_darknet.yml \ +python -u tools/infer.py -c configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml \ -o weights=https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams \ - --infer_dir configs/vehicle/demo \ + --infer_dir configs/ppvehicle/vehicle_yolov3/demo \ --draw_threshold 0.2 \ - --output_dir configs/vehicle/demo/output + --output_dir configs/ppvehicle/vehicle_yolov3/demo/output ``` Some inference results are visualized below: -![](../../docs/images/VehicleDetection_001.jpeg) +![](../../../docs/images/VehicleDetection_001.jpeg) -![](../../docs/images/VehicleDetection_005.png) +![](../../../docs/images/VehicleDetection_005.png) diff --git a/configs/vehicle/README_cn.md b/configs/ppvehicle/vehicle_yolov3/README_cn.md similarity index 81% rename from configs/vehicle/README_cn.md rename to configs/ppvehicle/vehicle_yolov3/README_cn.md index 2bd09bb10bb4ab6e56f15fb4411ecd012249b677..52e412d8b3c6395d738f2278b6db664ea94af2ff 100644 --- a/configs/vehicle/README_cn.md +++ b/configs/ppvehicle/vehicle_yolov3/README_cn.md @@ -5,7 +5,7 @@ | 任务 | 算法 | 精度(Box AP) | 下载 | 配置文件 | |:---------------------|:---------:|:------:| :---------------------------------------------------------------------------------: | :------:| -| 车辆检测 | YOLOv3 | 54.5 | [下载链接](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vehicle/vehicle_yolov3_darknet.yml) | +| 车辆检测 | YOLOv3 | 54.5 | [下载链接](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [配置文件](./vehicle_yolov3_darknet.yml) | ## 车辆检测（Vehicle Detection） @@ -40,15 +40,15 @@ IOU=.5时的AP为 0.764。 ``` export CUDA_VISIBLE_DEVICES=0 -python -u tools/infer.py -c configs/vehicle/vehicle_yolov3_darknet.yml \ +python -u tools/infer.py -c configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml \ -o weights=https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams \ - --infer_dir configs/vehicle/demo \ + --infer_dir configs/ppvehicle/vehicle_yolov3/demo \ --draw_threshold 0.2 \ - --output_dir configs/vehicle/demo/output + --output_dir configs/ppvehicle/vehicle_yolov3/demo/output ``` 预测结果示例： -![](../../docs/images/VehicleDetection_001.jpeg) +![](../../../docs/images/VehicleDetection_001.jpeg) -![](../../docs/images/VehicleDetection_005.png) +![](../../../docs/images/VehicleDetection_005.png) diff --git a/configs/vehicle/demo/001.jpeg b/configs/ppvehicle/vehicle_yolov3/demo/001.jpeg similarity index 100% rename from configs/vehicle/demo/001.jpeg rename to configs/ppvehicle/vehicle_yolov3/demo/001.jpeg diff --git a/configs/vehicle/demo/003.png b/configs/ppvehicle/vehicle_yolov3/demo/003.png similarity index 100% rename from configs/vehicle/demo/003.png rename to configs/ppvehicle/vehicle_yolov3/demo/003.png diff --git a/configs/vehicle/demo/004.png b/configs/ppvehicle/vehicle_yolov3/demo/004.png similarity index 100% rename from configs/vehicle/demo/004.png rename to configs/ppvehicle/vehicle_yolov3/demo/004.png diff --git a/configs/vehicle/demo/005.png b/configs/ppvehicle/vehicle_yolov3/demo/005.png similarity index 100% rename from configs/vehicle/demo/005.png rename to configs/ppvehicle/vehicle_yolov3/demo/005.png diff --git a/configs/vehicle/vehicle.json b/configs/ppvehicle/vehicle_yolov3/vehicle.json similarity index 100% rename from configs/vehicle/vehicle.json rename to configs/ppvehicle/vehicle_yolov3/vehicle.json diff --git a/configs/vehicle/vehicle_yolov3_darknet.yml