diff --git a/configs/mot/deepsort/README.md b/configs/mot/deepsort/README.md index 05b63f0f05e71faabbd6b9f9e4758fbf8c957f50..919973f54d6e0379691bed95b09560bc6563576c 100644 --- a/configs/mot/deepsort/README.md +++ b/configs/mot/deepsort/README.md @@ -37,17 +37,17 @@ det_results_dir ```bash # use weights released in PaddleDetection model zoo -CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams --output ./det_results_dir +CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams # use saved checkpoint after training -CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=output/jde_darknet53_30e_1088x608/model_final --output ./det_results_dir +CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=output/jde_darknet53_30e_1088x608/model_final ``` ### 2. Tracking ```bash # track the objects by loading detected result files -CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir ./det_results_dir/mot_results +CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir {your detection results} ``` ## Citations diff --git a/configs/mot/deepsort/README_cn.md b/configs/mot/deepsort/README_cn.md index 0958f9c8e0c3d12047edb7f16402dc5b889b85b4..ac09f0ccb7b2da5479a97d38f4491432a9271bd2 100644 --- a/configs/mot/deepsort/README_cn.md +++ b/configs/mot/deepsort/README_cn.md @@ -37,17 +37,17 @@ det_results_dir ```bash # 使用PaddleDetection发布的权重 -CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams --output ./det_results_dir +CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams # 使用训练保存的checkpoint -CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=output/jde_darknet53_30e_1088x608/model_final --output ./det_results_dir +CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=output/jde_darknet53_30e_1088x608/model_final ``` ### 2. 跟踪预测 ```bash # 加载检测结果文件得到跟踪结果 -CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir ./det_results_dir/mot_results +CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir {your detection results} ``` ## 引用 diff --git a/configs/mot/jde/README.md b/configs/mot/jde/README.md index ff5fd511a844371081bc9fa93de50734dd8eef55..1ad3c9060254eb606ee438c34eb95814d4c6a0b0 100644 --- a/configs/mot/jde/README.md +++ b/configs/mot/jde/README.md @@ -11,7 +11,7 @@ English | [简体中文](README_cn.md) [Joint Detection and Embedding](https://arxiv.org/abs/1909.12605)(JDE) is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network.
- +
## Model Zoo @@ -21,6 +21,9 @@ English | [简体中文](README_cn.md) | backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config | | :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: | | DarkNet53 | 1088x608 | 73.2 | 69.4 | 1320 | 6613 | 21629 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_1088x608.yml) | +| DarkNet53 | 864x480 | 70.1 | 65.4 | 1341 | 6454 | 25208 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) | +| DarkNet53 | 576x320 | 63.1 | 64.6 | 1357 | 7083 | 32312 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) | + **Notes:** JDE used 8 GPUs for training and mini-batch size as 4 on each GPU, and trained for 30 epoches. diff --git a/configs/mot/jde/README_cn.md b/configs/mot/jde/README_cn.md index b6b4bd754b99709936786b451ebb476927f6eb29..20c0a4bde30c21bc6bc79cb3bf8fc83ab89913dd 100644 --- a/configs/mot/jde/README_cn.md +++ b/configs/mot/jde/README_cn.md @@ -12,16 +12,18 @@ [Joint Detection and Embedding](https://arxiv.org/abs/1909.12605)(JDE) 是一个快速高性能多目标跟踪器，它是在共享神经网络中同时学习目标检测任务和外观嵌入任务的。
