diff --git a/ObjectDetection/obj2Yolov4dataset.py b/ObjectDetection/obj2Yolov4dataset.py
new file mode 100644
index 0000000..d9960d3
--- /dev/null
+++ b/ObjectDetection/obj2Yolov4dataset.py
@@ -0,0 +1,141 @@
+import blenderproc as bproc
+"""
+  obj2Yolov4dataset
+  Общая задача: обнаружение объекта (Object detection)
+  Реализуемая функция: создание датасета в формате YoloV4 для заданного объекта (*.obj)
+  Используется модуль blenderproc
+
+  24.01.2023 @shalenikol release 0.1
+"""
+import numpy as np
+import argparse
+import random
+import os
+import shutil
+import json
+
+def convert2relative(height, width, bbox):
+    """
+    YOLO format use relative coordinates for annotation
+    """
+    x, y, w, h = bbox
+    return x/width, y/height, w/width, h/height
+
+parser = argparse.ArgumentParser()
+parser.add_argument('scene', nargs='?', default="resources/robossembler-asset.obj", help="Path to the object file.")
+parser.add_argument('output_dir', nargs='?', default="output", help="Path to where the final files, will be saved")
+parser.add_argument('--imgs', default=1, type=int, help="The number of times the objects should be rendered.")
+args = parser.parse_args()
+
+if not os.path.isdir(args.output_dir):
+    os.mkdir(args.output_dir)
+
+bproc.init()
+
+# load the objects into the scene
+obj = bproc.loader.load_obj(args.scene)[0]
+obj.set_cp("category_id", 1)
+
+# Randomly perturbate the material of the object
+mat = obj.get_materials()[0]
+mat.set_principled_shader_value("Specular", random.uniform(0, 1))
+mat.set_principled_shader_value("Roughness", random.uniform(0, 1))
+mat.set_principled_shader_value("Base Color", np.random.uniform([0, 0, 0, 1], [1, 1, 1, 1]))
+mat.set_principled_shader_value("Metallic", random.uniform(0, 1))
+
+# Create a new light
+light = bproc.types.Light()
+light.set_type("POINT")
+# Sample its location around the object
+light.set_location(bproc.sampler.shell(
+    center=obj.get_location(),
+    radius_min=1,
+    radius_max=5,
+    elevation_min=1,
+    elevation_max=89
+))
+# Randomly set the color and energy
+light.set_color(np.random.uniform([0.5, 0.5, 0.5], [1, 1, 1]))
+light.set_energy(random.uniform(100, 1000))
+
+bproc.camera.set_resolution(640, 480)
+
+# Sample five camera poses
+poses = 0
+tries = 0
+while tries < 10000 and poses < args.imgs:
+    # Sample random camera location around the object
+    location = bproc.sampler.shell(
+        center=obj.get_location(),
+        radius_min=1,
+        radius_max=4,
+        elevation_min=1,
+        elevation_max=89
+    )
+    # Compute rotation based lookat point which is placed randomly around the object
+    lookat_point = obj.get_location() + np.random.uniform([-0.5, -0.5, -0.5], [0.5, 0.5, 0.5])
+    rotation_matrix = bproc.camera.rotation_from_forward_vec(lookat_point - location, inplane_rot=np.random.uniform(-0.7854, 0.7854))
+    # Add homog cam pose based on location an rotation
+    cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix)
+
+    # Only add camera pose if object is still visible
+    if obj in bproc.camera.visible_objects(cam2world_matrix):
+        bproc.camera.add_camera_pose(cam2world_matrix)
+        poses += 1
+    tries += 1
+
+# Enable transparency so the background becomes transparent
+bproc.renderer.set_output_format(enable_transparency=True)
+# add segmentation masks (per class and per instance)
+bproc.renderer.enable_segmentation_output(map_by=["category_id", "instance", "name"])
+
+# Render RGB images
+data = bproc.renderer.render()
+
+# Write data to coco file
+res_dir = os.path.join(args.output_dir, 'coco_data')
+bproc.writer.write_coco_annotations(res_dir,
+                                    instance_segmaps=data["instance_segmaps"],
+                                    instance_attribute_maps=data["instance_attribute_maps"],
+                                    color_file_format='JPEG',
+                                    colors=data["colors"],
+                                    append_to_existing_output=True)
+
+#загрузим аннотацию
+with open(os.path.join(res_dir,"coco_annotations.json"), "r") as fh:
+    y = json.load(fh)
+
+# список имен объектов
+with open(os.path.join(res_dir,"obj.names"), "w") as fh:
+    for cat in y["categories"]:
+        fh.write(cat["name"]+"\n")
+    
+# содадим или очистим папку data для датасета
+res_data = os.path.join(res_dir, 'data')
+if os.path.isdir(res_data):
+    for f in os.listdir(res_data):
+        os.remove(os.path.join(res_data, f))
+else:
+    os.mkdir(res_data)
+
+# список имен файлов с изображениями
+s = []
+with open(os.path.join(res_dir,"images.txt"), "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")
+        s.append((os.path.split(filename))[1])
+
+# предполагается, что "images" и "annotations" следуют в одном и том же порядке
+c = 0
+for i in y["annotations"]:
+    bbox = i["bbox"]
+    im_h = i["height"]
+    im_w = i["width"]
+    rel = convert2relative(im_h,im_w,bbox)
+    fn = (os.path.splitext(s[c]))[0] # только имя файла
+    with open(os.path.join(res_data,fn+".txt"), "w") as fh:
+        # формат: <target> <x-center> <y-center> <width> <height>
+        fh.write("0 "+'{:-f} {:-f} {:-f} {:-f}'.format(rel[0],rel[1],rel[2],rel[3])+"\n")
+    c += 1
diff --git a/ObjectDetection/objs2Yolov4dataset.py b/ObjectDetection/objs2Yolov4dataset.py
new file mode 100644
index 0000000..d84e839
--- /dev/null
+++ b/ObjectDetection/objs2Yolov4dataset.py
@@ -0,0 +1,266 @@
+import blenderproc as bproc
+"""
+  objs2Yolov4dataset
+  Общая задача: обнаружение объекта (Object detection)
+  Реализуемая функция: создание датасета в формате YoloV4 для серии заданных объектов (*.obj) в заданной сцене (*.blend)
+  Используется модуль blenderproc
+
+  17.02.2023 @shalenikol release 0.1
+"""
+import sys
+import numpy as np
+import argparse
+import random
+import os
+import shutil
+import json
+
+def convert2relative(height, width, bbox):
+    """
+    YOLO format use relative coordinates for annotation
+    """
+    x, y, w, h = bbox
+    return x/width, y/height, w/width, h/height
+
+parser = argparse.ArgumentParser()
+parser.add_argument('scene', nargs='?', default="resources/sklad.blend", help="Path to the scene object.")
+parser.add_argument('obj_path', nargs='?', default="resources/in_obj", help="Path to the object files.")
+parser.add_argument('output_dir', nargs='?', default="output", help="Path to where the final files, will be saved")
+parser.add_argument('vhacd_path', nargs='?', default="blenderproc_resources/vhacd", help="The directory in which vhacd should be installed or is already installed.")
+parser.add_argument('--imgs', default=2, type=int, help="The number of times the objects should be rendered.")
