diff --git a/configs/mot/bytetrack/README_cn.md b/configs/mot/bytetrack/README_cn.md index 477e72e6822da3336e71ab7e9d5adf21e5c39f3f..56f72e0b168f64ffb6646c754de5272ab6a40c09 100644 --- a/configs/mot/bytetrack/README_cn.md +++ b/configs/mot/bytetrack/README_cn.md @@ -20,9 +20,12 @@ | MOT-17 half train | YOLOv3 | 608x608 | - | 42.7 | 49.5 | 54.8 | - |[配置文件](./bytetrack_yolov3.yml) | | MOT-17 half train | PPYOLOe | 640x640 | - | 52.9 | 50.4 | 59.7 | - |[配置文件](./bytetrack_ppyoloe.yml) | | MOT-17 half train | PPYOLOe | 640x640 |PPLCNet| 52.9 | 51.7 | 58.8 | - |[配置文件](./bytetrack_ppyoloe_pplcnet.yml) | +| mix_det | YOLOX-x | 800x1440| - | 61.9 | 77.3 | 71.6 | - |[配置文件](./bytetrack_yolox.yml) | **注意:** - 模型权重下载链接在配置文件中的```det_weights```和```reid_weights```，运行验证的命令即可自动下载。 +- **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://dataset.bj.bcebos.com/mot/MOT17.zip)下载，并解压放在`dataset/mot/`文件夹下。 +- **mix_det**是MOT17、crowdhuman、Cityscapes、ETHZ组成的联合数据集，数据集整理的格式和目录可以参考[此链接](https://github.com/ifzhang/ByteTrack#data-preparation)，最终放置于`dataset/mot/`目录下。为了验证精度可以都用**MOT17-half val**数据集去评估。 - ByteTrack的训练是单独的检测器训练MOT数据集，推理是组装跟踪器去评估MOT指标，单独的检测模型也可以评估检测指标。 - ByteTrack的导出部署，是单独导出检测模型，再组装跟踪器运行的，参照[PP-Tracking](../../../deploy/pptracking/python/README.md)。 diff --git a/configs/mot/bytetrack/_base_/ht21.yml b/configs/mot/bytetrack/_base_/ht21.yml new file mode 100644 index 0000000000000000000000000000000000000000..8500af3165e1173cc442396ace1af54f09ab810a --- /dev/null +++ b/configs/mot/bytetrack/_base_/ht21.yml @@ -0,0 +1,34 @@ +metric: COCO +num_classes: 1 + +# Detection Dataset for training +TrainDataset: + !COCODataSet + image_dir: images/train + anno_path: annotations/train.json + dataset_dir: dataset/mot/HT21 + data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] + +EvalDataset: + !COCODataSet + image_dir: images/train + anno_path: annotations/val_half.json + dataset_dir: dataset/mot/HT21 + +TestDataset: + !ImageFolder + dataset_dir: dataset/mot/HT21 + anno_path: annotations/val_half.json + + +# MOTDataset for MOT evaluation and inference +EvalMOTDataset: + !MOTImageFolder + dataset_dir: dataset/mot + data_root: HT21/images/test + keep_ori_im: True # set as True in DeepSORT and ByteTrack + +TestMOTDataset: + !MOTImageFolder + dataset_dir: dataset/mot + keep_ori_im: True # set True if save visualization images or video diff --git a/configs/mot/bytetrack/_base_/mix_det.yml b/configs/mot/bytetrack/_base_/mix_det.yml new file mode 100644 index 0000000000000000000000000000000000000000..157866824d72596c96014df278f3044c8430f04e --- /dev/null +++ b/configs/mot/bytetrack/_base_/mix_det.yml @@ -0,0 +1,34 @@ +metric: COCO +num_classes: 1 + +# Detection Dataset for training +TrainDataset: + !COCODataSet + image_dir: "" + anno_path: annotations/train.json + dataset_dir: dataset/mot/mix_det + data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] + +EvalDataset: + !COCODataSet + image_dir: train + anno_path: annotations/val_half.json + dataset_dir: dataset/mot/MOT17 + +TestDataset: + !ImageFolder + anno_path: annotations/val_half.json + dataset_dir: dataset/mot/MOT17 + + +# MOTDataset for MOT evaluation and inference +EvalMOTDataset: + !MOTImageFolder + dataset_dir: dataset/mot + data_root: MOT17/images/half + keep_ori_im: True # set as True in DeepSORT and ByteTrack + +TestMOTDataset: + !MOTImageFolder + dataset_dir: dataset/mot + keep_ori_im: True # set True if save visualization images or video diff --git a/configs/mot/bytetrack/_base_/yolox_mot_reader_800x1440.yml b/configs/mot/bytetrack/_base_/yolox_mot_reader_800x1440.yml new file mode 100644 index 0000000000000000000000000000000000000000..48d4144221f6fa353af90ce3781a21329a566751 --- /dev/null +++ b/configs/mot/bytetrack/_base_/yolox_mot_reader_800x1440.yml @@ -0,0 +1,67 @@ + +input_height: &input_height 800 +input_width: &input_width 1440 +input_size: &input_size [*input_height, *input_width] + +worker_num: 4 +TrainReader: + sample_transforms: + - Decode: {} + - Mosaic: + prob: 1.0 + input_dim: *input_size + degrees: [-10, 10] + scale: [0.1, 2.0] + shear: [-2, 2] + translate: [-0.1, 0.1] + enable_mixup: True + mixup_prob: 1.0 + mixup_scale: [0.5, 1.5] + - AugmentHSV: {is_bgr: False, hgain: 5, sgain: 30, vgain: 30} + - PadResize: {target_size: *input_size} + - RandomFlip: {} + batch_transforms: + - Permute: {} + batch_size: 6 + shuffle: True + drop_last: True + collate_batch: False + mosaic_epoch: 20 + +EvalReader: + sample_transforms: + - Decode: {} + - Resize: {target_size: *input_size, keep_ratio: True} + - Pad: {size: *input_size, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 8 + +TestReader: + inputs_def: + image_shape: [3, 800, 1440] + sample_transforms: + - Decode: {} + - Resize: {target_size: *input_size, keep_ratio: True} + - Pad: {size: *input_size, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 1 + + +# add MOTReader for MOT evaluation and inference, note batch_size should be 1 in MOT +EvalMOTReader: + sample_transforms: + - Decode: {} + - Resize: {target_size: *input_size, keep_ratio: True} + - Pad: {size: *input_size, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 1 + +TestMOTReader: + inputs_def: + image_shape: [3, 800, 1440] + sample_transforms: + - Decode: {} + - Resize: {target_size: *input_size, keep_ratio: True} + - Pad: {size: *input_size, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 1 diff --git a/configs/mot/bytetrack/bytetrack_yolox.yml b/configs/mot/bytetrack/bytetrack_yolox.yml new file mode 100644 index 0000000000000000000000000000000000000000..2e195c56d00cfc696e93fee4e9f709f123b5dcec --- /dev/null +++ b/configs/mot/bytetrack/bytetrack_yolox.yml @@ -0,0 +1,68 @@ +# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT. +_BASE_: [ + 'detector/yolox_x_24e_800x1440_mix_det.yml', + '_base_/mix_det.yml', + '_base_/yolox_mot_reader_800x1440.yml' +] +weights: output/bytetrack_yolox/model_final +log_iter: 20 +snapshot_epoch: 2 + +metric: MOT # eval/infer mode +num_classes: 1 + +architecture: ByteTrack +pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams +ByteTrack: + detector: YOLOX + reid: None + tracker: JDETracker +det_weights: https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_mix_det.pdparams +reid_weights: None + +depth_mult: 1.33 +width_mult: 1.25 + +YOLOX: + backbone: CSPDarkNet + neck: YOLOCSPPAN + head: YOLOXHead + input_size: [800, 1440] + size_stride: 32 + size_range: [18, 22] # multi-scale range [576*1024 ~ 800*1440], w/h ratio=1.8 + +CSPDarkNet: + arch: "X" + return_idx: [2, 3, 4] + depthwise: False + +YOLOCSPPAN: + depthwise: False + +# Tracking requires higher quality boxes, so NMS score_threshold will be higher +YOLOXHead: + l1_epoch: 20 + depthwise: False + loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0} + assigner: + name: SimOTAAssigner + candidate_topk: 10 + use_vfl: False + nms: + name: MultiClassNMS + nms_top_k: 1000 + keep_top_k: 100 + score_threshold: 0.01 + nms_threshold: 0.7 + # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%. + # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot. + + +# BYTETracker +JDETracker: + use_byte: True + match_thres: 0.9 + conf_thres: 0.6 + low_conf_thres: 0.2 + min_box_area: 100 + vertical_ratio: 1.6 # for pedestrian diff --git a/configs/mot/bytetrack/bytetrack_yolox_ht21.yml b/configs/mot/bytetrack/bytetrack_yolox_ht21.yml new file mode 100644 index 0000000000000000000000000000000000000000..d79ed69421d36899d33a2b226708a5e44b1b35e9 --- /dev/null +++ b/configs/mot/bytetrack/bytetrack_yolox_ht21.yml @@ -0,0 +1,68 @@ +# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT. +_BASE_: [ + 'detector/yolox_x_24e_800x1440_ht21.yml', + '_base_/ht21.yml', + '_base_/yolox_mot_reader_800x1440.yml' +] +weights: output/bytetrack_yolox_ht21/model_final +log_iter: 20 +snapshot_epoch: 2 + +metric: MOT # eval/infer mode +num_classes: 1 + +architecture: ByteTrack +pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams +ByteTrack: + detector: YOLOX + reid: None + tracker: JDETracker +det_weights: https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_ht21.pdparams +reid_weights: None + +depth_mult: 1.33 +width_mult: 1.25 + +YOLOX: + backbone: CSPDarkNet + neck: YOLOCSPPAN + head: YOLOXHead + input_size: [800, 1440] + size_stride: 32 + size_range: [18, 22] # multi-scale range [576*1024 ~ 800*1440], w/h ratio=1.8 + +CSPDarkNet: + arch: "X" + return_idx: [2, 3, 4] + depthwise: False + +YOLOCSPPAN: + depthwise: False + +# Tracking requires higher quality boxes, so NMS score_threshold will be higher +YOLOXHead: + l1_epoch: 20 + depthwise: False + loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0} + assigner: + name: SimOTAAssigner + candidate_topk: 10 + use_vfl: False + nms: + name: MultiClassNMS + nms_top_k: 1000 + keep_top_k: 100 + score_threshold: 0.01 + nms_threshold: 0.7 + # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%. + # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot. + + +# BYTETracker +JDETracker: + use_byte: True + match_thres: 0.9 + conf_thres: 0.6 + low_conf_thres: 0.2 + min_box_area: 0 + vertical_ratio: 0 # 1.6 for pedestrian diff --git a/configs/mot/bytetrack/detector/README_cn.md b/configs/mot/bytetrack/detector/README_cn.md index 7bdb095f177fa9365955649841a0a27eda571d7e..8b47596a716cd2b398c18171245d01e09e415bc1 100644 --- a/configs/mot/bytetrack/detector/README_cn.md +++ b/configs/mot/bytetrack/detector/README_cn.md @@ -12,10 +12,12 @@ | :-------------- | :------------- | :--------: | :---------: | :-----------: | :-----: | :------: | :-----: | | DarkNet-53 | YOLOv3 | 608X608 | 40e | ---- | 42.7 | [下载链接](https://paddledet.bj.bcebos.com/models/mot/deepsort/yolov3_darknet53_40e_608x608_mot17half.pdparams) | [配置文件](./yolov3_darknet53_40e_608x608_mot17half.yml) | | CSPResNet | PPYOLOe | 640x640 | 36e | ---- | 52.9 | [下载链接](https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyoloe_crn_l_36e_640x640_mot17half.pdparams) | [配置文件](./ppyoloe_crn_l_36e_640x640_mot17half.yml) | +| CSPDarkNet | YOLOX-x | 800x1440 | 24e | ---- | 61.9 | [下载链接](https://paddledet.bj.bcebos.com/models/mot/deepsort/yolox_x_24e_800x1440_mix_det.pdparams) | [配置文件](./yolox_x_24e_800x1440_mix_det.yml) | **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。 + - 以上模型除YOLOX外采用**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。 +- YOLOX采用**mix_det**数据集，是MOT17、crowdhuman、Cityscapes、ETHZ组成的联合数据集，数据集整理的格式和目录可以参考[此链接](https://github.com/ifzhang/ByteTrack#data-preparation)，最终放置于`dataset/mot/`目录下。为了验证精度可以都用**MOT17-half val**数据集去评估。 - 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ByteTrack跟踪时，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/mot/bytetrack/detector/yolox_x_24e_800x1440_ht21.yml b/configs/mot/bytetrack/detector/yolox_x_24e_800x1440_ht21.yml new file mode 100644 index 0000000000000000000000000000000000000000..bd102a48d1013b9e6399411562b47e1e85e2c2ec --- /dev/null +++ b/configs/mot/bytetrack/detector/yolox_x_24e_800x1440_ht21.yml @@ -0,0 +1,80 @@ +# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT. +_BASE_: [ + '../../../yolox/yolox_x_300e_coco.yml', + '../_base_/ht21.yml', +] +weights: output/yolox_x_24e_800x1440_ht21/model_final +log_iter: 20 +snapshot_epoch: 2 + +# schedule configuration for fine-tuning +epoch: 24 +LearningRate: + base_lr: 0.0005 # fintune + schedulers: + - !CosineDecay + max_epochs: 24 + min_lr_ratio: 0.05 + last_plateau_epochs: 4 + - !ExpWarmup + epochs: 1 + +OptimizerBuilder: + optimizer: + type: Momentum + momentum: 0.9 + use_nesterov: True + regularizer: + factor: 0.0005 + type: L2 + + +TrainReader: + batch_size: 4 + mosaic_epoch: 20 + +# detector configuration +architecture: YOLOX +pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams +norm_type: sync_bn +use_ema: True +ema_decay: 0.9999 +ema_decay_type: "exponential" +act: silu +find_unused_parameters: True +depth_mult: 1.33 +width_mult: 1.25 + +YOLOX: + backbone: CSPDarkNet + neck: YOLOCSPPAN + head: YOLOXHead + input_size: [800, 1440] + size_stride: 32 + size_range: [18, 32] # multi-scale range [576*1024 ~ 800*1440], w/h ratio=1.8 + +CSPDarkNet: + arch: "X" + return_idx: [2, 3, 4] + depthwise: False + +YOLOCSPPAN: + depthwise: False + +# Tracking requires higher quality boxes, so NMS score_threshold will be higher +YOLOXHead: + l1_epoch: 20 + depthwise: False + loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0} + assigner: + name: SimOTAAssigner + candidate_topk: 10 + use_vfl: False + nms: + name: MultiClassNMS + nms_top_k: 1000 + keep_top_k: 100 + score_threshold: 0.01 + nms_threshold: 0.7 + # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%. + # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot. diff --git a/configs/mot/bytetrack/detector/yolox_x_24e_800x1440_mix_det.yml b/configs/mot/bytetrack/detector/yolox_x_24e_800x1440_mix_det.yml new file mode 100644 index 0000000000000000000000000000000000000000..2585e5a47ac0589f7d673803a5172b42f3b902bc --- /dev/null +++ b/configs/mot/bytetrack/detector/yolox_x_24e_800x1440_mix_det.yml @@ -0,0 +1,80 @@ +# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT. +_BASE_: [ + '../../../yolox/yolox_x_300e_coco.yml', + '../_base_/mix_det.yml', +] +weights: output/yolox_x_24e_800x1440_mix_det/model_final +log_iter: 20 +snapshot_epoch: 2 + +# schedule configuration for fine-tuning +epoch: 24 +LearningRate: + base_lr: 0.00075 # fintune + schedulers: + - !CosineDecay + max_epochs: 24 + min_lr_ratio: 0.05 + last_plateau_epochs: 4 + - !ExpWarmup + epochs: 1 + +OptimizerBuilder: + optimizer: + type: Momentum + momentum: 0.9 + use_nesterov: True + regularizer: + factor: 0.0005 + type: L2 + + +TrainReader: + batch_size: 6 + mosaic_epoch: 20 + +# detector configuration +architecture: YOLOX +pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams +norm_type: sync_bn +use_ema: True +ema_decay: 0.9999 +ema_decay_type: "exponential" +act: silu +find_unused_parameters: True +depth_mult: 1.33 +width_mult: 1.25 + +YOLOX: + backbone: CSPDarkNet + neck: YOLOCSPPAN + head: YOLOXHead + input_size: [800, 1440] + size_stride: 32 + size_range: [18, 30] # multi-scale range [576*1024 ~ 800*1440], w/h ratio=1.8 + +CSPDarkNet: + arch: "X" + return_idx: [2, 3, 4] + depthwise: False + +YOLOCSPPAN: + depthwise: False + +# Tracking requires higher quality boxes, so NMS score_threshold will be higher +YOLOXHead: + l1_epoch: 20 + depthwise: False + loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0} + assigner: + name: SimOTAAssigner + candidate_topk: 10 + use_vfl: False + nms: + name: MultiClassNMS + nms_top_k: 1000 + keep_top_k: 100 + score_threshold: 0.01 + nms_threshold: 0.7 + # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%. + # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot. diff --git a/configs/mot/headtracking21/README_cn.md b/configs/mot/headtracking21/README_cn.md index eafd87d7cbae64ea46bd31682687b8c6b7f7df8a..2a08f01782107bbf6ca86910efbb5d4311904224 100644 --- a/configs/mot/headtracking21/README_cn.md +++ b/configs/mot/headtracking21/README_cn.md @@ -11,21 +11,22 @@ ## 模型库 ### FairMOT在HT-21 Training Set上结果 -| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | +| 模型 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :---: | :----: | :---: | :------: | :----: |:----: | -| DLA-34 | 1088x608 | 64.7 | 69.0 | 8533 | 148817 | 234970 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) | -| HRNetv2-W18 | 1088x608 | 57.2 | 58.4 | 30950 | 188260 | 256580 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_1088x608_headtracking21.yml) | - +| FairMOT DLA-34 | 1088x608 | 64.7 | 69.0 | 8533 | 148817 | 234970 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) | +| ByteTrack-x | 1440x800 | 62.2 | 59.9 | 5736 | 222583 | 191737 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/bytetrack_yolox_ht21.pdparams) | [配置文件](../bytetrack/bytetrack_yolox_ht21.yml) | ### FairMOT在HT-21 Test Set上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: |:-------: | :----: | :----: | -| DLA-34 | 1088x608 | 60.8 | 62.8 | 12781 | 118109 | 198896 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) | -| HRNetv2-W18 | 1088x608 | 41.2 | 47.1 | 48809 | 241683 | 204346 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) | +| FairMOT DLA-34 | 1088x608 | 60.8 | 62.8 | 12781 | 118109 | 198896 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) | +| ByteTrack-x | 1440x800 | 72.6 | 61.8 | 5163 | 71235 | 154139 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/bytetrack_yolox_ht21.pdparams) | [配置文件](../bytetrack/bytetrack_yolox_ht21.yml) | **注意:** - - FairMOT DLA-34使用2个GPU进行训练，每个GPU上batch size为6，训练30个epoch。目前MOTA精度位于MOT官网[Head Tracking 21](https://motchallenge.net/results/Head_Tracking_21)榜单榜首。 - - FairMOT HRNetv2-W18使用4个GPU进行训练，每个GPU上batch size为8，训练30个epoch。 + - FairMOT DLA-34使用2个GPU进行训练，每个GPU上batch size为6，训练30个epoch。 + - ByteTrack使用YOLOX-x做检测器，使用8个GPU进行训练，每个GPU上batch size为8，训练30个epoch，具体细节参照[bytetrack](../bytetrack/)。 + - 此处提供PaddleDetection团队整理后的[下载链接](https://bj.bcebos.com/v1/paddledet/data/mot/HT21.zip)，下载后需解压放到`dataset/mot/`目录下，HT-21 Test集的结果需要交到[官网](https://motchallenge.net)评测。 + ## 快速开始