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Merge pull request #508 from Nuzhny007/master
Add new SOTA: YOLOv26, YOLOv26-obb and YOLOv26-seg models
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README.md

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## Latest Features
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- Add new SOTA: YOLOv26, YOLOv26-obb and YOLOv26-seg models from [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)
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- Add RT-DETRv4 (API similar D-FINE) detection model [RT-DETRs/RT-DETRv4](https://github.com/RT-DETRs/RT-DETRv4)
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- Add D-FINE seg detection model [ArgoHA/D-FINE-seg](https://github.com/ArgoHA/D-FINE-seg)
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- Add ByteTrack MOT algorithm based on [Vertical-Beach/ByteTrack-cpp](https://github.com/Vertical-Beach/ByteTrack-cpp)

data/settings_yolov26m.ini

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[detection]
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#-----------------------------
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# opencv_dnn = 6
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# tensorrt = 5
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detector_backend = 5
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#-----------------------------
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# Target and backend for opencv_dnn detector
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# DNN_TARGET_CPU
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# DNN_TARGET_OPENCL
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# DNN_TARGET_OPENCL_FP16
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# DNN_TARGET_MYRIAD
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# DNN_TARGET_CUDA
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# DNN_TARGET_CUDA_FP16
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ocv_dnn_target = DNN_TARGET_CPU
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# DNN_BACKEND_DEFAULT
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# DNN_BACKEND_HALIDE
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# DNN_BACKEND_INFERENCE_ENGINE
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# DNN_BACKEND_OPENCV
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# DNN_BACKEND_VKCOM
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# DNN_BACKEND_CUDA
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# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
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# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
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ocv_dnn_backend = DNN_BACKEND_INFERENCE_ENGINE
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#-----------------------------
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nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo26m.onnx
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nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo26m.onnx
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class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
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#-----------------------------
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confidence_threshold = 0.5
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max_crop_ratio = 0
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max_batch = 1
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gpu_id = 0
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#-----------------------------
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# YOLOV3
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# YOLOV4
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# YOLOV5
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net_type = YOLOV26
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#-----------------------------
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# INT8
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# FP16
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# FP32
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inference_precision = FP16
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[tracking]
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#-----------------------------
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# DistCenters = 0 // Euclidean distance between centers, pixels
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# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
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# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
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# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
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distance_type = 0
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#-----------------------------
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# KalmanLinear = 0
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# KalmanUnscented = 1
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kalman_type = 0
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#-----------------------------
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# FilterCenter = 0
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# FilterRect = 1
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# FilterRRect = 2
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filter_goal = 0
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#-----------------------------
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# TrackNone = 0
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# TrackKCF = 1
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# TrackMIL = 2
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# TrackMedianFlow = 3
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# TrackGOTURN = 4
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# TrackMOSSE = 5
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# TrackCSRT = 6
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# TrackDAT = 7
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# TrackSTAPLE = 8
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# TrackLDES = 9
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# TrackDaSiamRPN = 10
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# Used if filter_goal == FilterRect
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lost_track_type = 0
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#-----------------------------
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# MatchHungrian = 0
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# MatchBipart = 1
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match_type = 0
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#-----------------------------
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# Use constant acceleration motion model:
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# 0 - unused (stable)
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# 1 - use acceleration in Kalman filter (experimental)
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use_aceleration = 0
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#-----------------------------
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# Delta time for Kalman filter
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delta_time = 0.4
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#-----------------------------
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# Accel noise magnitude for Kalman filter
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accel_noise = 0.2
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#-----------------------------
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# Distance threshold between region and object on two frames
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dist_thresh = 0.8
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#-----------------------------
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# If this value > 0 than will be used circle with this radius
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# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
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min_area_radius_pix = -1
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#-----------------------------
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# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
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min_area_radius_k = 0.8
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#-----------------------------
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# If the object do not assignment more than this seconds then it will be removed
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max_lost_time = 2
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#-----------------------------
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# The maximum trajectory length
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max_trace_len = 2
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#-----------------------------
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# Detection abandoned objects
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detect_abandoned = 0
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# After this time (in seconds) the object is considered abandoned
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min_static_time = 5
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# After this time (in seconds) the abandoned object will be removed
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max_static_time = 25
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# Speed in pixels. If speed of object is more that this value than object is non static
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max_speed_for_static = 10