+args = parser.parse_args()
+
+if not os.path.isdir(args.obj_path):
+    print(f"{args.obj_path} : no object directory")
+    sys.exit()
+
+if not os.path.isdir(args.output_dir):
+    os.mkdir(args.output_dir)
+
+bproc.init()
+
+# ? загрузим свет из сцены
+#cam = bproc.loader.load_blend(args.scene, data_blocks=["cameras"])
+#lights = bproc.loader.load_blend(args.scene, data_blocks=["lights"])
+
+# загрузим объекты
+list_files = os.listdir(args.obj_path)
+meshs = []
+i = 0
+for f in list_files:
+    if (os.path.splitext(f))[1] == ".obj":
+        f = os.path.join(args.obj_path, f) # путь к файлу объекта
+        if os.path.isfile(f):
+            meshs += bproc.loader.load_obj(f)
+            i += 1
+
+if i == 0:
+    print("Objects not found")
+    sys.exit()
+
+for i,o in enumerate(meshs):
+    o.set_cp("category_id", i+1)
+
+# загрузим сцену
+scene = bproc.loader.load_blend(args.scene, data_blocks=["objects"])
+#scene = bproc.loader.load_obj(args.scene)
+
+# найдём пол
+floor = None
+for o in scene:
+    o.set_cp("category_id", 999)
+    s = o.get_name()
+    if s.find("floor") >= 0:
+        floor = o
+if floor == None:
+    print("Floor not found in the scene")
+    sys.exit()
+
+floor.enable_rigidbody(False, collision_shape='BOX')
+
+objs = meshs + scene
+
+for obj in meshs:
+    # Make the object actively participate in the physics simulation
+    obj.enable_rigidbody(active=True, collision_shape="COMPOUND")
+    # Also use convex decomposition as collision shapes
+    obj.build_convex_decomposition_collision_shape(args.vhacd_path)
+
+with open(os.path.join(args.output_dir,"res.txt"), "w") as fh:
+#    fh.write(str(type(scene[0]))+"\n")
+    i = 0
+    for o in objs:
+        i += 1
+        loc = o.get_location()
+        euler = o.get_rotation_euler()
+        fh.write(f"{i} : {o.get_name()}  {loc} {euler}\n")
+
+# define a light and set its location and energy level
+light = bproc.types.Light()
+light.set_type("POINT")
+light.set_location([5, -5, 5])
+#light.set_energy(900)
+#light.set_color([0.7, 0.7, 0.7])
+
+light1 = bproc.types.Light(name="light1")
+light1.set_type("SUN")
+light1.set_location([0, 0, 0])
+light1.set_rotation_euler([-0.063, 0.6177, -0.1985])
+#light1.set_energy(7)
+light1.set_color([1, 1, 1])
+"""
+# Sample its location around the object
+light.set_location(bproc.sampler.shell(
+    center=obj.get_location(),
+    radius_min=2.5,
+    radius_max=5,
+    elevation_min=1,
+    elevation_max=89
+))
+"""
+
+# define the camera intrinsics
+bproc.camera.set_intrinsics_from_blender_params(1, 640, 480, lens_unit="FOV")
+bproc.renderer.enable_segmentation_output(map_by=["category_id", "instance", "name"])
+
+res_dir = os.path.join(args.output_dir, 'coco_data')
+# Цикл рендеринга
+n = 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
+    light.set_color(np.random.uniform([0.5, 0.5, 0.5], [1, 1, 1]))
+    light.set_energy(random.uniform(500, 1000))
+    light1.set_energy(random.uniform(3, 11))
+
+    for i,o in enumerate(objs):
+        mat = o.get_materials()[0]
+        mat.set_principled_shader_value("Specular", random.uniform(0, 1))
+        mat.set_principled_shader_value("Roughness", random.uniform(0, 1))
+        mat.set_principled_shader_value("Base Color", np.random.uniform([0, 0, 0, 1], [1, 1, 1, 1]))
+        mat.set_principled_shader_value("Metallic", random.uniform(0, 1))
+
+    # Clear all key frames from the previous run
+    bproc.utility.reset_keyframes()
+
+    # Define a function that samples 6-DoF poses
+    def sample_pose(obj: bproc.types.MeshObject):
+        obj.set_location(np.random.uniform([-1, -1.5, 0.2], [1, 2, 1.2])) #[-1, -1, 0], [1, 1, 2]))
+        obj.set_rotation_euler(bproc.sampler.uniformSO3())
+
+    # Sample the poses of all shapenet objects above the ground without any collisions in-between
+    bproc.object.sample_poses(meshs, objects_to_check_collisions = meshs + [floor], sample_pose_func = sample_pose)
+
+    # Run the simulation and fix the poses of the shapenet objects at the end
+    bproc.object.simulate_physics_and_fix_final_poses(min_simulation_time=4, max_simulation_time=20, check_object_interval=1)
+
+    # Find point of interest, all cam poses should look towards it
+    poi = bproc.object.compute_poi(meshs)
+
+    coord_max = [0.1, 0.1, 0.1]
+    coord_min = [0., 0., 0.]