b/configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml similarity index 93% rename from configs/vehicle/vehicle_yolov3_darknet.yml rename to configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml index 17f401ac15480fa76e9ffa8b9e21aad35459c3ce..0afb50b48a450143a83ccc145c5bea58cdd15b3d 100644 --- a/configs/vehicle/vehicle_yolov3_darknet.yml +++ b/configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml @@ -39,4 +39,4 @@ EvalDataset: TestDataset: !ImageFolder - anno_path: configs/vehicle/vehicle.json + anno_path: configs/ppvehicle/vehicle_yolov3/vehicle.json diff --git a/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md index 88ab8841831a2f9df2dac87c43451b34ebdc013d..e4861ed8c446913d66507d1bc60bbca9dad69b0b 100644 --- a/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md +++ b/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md @@ -129,6 +129,18 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_human_attr.yml -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu ``` +### Jetson部署说明 + +由于Jetson平台算力相比服务器有较大差距，有如下使用建议： + +1. 模型选择轻量级版本，特别是跟踪模型，推荐使用`ppyoloe_s: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip` +2. 开启跟踪跳帧功能，推荐使用2或者3: `skip_frame_num: 3` + +使用该推荐配置，在TX2平台上可以达到较高速率，经测试属性案例达到20fps。 + +可以直接修改配置文件（推荐），也可以在命令行中修改（字段较长，不推荐）。 + + ### 参数说明 | 参数 | 是否必须|含义 | @@ -147,6 +159,9 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe | --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False | | --do_entrance_counting | Option | 是否统计出入口流量，默认为False | | --draw_center_traj | Option | 是否绘制跟踪轨迹，默认为False | +| --region_type | Option | 'horizontal'（默认值）、'vertical'：表示流量统计方向选择；'custom'：表示设置闯入区域 | +| --region_polygon | Option | 设置闯入区域多边形多点的坐标，无默认值 | +| --do_break_in_counting | Option | 此项表示做区域闯入检查 | ## 方案介绍 diff --git a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md index 4921a4225316900b7394dcee8be69ab54c6d0d61..b75ece1d6d1009a1a728e889a0bb150ee99a4fbb 100644 --- a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md +++ b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md @@ -135,6 +135,18 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_vehicle_attr.yml -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu ``` +### Jetson部署说明 + +由于Jetson平台算力相比服务器有较大差距，有如下使用建议： + +1. 模型选择轻量级版本，特别是跟踪模型，推荐使用`ppyoloe_s: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip` +2. 开启跟踪跳帧功能，推荐使用2或者3. `skip_frame_num: 3` + +使用该推荐配置，在TX2平台上可以达到较高速率，经测试属性案例达到20fps。 + +可以直接修改配置文件（推荐），也可以在命令行中修改（字段较长，不推荐）。 + + ### 参数说明 | 参数 | 是否必须|含义 | @@ -153,6 +165,9 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe | --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False | | --do_entrance_counting | Option | 是否统计出入口流量，默认为False | | --draw_center_traj | Option | 是否绘制跟踪轨迹，默认为False | +| --region_type | Option | 'horizontal'（默认值）、'vertical'：表示流量统计方向选择；'custom'：表示设置车辆禁停区域 | +| --region_polygon | Option | 设置禁停区域多边形多点的坐标，无默认值 | +| --illegal_parking_time | Option | 设置禁停时间阈值，单位秒（s），-1（默认值）表示不做检查 | ## 方案介绍 diff --git a/deploy/pipeline/ppvehicle/vehicle_plate.py b/deploy/pipeline/ppvehicle/vehicle_plate.py index d1263793788a4d37a106f575c2829dd7991897ae..01f260e7f183d5efccec776e02e51f9c3e6fe37e 100644 --- a/deploy/pipeline/ppvehicle/vehicle_plate.py +++ b/deploy/pipeline/ppvehicle/vehicle_plate.py @@ -298,6 +298,7 @@ class PlateRecognizer(object): '甘': 'GS-', '青': 'QH-', '宁': 'NX-', + '闽': 'FJ-', '·': ' ' } for _char in text: