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# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT.
_BASE_: [
'../bytetrack/detector/yolox_x_24e_800x1440_mix_det.yml',
'../bytetrack/_base_/mix_det.yml',
'../bytetrack/_base_/yolox_mot_reader_800x1440.yml'
]
weights: output/ocsort_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: OCSORTTracker
det_weights: https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_mix_mot_ch.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: 0.1
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.
OCSORTTracker:
det_thresh: 0.6
max_age: 30
min_hits: 3
iou_threshold: 0.3
delta_t: 3
inertia: 0.2
vertical_ratio: 1.6
min_box_area: 100
use_byte: False
# 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
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