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## 模型库与基线 ### JDE on MOT-16 training set -| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 检测模型 | ReID模型 | 配置文件 | +| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 检测模型 | 配置文件 | | :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: | | DarkNet53 | 1088x608 | 73.2 | 69.4 | 1320 | 6613 | 21629 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_1088x608.yml) | +| DarkNet53 | 864x480 | 70.1 | 65.4 | 1341 | 6454 | 25208 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) | +| DarkNet53 | 576x320 | 63.1 | 64.6 | 1357 | 7083 | 32312 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) | **Notes:** JDE使用8个GPU进行训练，每个GPU上batch size为4，训练了30个epoches。 diff --git a/configs/mot/jde/_base_/jde_reader_576x320.yml b/configs/mot/jde/_base_/jde_reader_576x320.yml new file mode 100644 index 0000000000000000000000000000000000000000..d4d7be98c61916e19e221d99f747836a7f881a58 --- /dev/null +++ b/configs/mot/jde/_base_/jde_reader_576x320.yml @@ -0,0 +1,79 @@ +worker_num: 2 +TrainReader: + sample_transforms: + - Decode: {} + - AugmentHSV: {} + - LetterBoxResize: {target_size: [320, 576]} + - MOTRandomAffine: {} + - RandomFlip: {} + - BboxXYXY2XYWH: {} + - NormalizeBox: {} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_transforms: + - Gt2JDETargetThres: + anchor_masks: [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] + anchors: [[[85,255], [120,320], [170,320], [340,320]], + [[21,64], [30,90], [43,128], [60,180]], + [[6,16], [8,23], [11,32], [16,45]]] + downsample_ratios: [32, 16, 8] + ide_thresh: 0.5 + fg_thresh: 0.5 + bg_thresh: 0.4 + batch_size: 4 + shuffle: true + drop_last: true + use_shared_memory: true + + +EvalReader: + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [320, 576]} + - BboxXYXY2XYWH: {} + - NormalizeBox: {} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_transforms: + - Gt2JDETargetMax: + anchor_masks: [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] + anchors: [[[85,255], [120,320], [170,320], [340,320]], + [[21,64], [30,90], [43,128], [60,180]], + [[6,16], [8,23], [11,32], [16,45]]] + downsample_ratios: [32, 16, 8] + max_iou_thresh: 0.60 + - BboxCXCYWH2XYXY: {} + - Norm2PixelBbox: {} + batch_size: 1 + drop_empty: false + + +TestReader: + inputs_def: + image_shape: [3, 320, 576] + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [320, 576]} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_size: 1 + + +EvalMOTReader: + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [320, 576]} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_size: 1 + + +TestMOTReader: + inputs_def: + image_shape: [3, 320, 576] + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [320, 576]} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_size: 1 diff --git a/configs/mot/jde/_base_/jde_reader_864x480.yml b/configs/mot/jde/_base_/jde_reader_864x480.yml new file mode 100644 index 0000000000000000000000000000000000000000..6690f5491eb3b726a050121eb74d47315a150176 --- /dev/null +++ b/configs/mot/jde/_base_/jde_reader_864x480.yml @@ -0,0 +1,79 @@ +worker_num: 2 +TrainReader: + sample_transforms: + - Decode: {} + - AugmentHSV: {} + - LetterBoxResize: {target_size: [480, 864]} + - MOTRandomAffine: {} + - RandomFlip: {} + - BboxXYXY2XYWH: {} + - NormalizeBox: {} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_transforms: + - Gt2JDETargetThres: + anchor_masks: [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] + anchors: [[[102,305], [143, 429], [203,508], [407,508]], + [[25,76], [36,107], [51,152], [71,215]], + [[6,19], [9,27], [13,38], [18,54]]] + downsample_ratios: [32, 16, 8] + ide_thresh: 0.5 + fg_thresh: 0.5 + bg_thresh: 0.4 + batch_size: 4 + shuffle: true + drop_last: true + use_shared_memory: true + + +EvalReader: + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [480, 864]} + - BboxXYXY2XYWH: {} + - NormalizeBox: {} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_transforms: + - Gt2JDETargetMax: + anchor_masks: [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] + anchors: [[[102,305], [143, 