ByteTrack 是一个简单、快速、强大的多对象跟踪器,通过关联每个 Detection Box 进行多对象跟踪。
致力于在学术研究和工业界之间架起一座桥梁。了解更多的网络细节,请参考ByteTrack论文。
论文: Bytetrack is accepted by ECCV 2022!
ByteTrack 使用当前性能非常优秀的检测器 YOLOX 得到检测结果。 \在数据关联的过程中,和 SORT 一样,只使用卡尔曼滤波来预测当前帧的跟踪轨迹在下一帧的位置, \预测的框和实际的检测框之间的 IoU 作为两次匹配时的相似度,通过匈牙利算法完成匹配。
使用的数据集:使用COCO格式的数据集格式
支持的数据集: COCO 或者与 MS COCO 格式相同的数据集
支持的标注: COCO 或者与 MS COCO 相同格式的标注
-
目录结构如下,由用户定义目录和文件的名称
datasets |——————mot | └——————train | └——————test └——————crowdhuman | └——————Crowdhuman_train | └——————Crowdhuman_val | └——————annotation_train.odgt | └——————annotation_val.odgt └——————MOT20 | └——————train | └——————test └——————Cityscapes | └——————images | └——————labels_with_ids └——————ETHZ └——————eth01 └——————... └——————eth07
然后,您需要将数据集转换为 COCO 格式并混合不同的训练数据: cd <ByteTrack_HOME> python3 tools/convert_mot17_to_coco.py python3 tools/convert_mot20_to_coco.py python3 tools/convert_crowdhuman_to_coco.py python3 tools/convert_cityperson_to_coco.py python3 tools/convert_ethz_to_coco.py
在混合不同的数据集之前,您需要按照mix_xxx.py中的操作创建一个数据文件夹和链接。最后,您可以混合训练数据: cd <ByteTrack_HOME> python3 tools/mix_data_ablation.py python3 tools/mix_data_test_mot17.py python3 tools/mix_data_test_mot20.py
- 如果用户需要自定义数据集,则需要将数据集格式转化为coco数据格式,并且,json文件中的数据要和图片数据对应好。
- 硬件(Ascend)
- 使用Ascend处理器来搭建硬件环境。
- 框架
- 如需查看详情,请参见如下资源
-
通过官方网站安装Mindspore后,您可以按照如下步骤进行训练和评估
-
选择backbone:训练支持 yolox-darknet53 以及 yolox-x, 在训练之前需要指定backbone的名称,比如在default_config.yaml文件指定backbone为 "yolox_darknet53"或者"yolox_x",你也可以在命令行手动指定backbone的名称,如
python train.py --backbone="yolox_x"
-
训练分为前70轮和后10轮,区别主要在于后10轮的训练关闭了数据增强以及使用了L1 loss,若您不打算训练完80轮便打算终止,请将default_config.yaml文件中的total_epoch调小。
-
在本地进行训练
# 单卡训练 python train.py data_path=$DATASET_PATH data_aug=True is_distributed=0 eval_interval=10 load_path=yolox_x.ckpt backbone=yolox_x
# 通过shell脚本进行8卡训练 bash run_distribute_train.sh xxx/dataset/ yolox-x pretrained/yolox_x.ckpt
-
在本地进行评估
python eval.py --data_dir=./dataset/xxx --val_ckpt=your_val_ckpt_file_path --backbone=yolox-x --eval_batch_size=1
|----README_CN.md
|----ascend310_infer
| |----build.sh
| |----CMakeLists.txt
| |----inc
| | |----utils.h
| |----src
| | |----main.cc
| | |----utils.cc
|----model_utils
| |----__init__.py
| |----config.py
| |----device_adapter.py
| |----local_adapter.py
| |----moxing_adapter.py
|----scripts
| |----run_distribute_train.sh
| |----run_infer_310.sh
| |----run_eval.sh
| |----run_standalone_train.sh
|----data
| |----__init__.py
| |----mosaicdetection.py
| |----mot.py
| |----transform.py
| |----mot.py
| |----yolox_dataset.py
| |----data_augment.py
|----model
| |----__init__.py
| |----boxes.py
| |----darknet.py
| |----network_blocks.py
| |----yolox.py
| |----yolo_fpn.py
| |----yolo_pafpn.py
|----tracker
| |----basetrack.py
| |----byte_tracker.py
| |----kalman_filter.py
| |----matching.py
|----utils
| |----initializer.py
| |----logger.py
| |----util.py
| |----__init__.py
|----train.py
|----eval.py
|----export.py
|----postprocess.py
|----preprocess.py
|----default_config.yaml
train.py中主要的参数如下:
--backbone 训练的主干网络,默认为yolox_darknet53,你也可以设置为yolox_x
--data_aug 是否开启数据增强,默认为True,在前面的训练轮次是开启的,最后的训练轮次关闭
--device_target
实现代码的设备,默认为'Ascend'
--outputs_dir 训练信息的保存文件目录
--save_graphs 是否保存图文件,默认为False
--max_epoch 开启数据增强的训练轮次,默认为70
--total_epoch 总的训练轮次,默认为80
--no_aug_epochs 不开启数据增强,默认为10
--data_dir 数据集的目录
--need_profiler
是否使用profiler。 0表示否,1表示是。 默认值:0
--per_batch_size 训练的批处理大小。 默认值:4
--max_gt 图片中gt的最大数量,默认值:1000
--num_classes 数据集中类别的个数,默认值:1
--input_size 输入网络的尺度大小,默认值:[800, 1440]
--fpn_strides fpn缩放的步幅,默认:[8, 16, 32]
--use_l1 是否使用L1 loss,默认为False
--use_syc_bn 是否开启同步BN,默认True
--n_candidate_k 动态k中候选iou的个数,默认为10
--lr 学习率,默认为0.01
--min_lr_ratio 学习率衰减比率,默认为0.05
--warmup_epochs warm up 轮次,默认为2
--weight_decay 权重衰减,默认为0.0005
--momentum 动量默认为0.9
--log_interval 日志记录间隔步数,默认为30
--ckpt_interval 保存checkpoint间隔。 默认值:-1
--is_save_on_master 在master或all rank上保存ckpt,1代表master,0代表all ranks。 默认值:1
--is_distributed 是否分发训练,1代表是,0代表否。 默认值:1
--rank 分布式本地进程序号。 默认值:0
--group_size 设备进程总数。 默认值:1
--run_eval 是否开启边训练边推理。默认为False
--device_num 采用8卡训练模式,默认为8
由于 ByteTrack采用了YOLOX网络,而YOLOX网络使用了强大的数据增强,在ImageNet上的预训练模型参数不再重要,因此所有的训练都将从头开始训练。训练分为两步:第一步是从头训练并开启数据增强,第二步是使用第一步训练好的检查点文件作为预训练模型并关闭数据增强训练。
在Ascend设备上,使用python脚本直接开始训练(单卡)
python命令启动
```shell
# 单卡训练
python train.py data_path=$DATASET_PATH data_aug=True is_distributed=0 eval_interval=10 load_path=yolox_x.ckpt backbone=yolox_x
```
shell脚本启动
```shell
bash run_standalone_train.sh [DATASET_PATH] [BACKBONE] [PRETRAINED_CKPT]
```
第一步训练结束后,在默认文件夹中找到最后一个轮次保存的检查点文件,并且将文件路径作为第二步训练的参数输入,如下所示:
在Ascend设备上,使用shell脚本执行分布式训练示例(8卡)
-
第一步
# 通过shell脚本进行8卡训练 bash run_distribute_train.sh xxx/dataset/ yolox-x pretrained/yolox_x.ckpt
上述shell脚本将在后台运行分布式训练。 您可以通过train/log.txt文件查看结果。 得到如下损失值:
... 2022-09-07 18:13:19,017:INFO:epoch: 7 step: [30/766], loss: 4.2296, overflow: False, scale: 4096, lr: 0.039906, avg step time: 3399.11 ms 2022-09-07 18:15:02,108:INFO:epoch: 7 step: [60/766], loss: 5.4202, overflow: False, scale: 4096, lr: 0.039902, avg step time: 3436.35 ms 2022-09-07 18:16:43,538:INFO:epoch: 7 step: [90/766], loss: 5.1512, overflow: False, scale: 4096, lr: 0.039899, avg step time: 3380.99 ms 2022-09-07 18:18:24,509:INFO:epoch: 7 step: [120/766], loss: 4.0267, overflow: False, scale: 4096, lr: 0.039895, avg step time: 3365.65 ms 2022-09-07 18:20:05,368:INFO:epoch: 7 step: [150/766], loss: 5.5103, overflow: False, scale: 4096, lr: 0.039891, avg step time: 3361.93 ms 2022-09-07 18:21:45,653:INFO:epoch: 7 step: [180/766], loss: 4.6702, overflow: False, scale: 4096, lr: 0.039887, avg step time: 3342.81 ms 2022-09-07 18:23:26,179:INFO:epoch: 7 step: [210/766], loss: 5.4270, overflow: False, scale: 4096, lr: 0.039883, avg step time: 3350.84 ms 2022-09-07 18:25:06,799:INFO:epoch: 7 step: [240/766], loss: 4.5086, overflow: False, scale: 4096, lr: 0.039879, avg step time: 3354.00 ms 2022-09-07 18:26:46,874:INFO:epoch: 7 step: [270/766], loss: 5.4404, overflow: False, scale: 4096, lr: 0.039875, avg step time: 3335.79 ms 2022-09-07 18:28:27,289:INFO:epoch: 7 step: [300/766], loss: 5.4157, overflow: False, scale: 4096, lr: 0.039871, avg step time: 3347.13 ms 2022-09-07 18:30:07,555:INFO:epoch: 7 step: [330/766], loss: 5.7149, overflow: False, scale: 4096, lr: 0.039867, avg step time: 3342.19 ms 2022-09-07 18:31:47,641:INFO:epoch: 7 step: [360/766], loss: 4.8638, overflow: False, scale: 4096, lr: 0.039862, avg step time: 3336.18 ms 2022-09-07 18:33:27,448:INFO:epoch: 7 step: [390/766], loss: 5.0072, overflow: False, scale: 4096, lr: 0.039858, avg step time: 3326.85 ms 2022-09-07 18:35:07,407:INFO:epoch: 7 step: [420/766], loss: 4.9238, overflow: False, scale: 4096, lr: 0.039853, avg step time: 3331.93 ms 2022-09-07 18:36:46,999:INFO:epoch: 7 step: [450/766], loss: 4.9315, overflow: False, scale: 4096, lr: 0.039849, avg step time: 3319.70 ms 2022-09-07 18:38:26,593:INFO:epoch: 7 step: [480/766], loss: 5.0438, overflow: False, scale: 4096, lr: 0.039844, avg step time: 3319.78 ms 2022-09-07 18:40:06,158:INFO:epoch: 7 step: [510/766], loss: 5.0004, overflow: False, scale: 4096, lr: 0.039840, avg step time: 3318.81 ms 2022-09-07 18:41:45,116:INFO:epoch: 7 step: [540/766], loss: 4.2428, overflow: False, scale: 4096, lr: 0.039835, avg step time: 3298.57 ms 2022-09-07 18:43:24,390:INFO:epoch: 7 step: [570/766], loss: 5.2726, overflow: False, scale: 4096, lr: 0.039830, avg step time: 3309.13 ms 2022-09-07 18:45:03,704:INFO:epoch: 7 step: [600/766], loss: 5.5637, overflow: False, scale: 4096, lr: 0.039825, avg step time: 3310.44 ms 2022-09-07 18:46:45,732:INFO:epoch: 7 step: [630/766], loss: 5.0450, overflow: False, scale: 4096, lr: 0.039820, avg step time: 3400.88 ms 2022-09-07 18:48:25,264:INFO:epoch: 7 step: [660/766], loss: 4.8747, overflow: False, scale: 4096, lr: 0.039815, avg step time: 3317.73 ms 2022-09-07 18:50:05,139:INFO:epoch: 7 step: [690/766], loss: 4.3879, overflow: False, scale: 4096, lr: 0.039810, avg step time: 3329.14 ms 2022-09-07 18:51:44,589:INFO:epoch: 7 step: [720/766], loss: 3.9797, overflow: False, scale: 4096, lr: 0.039805, avg step time: 3314.99 ms 2022-09-07 18:53:26,479:INFO:epoch: 7 step: [750/766], loss: 4.2583, overflow: False, scale: 4096, lr: 0.039800, avg step time: 3396.30 ms 2022-09-07 18:54:35,593:INFO:epoch: 7 epoch time 2578.55s loss: 4.1540, overflow: False, scale: 4096 ...
python eval.py --data_dir=./dataset/xxx --val_ckpt=your_val_ckpt_file_path --per_batch_size=8 --backbone=yolox_x
backbone参数指定为yolox_darknet53或者yolox_x,上述python命令将在后台运行。 您可以通过%Y-%m-%d_time_%H_%M_%S.log
文件查看结果。
bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [BACKBONE] [BATCH_SIZE]
===============================coco eval result===============================
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.598
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.866
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.688
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.515
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.042
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.318
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.648
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.212
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.583
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.755
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP IDt IDa IDm num _ objects
MOT17-04-SDP 91.6% 93.1% 90.2% 93.9% 96.9% 69 61 6 2 734 1478 14 55 90.8% 0.135 7 9 3 24178
MOT17-11-SDP 69.2% 73.3% 65.5% 79.4% 88.9% 44 21 11 12 449 931 14 26 69.1% 0.140 6 11 3 4517
MOT17-02-FRCNN 51.4% 59.4% 45.3% 63.6% 83.3% 53 19 25 9 1255 3600 85 198 50.0% 0.204 66 20 8 9880
MOT17-02-DPM 51.4% 59.4% 45.3% 63.6% 83.3% 53 19 25 9 1255 3600 85 198 50.0% 0.204 66 20 8 9880
MOT17-09-DPM 75.5% 82.0% 69.9% 83.6% 98.1% 22 16 5 1 47 471 15 27 81.5% 0.160 19 2 6 2879
MOT17-05-SDP 72.1% 80.4% 65.4% 77.9% 95.9% 71 32 30 9 113 741 21 46 73.9% 0.182 32 7 19 3357
MOT17-11-FRCNN 69.2% 73.3% 65.5% 79.4% 88.9% 44 21 11 12 449 931 14 26 69.1% 0.140 6 11 3 4517
MOT17-13-FRCNN 72.0% 90.5% 59.7% 63.8% 96.7% 44 20 13 11 68 1142 8 26 61.4% 0.249 8 5 5 3156
MOT17-02-SDP 51.4% 59.4% 45.3% 63.6% 83.3% 53 19 25 9 1255 3600 85 198 50.0% 0.204 66 20 8 9880
MOT17-05-DPM 72.1% 80.4% 65.4% 77.9% 95.9% 71 32 30 9 113 741 21 46 73.9% 0.182 32 7 19 3357
MOT17-04-FRCNN 91.6% 93.1% 90.2% 93.9% 96.9% 69 61 6 2 734 1478 14 55 90.8% 0.135 7 9 3 24178
MOT17-10-SDP 69.9% 78.2% 63.2% 75.7% 93.7% 36 14 20 2 300 1437 29 93 70.2% 0.222 17 15 5 5923
MOT17-13-SDP 72.0% 90.5% 59.7% 63.8% 96.7% 44 20 13 11 68 1142 8 26 61.4% 0.249 8 5 5 3156
MOT17-09-FRCNN 75.5% 82.0% 69.9% 83.6% 98.1% 22 16 5 1 47 471 15 27 81.5% 0.160 19 2 6 2879
MOT17-05-FRCNN 72.2% 80.8% 65.3% 77.6% 96.0% 71 32 29 10 108 753 23 46 73.7% 0.182 28 9 15 3357
MOT17-11-DPM 69.2% 73.3% 65.5% 79.4% 88.9% 44 21 11 12 449 931 14 26 69.1% 0.140 6 11 3 4517
MOT17-09-SDP 75.5% 82.0% 69.9% 83.6% 98.1% 22 16 5 1 47 471 15 27 81.5% 0.160 19 2 6 2879
MOT17-13-DPM 72.0% 90.5% 59.7% 63.8% 96.7% 44 20 13 11 68 1142 8 26 61.4% 0.249 8 5 5 3156
MOT17-10-FRCNN 69.9% 78.2% 63.2% 75.7% 93.7% 36 14 20 2 300 1437 29 93 70.2% 0.222 17 15 5 5923
MOT17-10-DPM 69.9% 78.2% 63.2% 75.7% 93.7% 36 14 20 2 300 1437 29 93 70.2% 0.222 17 15 5 5923
MOT17-04-DPM 91.6% 93.1% 90.2% 93.9% 96.9% 69 61 6 2 734 1478 14 55 90.8% 0.135 7 9 3 24178
0VERALL 77.4% 83.1% 72.5% 81.8% 93.7% 1017 549 329 139 8893 29412 560 1413 76.0% 0.163 461 209 143 161670
python export.py --backbone [backbone] --val_ckpt [CKPT_PATH] --file_format [MINDIR/AIR]
参数backbone
用于指定主干网络,你可以选择 yolox_darknet53 或者是 yolox_x ,val_ckpt
用于导出的模型文件
- 首先要通过执行export.py导出mindir文件,同理可在配置文件中制定默认backbone的类型
- 通过preprocess.py将数据集转为二进制文件
- 执行postprocess.py将根据mindir网络输出结果进行推理,并保存评估指标等结果
执行完整的推理脚本如下:
# Ascend310 推理
bash run_infer_310.sh [MINDIR_PATH] [DATA_DIR] [DEVICE_ID]
推理结果保存在当前路径,通过cat acc.log中看到最终精度结果。
yolox-x
=============coco eval result=========
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.594
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.888
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.674
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.203
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.524
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.690
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.040
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.314
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.645
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.322
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.592
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.730
2022-10-29 11:10:45,140:INFO:GT Type: _val_half
2022-10-29 11:10:45,142:INFO:GT Files: ['/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-09-FRCNN/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-10-FRCNN/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-02-FRCNN/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-13-DPM/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-05-DPM/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-04-FRCNN/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-05-SDP/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-09-SDP/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-04-DPM/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-10-DPM/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-10-SDP/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-13-SDP/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-11-FRCNN/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-02-DPM/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-04-SDP/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-13-FRCNN/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-05-FRCNN/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-11-SDP/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-02-SDP/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-09-DPM/gt/gt_val_half.txt', '/home/stu/pl/ByteTrackMot-dev/bytetrackmot/mot/train/MOT17-11-DPM/gt/gt_val_half.txt']
2022-10-29 11:10:45,143:INFO:Found 21 groundtruths and 21 test files.
2022-10-29 11:10:45,143:INFO:Available LAP solvers ['lap', 'scipy']
2022-10-29 11:10:45,144:INFO:Default LAP solver 'lap'
2022-10-29 11:10:45,144:INFO:Loading files.
2022-10-29 11:10:53,097:INFO:Comparing MOT17-02-DPM...
2022-10-29 11:10:53,763:INFO:Comparing MOT17-02-FRCNN...
2022-10-29 11:10:54,414:INFO:Comparing MOT17-02-SDP...
2022-10-29 11:10:56,108:INFO:Comparing MOT17-04-DPM...
2022-10-29 11:10:57,573:INFO:Comparing MOT17-04-FRCNN...
2022-10-29 11:10:59,098:INFO:Comparing MOT17-04-SDP...
2022-10-29 11:11:01,585:INFO:Comparing MOT17-05-DPM...
2022-10-29 11:11:02,139:INFO:Comparing MOT17-05-FRCNN...
2022-10-29 11:11:02,698:INFO:Comparing MOT17-05-SDP...
2022-10-29 11:11:03,303:INFO:Comparing MOT17-09-DPM...
2022-10-29 11:11:03,734:INFO:Comparing MOT17-09-FRCNN...
2022-10-29 11:11:04,125:INFO:Comparing MOT17-09-SDP...
2022-10-29 11:11:04,508:INFO:Comparing MOT17-10-DPM...
2022-10-29 11:11:05,046:INFO:Comparing MOT17-10-FRCNN...
2022-10-29 11:11:05,587:INFO:Comparing MOT17-10-SDP...
2022-10-29 11:11:06,145:INFO:Comparing MOT17-11-DPM...
2022-10-29 11:11:06,789:INFO:Comparing MOT17-11-FRCNN...
2022-10-29 11:11:07,424:INFO:Comparing MOT17-11-SDP...
2022-10-29 11:11:08,052:INFO:Comparing MOT17-13-DPM...
2022-10-29 11:11:08,531:INFO:Comparing MOT17-13-FRCNN...
2022-10-29 11:11:09,009:INFO:Comparing MOT17-13-SDP...
2022-10-29 11:11:09,482:INFO:Running metrics
Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP num_objects
MOT17-02-DPM 66.0% 81.9% 53 32.1% 52.8% 15.1% 14.6% 34.0% 1.0% 2.1% 50.3% 0.205 9880
MOT17-02-FRCNN 66.0% 81.9% 53 32.1% 52.8% 15.1% 14.6% 34.0% 1.0% 2.1% 50.3% 0.205 9880
MOT17-02-SDP 66.0% 81.9% 53 32.1% 52.8% 15.1% 14.6% 34.0% 1.0% 2.1% 50.3% 0.205 9880
MOT17-04-DPM 92.9% 97.7% 69 85.5% 11.6% 2.9% 2.2% 7.1% 0.1% 0.3% 90.6% 0.141 24178
MOT17-04-FRCNN 92.9% 97.7% 69 85.5% 11.6% 2.9% 2.2% 7.1% 0.1% 0.3% 90.6% 0.141 24178
MOT17-04-SDP 92.9% 97.7% 69 85.5% 11.6% 2.9% 2.2% 7.1% 0.1% 0.3% 90.6% 0.141 24178
MOT17-05-DPM 77.8% 96.4% 71 43.7% 40.8% 15.5% 2.9% 22.2% 0.6% 1.1% 74.3% 0.186 3357
MOT17-05-FRCNN 77.6% 96.4% 71 43.7% 40.8% 15.5% 2.9% 22.4% 0.7% 1.1% 74.1% 0.186 3357
MOT17-05-SDP 77.8% 96.4% 71 43.7% 40.8% 15.5% 2.9% 22.2% 0.6% 1.1% 74.3% 0.186 3357
MOT17-09-DPM 83.5% 99.1% 22 72.7% 22.7% 4.5% 0.8% 16.5% 0.4% 1.0% 82.4% 0.155 2879
MOT17-09-FRCNN 83.5% 99.1% 22 72.7% 22.7% 4.5% 0.8% 16.5% 0.4% 1.0% 82.4% 0.155 2879
MOT17-09-SDP 83.5% 99.1% 22 72.7% 22.7% 4.5% 0.8% 16.5% 0.4% 1.0% 82.4% 0.155 2879
MOT17-10-DPM 72.5% 96.2% 36 41.7% 47.2% 11.1% 2.8% 27.5% 0.6% 1.4% 69.1% 0.222 5923
MOT17-10-FRCNN 72.5% 96.2% 36 41.7% 47.2% 11.1% 2.8% 27.5% 0.6% 1.4% 69.1% 0.222 5923
MOT17-10-SDP 72.5% 96.2% 36 41.7% 47.2% 11.1% 2.8% 27.5% 0.6% 1.4% 69.1% 0.222 5923
MOT17-11-DPM 80.2% 88.3% 44 50.0% 27.3% 22.7% 10.6% 19.8% 0.4% 0.7% 69.2% 0.143 4517
MOT17-11-FRCNN 80.2% 88.3% 44 50.0% 27.3% 22.7% 10.6% 19.8% 0.4% 0.7% 69.2% 0.143 4517
MOT17-11-SDP 80.2% 88.3% 44 50.0% 27.3% 22.7% 10.6% 19.8% 0.4% 0.7% 69.2% 0.143 4517
MOT17-13-DPM 64.3% 97.0% 44 45.5% 29.5% 25.0% 2.0% 35.7% 0.3% 0.8% 62.0% 0.240 3156
MOT17-13-FRCNN 64.3% 97.0% 44 45.5% 29.5% 25.0% 2.0% 35.7% 0.3% 0.8% 62.0% 0.240 3156
MOT17-13-SDP 64.3% 97.0% 44 45.5% 29.5% 25.0% 2.0% 35.7% 0.3% 0.8% 62.0% 0.240 3156
OVERALL 81.5% 94.0% 1017 53.1% 33.0% 13.9% 5.2% 18.5% 0.4% 0.9% 75.9% 0.167 161670
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP IDt IDa IDm num_objects
