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- # 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
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