429], [203,508], [407,508]], + [[25,76], [36,107], [51,152], [71,215]], + [[6,19], [9,27], [13,38], [18,54]]] + downsample_ratios: [32, 16, 8] + max_iou_thresh: 0.60 + - BboxCXCYWH2XYXY: {} + - Norm2PixelBbox: {} + batch_size: 1 + drop_empty: false + + +TestReader: + inputs_def: + image_shape: [3, 480, 864] + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [480, 864]} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_size: 1 + + +EvalMOTReader: + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [480, 864]} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_size: 1 + + +TestMOTReader: + inputs_def: + image_shape: [3, 480, 864] + sample_transforms: + - Decode: {} + - LetterBoxResize: {target_size: [480, 864]} + - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True} + - Permute: {} + batch_size: 1 diff --git a/configs/mot/jde/jde_darknet53_30e_576x320.yml b/configs/mot/jde/jde_darknet53_30e_576x320.yml new file mode 100644 index 0000000000000000000000000000000000000000..23ba22efb39d6b26580edaa28abe95ae4208540b --- /dev/null +++ b/configs/mot/jde/jde_darknet53_30e_576x320.yml @@ -0,0 +1,47 @@ +_BASE_: [ + '../../datasets/mot.yml', + '../../runtime.yml', + '_base_/optimizer_30e.yml', + '_base_/jde_darknet53.yml', + '_base_/jde_reader_576x320.yml', +] +weights: output/jde_darknet53_30e_576x320/model_final + +JDE: + detector: YOLOv3 + reid: JDEEmbeddingHead + tracker: JDETracker + +YOLOv3: + backbone: DarkNet + neck: YOLOv3FPN + yolo_head: YOLOv3Head + post_process: JDEBBoxPostProcess + for_mot: True + +YOLOv3Head: + anchors: [[85,255], [120,320], [170,320], [340,320], + [21,64], [30,90], [43,128], [60,180], + [6,16], [8,23], [11,32], [16,45]] + anchor_masks: [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] + loss: JDEDetectionLoss + +JDETracker: + det_thresh: 0.3 + track_buffer: 30 + min_box_area: 200 + motion: KalmanFilter + +JDEBBoxPostProcess: + decode: + name: JDEBox + conf_thresh: 0.5 + downsample_ratio: 32 + nms: + name: MultiClassNMS + keep_top_k: 500 + score_threshold: 0.01 + nms_threshold: 0.4 + nms_top_k: 2000 + normalized: true + return_index: true diff --git a/configs/mot/jde/jde_darknet53_30e_864x480.yml b/configs/mot/jde/jde_darknet53_30e_864x480.yml new file mode 100644 index 0000000000000000000000000000000000000000..96ed2232c3b9d2a5ab2b702792fb61f8dcdffc9a --- /dev/null +++ b/configs/mot/jde/jde_darknet53_30e_864x480.yml @@ -0,0 +1,47 @@ +_BASE_: [ + '../../datasets/mot.yml', + '../../runtime.yml', + '_base_/optimizer_30e.yml', + '_base_/jde_darknet53.yml', + '_base_/jde_reader_864x480.yml', +] +weights: output/jde_darknet53_30e_864x480/model_final + +JDE: + detector: YOLOv3 + reid: JDEEmbeddingHead + tracker: JDETracker + +YOLOv3: + backbone: DarkNet + neck: YOLOv3FPN + yolo_head: YOLOv3Head + post_process: JDEBBoxPostProcess + for_mot: True + +YOLOv3Head: + anchors: [[102,305], [143, 429], [203,508], [407,508], + [25,76], [36,107], [51,152], [71,215], + [6,19], [9,27], [13,38], [18,54]] + anchor_masks: [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] + loss: JDEDetectionLoss + +JDETracker: + det_thresh: 0.3 + track_buffer: 30 + min_box_area: 200 + motion: KalmanFilter + +JDEBBoxPostProcess: + decode: + name: JDEBox + conf_thresh: 0.5 + downsample_ratio: 32 + nms: + name: MultiClassNMS + keep_top_k: 500 + score_threshold: 0.01 + nms_threshold: 0.4 + nms_top_k: 2000 + normalized: true + return_index: true diff --git a/requirements.txt b/requirements.txt index 6e0388c3b912a3f9f9164023a73b7e47afa5cc66..c90680d608003682955ee4e30f62f75e99ebbd9b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,4 +13,5 @@ setuptools>=42.0.0 lap sklearn cython_bbox -motmetrics \ No newline at end of file +motmetrics +openpyxl \ No newline at end of file