69 lines
1.7 KiB
YAML
69 lines
1.7 KiB
YAML
# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT.
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_BASE_: [
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'detector/yolox_x_24e_800x1440_mix_det.yml',
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'_base_/mix_det.yml',
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'_base_/yolox_mot_reader_800x1440.yml'
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]
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weights: output/bytetrack_yolox/model_final
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log_iter: 20
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snapshot_epoch: 2
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metric: MOT # eval/infer mode
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num_classes: 1
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architecture: ByteTrack
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pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams
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ByteTrack:
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detector: YOLOX
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reid: None
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tracker: JDETracker
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det_weights: https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_mix_det.pdparams
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reid_weights: None
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depth_mult: 1.33
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width_mult: 1.25
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YOLOX:
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backbone: CSPDarkNet
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neck: YOLOCSPPAN
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head: YOLOXHead
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input_size: [800, 1440]
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size_stride: 32
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size_range: [18, 22] # multi-scale range [576*1024 ~ 800*1440], w/h ratio=1.8
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CSPDarkNet:
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arch: "X"
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return_idx: [2, 3, 4]
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depthwise: False
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YOLOCSPPAN:
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depthwise: False
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# Tracking requires higher quality boxes, so NMS score_threshold will be higher
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YOLOXHead:
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l1_epoch: 20
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depthwise: False
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loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0}
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assigner:
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name: SimOTAAssigner
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candidate_topk: 10
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use_vfl: False
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nms:
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name: MultiClassNMS
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nms_top_k: 1000
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keep_top_k: 100
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score_threshold: 0.01
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nms_threshold: 0.7
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# 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%.
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# 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.
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# BYTETracker
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JDETracker:
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use_byte: True
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match_thres: 0.9
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conf_thres: 0.6
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low_conf_thres: 0.2
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min_box_area: 100
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vertical_ratio: 1.6 # for pedestrian
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