MOT17-02-DPM 54.2% 60.7% 48.9% 66.0% 81.9% 53 17 28 8 1444 3363 100 203 50.3% 0.205 72 25 7 9880
MOT17-02-FRCNN 54.2% 60.7% 48.9% 66.0% 81.9% 53 17 28 8 1444 3363 100 203 50.3% 0.205 72 25 7 9880
MOT17-02-SDP 54.2% 60.7% 48.9% 66.0% 81.9% 53 17 28 8 1444 3363 100 203 50.3% 0.205 72 25 7 9880
MOT17-04-DPM 89.5% 91.8% 87.3% 92.9% 97.7% 69 59 8 2 539 1719 24 73 90.6% 0.141 12 14 7 24178
MOT17-04-FRCNN 89.5% 91.8% 87.3% 92.9% 97.7% 69 59 8 2 539 1719 24 73 90.6% 0.141 12 14 7 24178
MOT17-04-SDP 89.5% 91.8% 87.3% 92.9% 97.7% 69 59 8 2 539 1719 24 73 90.6% 0.141 12 14 7 24178
MOT17-05-DPM 72.7% 81.5% 65.7% 77.8% 96.4% 71 31 29 11 97 746 21 37 74.3% 0.186 30 7 17 3357
MOT17-05-FRCNN 72.1% 80.9% 65.1% 77.6% 96.4% 71 31 29 11 96 751 23 37 74.1% 0.186 27 9 14 3357
MOT17-05-SDP 72.7% 81.5% 65.7% 77.8% 96.4% 71 31 29 11 97 746 21 37 74.3% 0.186 30 7 17 3357
MOT17-09-DPM 77.2% 84.4% 71.1% 83.5% 99.1% 22 16 5 1 22 475 11 29 82.4% 0.155 12 2 4 2879
MOT17-09-FRCNN 77.2% 84.4% 71.1% 83.5% 99.1% 22 16 5 1 22 475 11 29 82.4% 0.155 12 2 4 2879
MOT17-09-SDP 77.2% 84.4% 71.1% 83.5% 99.1% 22 16 5 1 22 475 11 29 82.4% 0.155 12 2 4 2879
MOT17-10-DPM 66.1% 76.9% 58.0% 72.5% 96.2% 36 15 17 4 168 1626 38 82 69.1% 0.222 23 16 4 5923
MOT17-10-FRCNN 66.1% 76.9% 58.0% 72.5% 96.2% 36 15 17 4 168 1626 38 82 69.1% 0.222 23 16 4 5923
MOT17-10-SDP 66.1% 76.9% 58.0% 72.5% 96.2% 36 15 17 4 168 1626 38 82 69.1% 0.222 23 16 4 5923
MOT17-11-DPM 68.1% 71.6% 65.0% 80.2% 88.3% 44 22 12 10 478 896 16 32 69.2% 0.143 8 10 2 4517
MOT17-11-FRCNN 68.1% 71.6% 65.0% 80.2% 88.3% 44 22 12 10 478 896 16 32 69.2% 0.143 8 10 2 4517
MOT17-11-SDP 68.1% 71.6% 65.0% 80.2% 88.3% 44 22 12 10 478 896 16 32 69.2% 0.143 8 10 2 4517
MOT17-13-DPM 71.1% 89.2% 59.1% 64.3% 97.0% 44 20 13 11 63 1128 8 26 62.0% 0.240 4 6 3 3156
MOT17-13-FRCNN 71.1% 89.2% 59.1% 64.3% 97.0% 44 20 13 11 63 1128 8 26 62.0% 0.240 4 6 3 3156
MOT17-13-SDP 71.1% 89.2% 59.1% 64.3% 97.0% 44 20 13 11 63 1128 8 26 62.0% 0.240 4 6 3 3156
OVERALL 76.4% 82.2% 71.3% 81.5% 94.0% 1017 540 336 141 8432 29864 656 1446 75.9% 0.167 480 242 129 161670
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@misc{ByteTrack,
title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
author={Liang Peng,JiChen Zhao},
year={2022}
}