diff --git a/ObjectDetection/README.md b/ObjectDetection/README.md
index a8a2c53..360589f 100644
--- a/ObjectDetection/README.md
+++ b/ObjectDetection/README.md
@@ -5,8 +5,10 @@
 ## Создание датасета в формате YoloV4 для заданного объекта
 
 Команда для запуска:  
-    blenderproc run obj2Yolov4dataset.py [obj] [output_dir] [--imgs 1]  
 
+```
+blenderproc run obj2Yolov4dataset.py [obj] [output_dir] [--imgs 1]  
+```
 - obj: файл описания объекта *.obj  
 - output_dir: выходной каталог  
 - --imgs 1: количество изображений на выходе  
@@ -14,7 +16,9 @@
 ## Создание датасета в формате YoloV4 для серии заданных объектов в заданной сцене
 
 Команда для запуска:  
-    blenderproc run objs2Yolov4dataset.py [scene] [obj_path] [output_dir] [vhacd_path] [--imgs 1]
+```
+blenderproc run objs2Yolov4dataset.py [scene] [obj_path] [output_dir] [vhacd_path] [--imgs 1]
+```
 - scene: путь к файлу описания сцены (*.blend)
 - obj_path: путь к каталогу с файлами описания детектируемых объектов *.obj
 - output_dir: выходной каталог
@@ -25,13 +29,77 @@
 
 Должен быть собран пакет [darknet](https://github.com/AlexeyAB/darknet) для работы на заданном ПО и оборудовании (CPU, GPU ...)
 
+---
+
 ## Обучение нейросети и получение файла с её весами
 
 Команда для запуска:  
-  darknet datector train [data] [cfg] [weight]
+```
+  darknet detector train [data] [cfg] [weight]
+```
 - data: файл с описанием датасета (*.data)
 - cfg: файл с описанием нейросети
 - weight: файл весов нейросети  
 
 Для обучения нужно загрузить файл с предобученными весами (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137)  
 Для разного количества детектируемых объектов в выборке нужны свои файлы [data](https://gitlab.com/robossembler/framework/-/blob/master/ObjectDetection/yolov4_objs2.data) и [cfg](https://gitlab.com/robossembler/framework/-/blob/master/ObjectDetection/yolov4_objs2.cfg).
+
+---
+
+## Команда для обнаружения объектов нейросетью с обученными весами
+* вариант 1 (в файле t.txt - список изображений):
+```
+darknet detector test yolov4_objs2.data yolov4_test.cfg yolov4_objs2_final.weights -dont_show -ext_output < t.txt > res.txt
+```
+
+* вариант 2 (файл 000015.jpg - тестовое изображение):
+```
+darknet detector test yolov4_objs2.data yolov4_test.cfg yolov4_objs2_final.weights -dont_show -ext_output 000015.jpg > res.txt
+```
+* вариант 3 (в файле t.txt - список изображений):
+```
+darknet detector test yolov4_objs2.data yolov4_test.cfg yolov4_objs2_final.weights -dont_show -ext_output -out res.json < t.txt
+```
+
+Файл res.txt после запуска варианта 2:
+
+> net.optimized_memory = 0  
+> mini_batch = 1, batch = 1, time_steps = 1, train = 0  
+> Create CUDA-stream - 0  
+>  Create cudnn-handle 0  
+> nms_kind: greedynms (1), beta = 0.600000  
+> nms_kind: greedynms (1), beta = 0.600000  
+> nms_kind: greedynms (1), beta = 0.600000  
+>
+> seen 64, trained: 768 K-images (12 Kilo-batches_64)  
+> Detection layer: 139 - type = 28  
+> Detection layer: 150 - type = 28  
+> Detection layer: 161 - type = 28  
+>000015.jpg: Predicted in 620.357000 milli-seconds. 
+>fork.001: 94%	(left_x:  145   top_y:   -0   width:   38   height:   18)
+>asm_element_edge.001: 28%	(left_x:  195   top_y:  320   width:   40   height:   61)  
+>start_link.001: 87%	(left_x:  197   top_y:  313   width:   39   height:   68)  
+>doking_link.001: 99%	(left_x:  290   top_y:  220   width:   32   height:   21)  
+>start_link.001: 90%	(left_x:  342   top_y:  198   width:   33   height:   34)  
+>doking_link.001: 80%	(left_x:  342   top_y:  198   width:   32   height:   34)  
+>assemb_link.001: 100%	(left_x:  426   top_y:  410   width:   45   height:   61)
+
+
+Файл res.json после запуска варианта 3:  
+>[  
+{  
+ "frame_id":1,  
+ "filename":"img_test/000001.jpg",  
+ "objects": [  
+  {"class_id":5, "name":"asm_element_edge.001", "relative_coordinates":{"center_x":0.498933, "center_y":0.502946, "width":0.083075, "height":0.073736}, "confidence":0.999638},  
+  {"class_id":4, "name":"grip-tool.001", "relative_coordinates":{"center_x":0.858856, "center_y":0.031339, "width":0.043919, "height":0.064563}, "confidence":0.996551}  
+ ]  
+},  
+{  
+ "frame_id":2,  
+ "filename":"img_test/000002.jpg",  
+ "objects": [  
+  {"class_id":1, "name":"start_link.001", "relative_coordinates":{"center_x":0.926026, "center_y":0.728457, "width":0.104029, "height":0.132757}, "confidence":0.995811},  
+  {"class_id":0, "name":"assemb_link.001", "relative_coordinates":{"center_x":0.280403, "center_y":0.129059, "width":0.029980, "height":0.025067}, "confidence":0.916782}  
+ ]  
+} 
diff --git a/ObjectDetection/obj2Yolov4dataset.py b/ObjectDetection/obj2Yolov4dataset.py
index d9960d3..3b0c6b5 100644
--- a/ObjectDetection/obj2Yolov4dataset.py
+++ b/ObjectDetection/obj2Yolov4dataset.py
@@ -6,6 +6,7 @@ import blenderproc as bproc
   Используется модуль blenderproc
 