+
+    with open(os.path.join(args.output_dir,"res.txt"), "a") as fh:
+        fh.write("*****************\n")
+        fh.write(f"{r}) poi = {poi}\n")
+        i = 0
+        for o in meshs:
+            i += 1
+            loc = o.get_location()
+            euler = o.get_rotation_euler()
+            fh.write(f"   {i} : {o.get_name()}  {loc} {euler}\n")
+            for j in range(3):
+                if loc[j] < coord_min[j]:
+                    coord_min[j] = loc[j]
+                if loc[j] > coord_max[j]:
+                    coord_max[j] = loc[j]
+
+    # Sample up to X camera poses
+    #an = np.random.uniform(0.78, 1.2) #1. #0.35
+    for i in range(5):
+        # Sample location
+        location = bproc.sampler.shell(center=[0, 0, 0],
+                                       radius_min=1.1,
+                                       radius_max=3.3,
+                                       elevation_min=5,
+                                       elevation_max=89)
+        # 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
+        if location[j] < 0:
+          d = -d
+        for k in range(n):
+          # 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
+          cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix)
+          bproc.camera.add_camera_pose(cam2world_matrix)
+          location[j] -= d
+        #world_matrix = bproc.math.build_transformation_mat([2.3, -0.4, 0.66], [1.396, 0., an])
+        #bproc.camera.add_camera_pose(world_matrix)
+        #an += 0.2
+
+    # render the whole pipeline
+    data = bproc.renderer.render()
+
+    # Write data to coco file
+    bproc.writer.write_coco_annotations(res_dir,
+                                    instance_segmaps=data["instance_segmaps"],
+                                    instance_attribute_maps=data["instance_attribute_maps"],
+                                    color_file_format='JPEG',
+                                    colors=data["colors"],
+                                    append_to_existing_output=True)
+
+#загрузим аннотацию
+with open(os.path.join(res_dir,"coco_annotations.json"), "r") as fh:
+    y = json.load(fh)
+
+# список имен объектов
+j = 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])
+          fh.write(n+"\n")
+          j += 1
+    
+# содадим или очистим папку data для датасета
+res_data = os.path.join(res_dir, 'data')
+if os.path.isdir(res_data):
+    for f in os.listdir(res_data):
+        os.remove(os.path.join(res_data, f))
+else:
+    os.mkdir(res_data)
+
+# список имен файлов с изображениями
+img_list = []
+with open(os.path.join(res_dir,"images.txt"), "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]])
+
+# заполним файлы с метками bbox
+for i in y["annotations"]:
+    cat_id = i["category_id"]
+    if cat_id < 999:
+        im_id = i["image_id"]
+        bbox = i["bbox"]
+        im_h = i["height"]
+        im_w = i["width"]
+        rel = convert2relative(im_h,im_w,bbox)
+    
+        # находим индекс списка с нужным изображением
+        j = next(k for k, (x, _) in enumerate(img_list) if x == im_id)
+        filename = img_list[j][1]
+        fn = (os.path.splitext(filename))[0] # только имя файла
+        with open(os.path.join(res_data,fn+".txt"), "a") as fh:
+            # находим индекс списка с нужным объектом
+            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")
diff --git a/ObjectDetection/yolov4_min.cfg b/ObjectDetection/yolov4_min.cfg
new file mode 100644
index 0000000..32f07de
--- /dev/null
+++ b/ObjectDetection/yolov4_min.cfg
@@ -0,0 +1,1160 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=64
+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 = 6000
+policy=steps
+steps=4800,5400
+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=18
+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=1
+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=18
+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=1
+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=18
+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=1
+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
+
diff --git a/ObjectDetection/yolov4_min.data b/ObjectDetection/yolov4_min.data
new file mode 100644
index 0000000..14a4f37
--- /dev/null
+++ b/ObjectDetection/yolov4_min.data
@@ -0,0 +1,7 @@
+classes= 1
+train  = i_train.txt
+valid = i_val.txt
+names = obj.names
+backup = backup
+eval=coco
+
diff --git a/ObjectDetection/yolov4_objs2.cfg b/ObjectDetection/yolov4_objs2.cfg
new file mode 100644
index 0000000..8c09f4e
--- /dev/null
+++ b/ObjectDetection/yolov4_objs2.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
diff --git a/ObjectDetection/yolov4_objs2.data b/ObjectDetection/yolov4_objs2.data
new file mode 100644
index 0000000..dd29275
--- /dev/null
+++ b/ObjectDetection/yolov4_objs2.data
@@ -0,0 +1,7 @@
+classes= 6
+train  = i_train.txt
+valid = i_val.txt
+names = obj.names
+backup = backup
+eval=coco
+