data/settings_yolov26m_obb.ini

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[detection]
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#-----------------------------
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# opencv_dnn = 6
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# tensorrt = 5
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detector_backend = 5
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#-----------------------------
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# Target and backend for opencv_dnn detector
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# DNN_TARGET_CPU
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# DNN_TARGET_OPENCL
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# DNN_TARGET_OPENCL_FP16
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# DNN_TARGET_MYRIAD
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# DNN_TARGET_CUDA
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# DNN_TARGET_CUDA_FP16
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ocv_dnn_target = DNN_TARGET_CPU
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# DNN_BACKEND_DEFAULT
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# DNN_BACKEND_HALIDE
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# DNN_BACKEND_INFERENCE_ENGINE
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# DNN_BACKEND_OPENCV
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# DNN_BACKEND_VKCOM
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# DNN_BACKEND_CUDA
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# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
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# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
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ocv_dnn_backend = DNN_BACKEND_OPENCV
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#-----------------------------
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nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo26m-obb.onnx
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nn_config = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo26m-obb.onnx
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class_names = C:/work/home/mtracker/Multitarget-tracker/data/dota/DOTA_v1.0.names
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#-----------------------------
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confidence_threshold = 0.5
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max_crop_ratio = 1
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max_batch = 1
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gpu_id = 0
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#-----------------------------
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# YOLOV3
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# YOLOV4
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# YOLOV5
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net_type = YOLOV26_OBB
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#-----------------------------
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# INT8
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# FP16
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# FP32
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inference_precision = FP16
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[tracking]
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#-----------------------------
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# DistCenters = 0 // Euclidean distance between centers, pixels
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# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
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# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
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# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
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distance_type = 0
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#-----------------------------
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# KalmanLinear = 0
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# KalmanUnscented = 1
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kalman_type = 0
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#-----------------------------
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# FilterCenter = 0
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# FilterRect = 1
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# FilterRRect = 2
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filter_goal = 0
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#-----------------------------
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# TrackNone = 0
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# TrackKCF = 1
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# TrackMIL = 2
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# TrackMedianFlow = 3
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# TrackGOTURN = 4
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# TrackMOSSE = 5
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# TrackCSRT = 6
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# TrackDAT = 7
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# TrackSTAPLE = 8
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# TrackLDES = 9
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# TrackDaSiamRPN = 10
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# Used if filter_goal == FilterRect
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lost_track_type = 0
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#-----------------------------
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# MatchHungrian = 0
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# MatchBipart = 1
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match_type = 0
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#-----------------------------
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# Use constant acceleration motion model:
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# 0 - unused (stable)
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# 1 - use acceleration in Kalman filter (experimental)
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use_aceleration = 0
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#-----------------------------
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# Delta time for Kalman filter
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delta_time = 0.4
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#-----------------------------
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# Accel noise magnitude for Kalman filter
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accel_noise = 0.2
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#-----------------------------
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# Distance threshold between region and object on two frames
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dist_thresh = 0.8
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#-----------------------------
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# If this value > 0 than will be used circle with this radius
118+
# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
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min_area_radius_pix = -1
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#-----------------------------
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# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
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min_area_radius_k = 0.8
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#-----------------------------
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# If the object do not assignment more than this seconds then it will be removed
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max_lost_time = 2
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#-----------------------------
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# The maximum trajectory length
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max_trace_len = 2
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#-----------------------------
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# Detection abandoned objects
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detect_abandoned = 0
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# After this time (in seconds) the object is considered abandoned
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min_static_time = 5
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# After this time (in seconds) the abandoned object will be removed
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max_static_time = 25
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# Speed in pixels. If speed of object is more that this value than object is non static
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max_speed_for_static = 10

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