   24.01.2023 @shalenikol release 0.1
+  22.02.2023 @shalenikol release 0.2 исправлен расчёт x,y в convert2relative
 """
 import numpy as np
 import argparse
@@ -19,6 +20,8 @@ def convert2relative(height, width, bbox):
     YOLO format use relative coordinates for annotation
     """
     x, y, w, h = bbox
+    x += w/2
+    y += h/2
     return x/width, y/height, w/width, h/height
 
 parser = argparse.ArgumentParser()
diff --git a/ObjectDetection/objs2Yolov4dataset.py b/ObjectDetection/objs2Yolov4dataset.py
index d84e839..a7d0023 100644
--- a/ObjectDetection/objs2Yolov4dataset.py
+++ b/ObjectDetection/objs2Yolov4dataset.py
@@ -6,6 +6,7 @@ import blenderproc as bproc
   Используется модуль blenderproc
 
   17.02.2023 @shalenikol release 0.1
+  22.02.2023 @shalenikol release 0.2 исправлен расчёт x,y в convert2relative
 """
 import sys
 import numpy as np
@@ -20,6 +21,8 @@ def convert2relative(height, width, bbox):
     YOLO format use relative coordinates for annotation
     """
     x, y, w, h = bbox
+    x += w/2
+    y += h/2
     return x/width, y/height, w/width, h/height
 
 parser = argparse.ArgumentParser()
@@ -125,7 +128,8 @@ bproc.renderer.enable_segmentation_output(map_by=["category_id", "instance", "na
 
 res_dir = os.path.join(args.output_dir, 'coco_data')
 # Цикл рендеринга
-n = 3 # количество сэмплов для каждой локации камеры
+n_cam_location = 5 # количество случайных локаций камеры
+n_cam_poses = 3    # количество сэмплов для каждой локации камеры
 # Do multiple times: Position the shapenet objects using the physics simulator and render X images with random camera poses
 for r in range(args.imgs):
     # Randomly set the color and energy
@@ -177,7 +181,7 @@ for r in range(args.imgs):
 
     # Sample up to X camera poses
     #an = np.random.uniform(0.78, 1.2) #1. #0.35
-    for i in range(5):
+    for i in range(n_cam_location):
         # Sample location
         location = bproc.sampler.shell(center=[0, 0, 0],
                                        radius_min=1.1,
@@ -187,10 +191,10 @@ for r in range(args.imgs):
         # координата, по которой будем сэмплировать положение камеры
         j = random.randint(0, 2)
         # разовый сдвиг по случайной координате
-        d = (coord_max[j] - coord_min[j]) / n
+        d = (coord_max[j] - coord_min[j]) / n_cam_poses
         if location[j] < 0:
           d = -d
-        for k in range(n):
+        for k in range(n_cam_poses):
           # Compute rotation based on vector going from location towards poi
           rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854))
           # Add homog cam pose based on location an rotation
@@ -217,16 +221,16 @@ with open(os.path.join(res_dir,"coco_annotations.json"), "r") as fh:
     y = json.load(fh)
 
 # список имен объектов
-j = 0
+n_obj = 0
 obj_list = []
 with open(os.path.join(res_dir,"obj.names"), "w") as fh:
     for cat in y["categories"]:
         if cat["id"] < 999:
           n = cat["name"]
           i = cat["id"]
-          obj_list.append([n,i,j])
+          obj_list.append([n,i,n_obj])
           fh.write(n+"\n")
-          j += 1
+          n_obj += 1
     
 # содадим или очистим папку data для датасета
 res_data = os.path.join(res_dir, 'data')
@@ -237,14 +241,31 @@ else:
     os.mkdir(res_data)
 
 # список имен файлов с изображениями
+fn_image = os.path.join(res_dir,"images.txt")
 img_list = []
-with open(os.path.join(res_dir,"images.txt"), "w") as fh:
+with open(fn_image, "w") as fh:
     for i in y["images"]:
         filename = i["file_name"]
         shutil.copy(os.path.join(res_dir,filename),res_data)
         fh.write(filename.replace('images','data')+"\n")
         img_list.append([i["id"], (os.path.split(filename))[1]])
 
+# создадим 2 списка имен файлов для train и valid
+n_image_in_series = n_cam_location * n_cam_poses # количество изображений в серии
+i = 0
+fh = open(fn_image, "r")
+f1 = open(os.path.join(res_dir,"i_train.txt"), "w")
+f2 = open(os.path.join(res_dir,"i_val.txt"), "w")
+for line in fh:
+    i += 1
+    if i % n_image_in_series == 0:
+        f2.write(line)
+    else:
+        f1.write(line)
+fh.close()
+f1.close()
+f2.close()
+
 # заполним файлы с метками bbox
 for i in y["annotations"]:
     cat_id = i["category_id"]
@@ -264,3 +285,12 @@ for i in y["annotations"]:
             j = next(k for k, (_, x, _) in enumerate(obj_list) if x == cat_id)
             # формат: <target> <x-center> <y-center> <width> <height>
             fh.write(f"{obj_list[j][2]} {rel[0]} {rel[1]} {rel[2]} {rel[3]}\n")
+
+# создадим файл описания датасета для darknet
+with open(os.path.join(res_dir,"yolov4_objs2.data"), "w") as fh:
+    fh.write(f"classes = {n_obj}\n")
+    fh.write("train = i_train.txt\n")
+    fh.write("valid = i_val.txt\n")
+    fh.write("names = obj.names\n")
+    fh.write("backup = backup\n")
+    fh.write("eval = coco\n")
\ No newline at end of file
diff --git a/ObjectDetection/yolov4_test.cfg b/ObjectDetection/yolov4_test.cfg
new file mode 100644
index 0000000..66926bd
--- /dev/null
+++ b/ObjectDetection/yolov4_test.cfg
@@ -0,0 +1,1159 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+#batch=64
+#subdivisions=16
+width=416
+height=416
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 12000
+policy=steps
+steps=9600,10800
+scales=.1,.1
+
+#cutmix=1
+mosaic=1
+
+#:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-7
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+stopbackward=800
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 85
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 54
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=33
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=6
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.2
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=leaky
+
+[route]
+layers = -1, -16
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=33
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=6
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.1
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=leaky
+
+[route]
+layers = -1, -37
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=33
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=6
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+scale_x_y = 1.05
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5