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data/DOTA_ROTATED.yaml

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+# train and val datasets (image directory or *.txt file with image paths)
+train: ../DOTA_YOLO/train_1024_OBB/images/train2020/
+val: ../DOTA_YOLO/val_1024_OBB/images/train2020/
+
+#train: DOTA_demo_view/images/
+#val: DOTA_demo_view/images/
+
+# number of classes
+nc: 16
+
+# class names
+names: [ 'plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship',
+         'tennis-court', 'basketball-court', 'storage-tank',  'soccer-ball-field', 'roundabout', 'harbor',
+         'swimming-pool', 'helicopter', 'container-crane' ]

data/hyp.finetune.yaml

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+# Hyperparameters for VOC finetuning
+# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+
+# Hyperparameter Evolution Results
+# Generations: 306
+#                   P         R     mAP.5 mAP.5:.95       box       obj       cls
+# Metrics:        0.6     0.936     0.896     0.684    0.0115   0.00805   0.00146
+
+lr0: 0.0032
+lrf: 0.12
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 2.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.1 #0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+angle: 0.5
+angle_pw: 0.851
+iou_t: 0.2
+anchor_t: 2.91
+# anchors: 3.63
+fl_gamma: 2.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001; if perspective=0 only run random_Shear and random_translation augmentation; else run random_Center,perspective,Rotation,scale,shear,translation augmentation
+flipud: 0.5
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0

data/hyp.scratch.yaml

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+# Hyperparameters for COCO training from scratch
+# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.1  # box loss gain(weight)
+cls: 0.5  # cls loss gain(weight)
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 1.0  # obj loss gain(weight) (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+angle: 0.8
+angle_pw: 1.0
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 0  # anchors per output grid (0 to ignore)
+fl_gamma: 2.0  # focal loss gamma (efficientDet default gamma=1.5)(paper default alpha=0.25 gamma=2.0)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001; if perspective=0 only run random_Shear and random_translation augmentation; else run random_Center,perspective,Rotation,scale,shear,translation augmentation
+flipud: 0.5  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.0  # image mixup (probability)

data/scripts/get_coco.sh

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+#!/bin/bash
+# COCO 2017 dataset http://cocodataset.org
+# Download command: bash data/scripts/get_coco.sh
+# Train command: python train.py --data coco.yaml
+# Default dataset location is next to /yolov5:
+#   /parent_folder
+#     /coco
+#     /yolov5
+
+# Download/unzip labels
+echo 'Downloading COCO 2017 labels ...'
+d='../' # unzip directory
+f='coco2017labels.zip' && curl -L https://github.com/ultralytics/yolov5/releases/download/v1.0/$f -o $f
+unzip -q $f -d $d && rm $f
+
+# Download/unzip images
+echo 'Downloading COCO 2017 images ...'
+d='../coco/images' # unzip directory
+f='train2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f # 19G, 118k images
+f='val2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f   # 1G, 5k images
+# f='test2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f  # 7G,  41k images

data/scripts/get_voc.sh

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+#!/bin/bash
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
+# Download command: bash data/scripts/get_voc.sh
+# Train command: python train.py --data voc.yaml
+# Default dataset location is next to /yolov5:
+#   /parent_folder
+#     /VOC
+#     /yolov5
+
+start=$(date +%s)
+
+# handle optional download dir
+if [ -z "$1" ]; then
+  # navigate to ~/tmp
+  echo "navigating to ../tmp/ ..."
+  mkdir -p ../tmp
+  cd ../tmp/
+else
+  # check if is valid directory
+  if [ ! -d $1 ]; then
+    echo $1 "is not a valid directory"
+    exit 0
+  fi
+  echo "navigating to" $1 "..."
+  cd $1
+fi
+
+echo "Downloading VOC2007 trainval ..."
+# Download data
+curl -LO http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
+echo "Downloading VOC2007 test data ..."
+curl -LO http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
+echo "Done downloading."
+
+# Extract data
+echo "Extracting trainval ..."
+tar -xf VOCtrainval_06-Nov-2007.tar
+echo "Extracting test ..."
+tar -xf VOCtest_06-Nov-2007.tar
+echo "removing tars ..."
+rm VOCtrainval_06-Nov-2007.tar
+rm VOCtest_06-Nov-2007.tar
+
+end=$(date +%s)
+runtime=$((end - start))
+
+echo "Completed in" $runtime "seconds"
+
+start=$(date +%s)
+
+# handle optional download dir
+if [ -z "$1" ]; then
+  # navigate to ~/tmp
+  echo "navigating to ../tmp/ ..."
+  mkdir -p ../tmp
+  cd ../tmp/
+else
+  # check if is valid directory
+  if [ ! -d $1 ]; then
+    echo $1 "is not a valid directory"
+    exit 0
+  fi
+  echo "navigating to" $1 "..."
+  cd $1
+fi
+
+echo "Downloading VOC2012 trainval ..."
+# Download data
+curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+echo "Done downloading."
+
+# Extract data
+echo "Extracting trainval ..."
+tar -xf VOCtrainval_11-May-2012.tar
+echo "removing tar ..."
+rm VOCtrainval_11-May-2012.tar
+
+end=$(date +%s)
+runtime=$((end - start))
+
+echo "Completed in" $runtime "seconds"
+
+cd ../tmp
+echo "Spliting dataset..."
+python3 - "$@" <<END
+import xml.etree.ElementTree as ET
+import pickle
+import os
+from os import listdir, getcwd
+from os.path import join
+
+sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
+
+classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
+
+
+def convert(size, box):
+    dw = 1./(size[0])
+    dh = 1./(size[1])
+    x = (box[0] + box[1])/2.0 - 1
+    y = (box[2] + box[3])/2.0 - 1
+    w = box[1] - box[0]
+    h = box[3] - box[2]
+    x = x*dw
+    w = w*dw
+    y = y*dh
+    h = h*dh
+    return (x,y,w,h)
+
+def convert_annotation(year, image_id):
+    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
+    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
+    tree=ET.parse(in_file)
+    root = tree.getroot()
+    size = root.find('size')
+    w = int(size.find('width').text)
+    h = int(size.find('height').text)
+
+    for obj in root.iter('object'):
+        difficult = obj.find('difficult').text
+        cls = obj.find('name').text
+        if cls not in classes or int(difficult)==1:
+            continue
+        cls_id = classes.index(cls)
+        xmlbox = obj.find('bndbox')
+        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
+        bb = convert((w,h), b)
+        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
+
+wd = getcwd()
+
+for year, image_set in sets:
+    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
+        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
+    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
+    list_file = open('%s_%s.txt'%(year, image_set), 'w')
+    for image_id in image_ids:
+        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
+        convert_annotation(year, image_id)
+    list_file.close()
+
+END
+
+cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
+cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
+
+python3 - "$@" <<END
+
+import shutil
+import os
+os.system('mkdir ../VOC/')
+os.system('mkdir ../VOC/images')
+os.system('mkdir ../VOC/images/train')
+os.system('mkdir ../VOC/images/val')
+
+os.system('mkdir ../VOC/labels')
+os.system('mkdir ../VOC/labels/train')
+os.system('mkdir ../VOC/labels/val')
+
+import os
+print(os.path.exists('../tmp/train.txt'))
+f = open('../tmp/train.txt', 'r')
+lines = f.readlines()
+
+for line in lines:
+    line = "/".join(line.split('/')[-5:]).strip()
+    if (os.path.exists("../" + line)):
+        os.system("cp ../"+ line + " ../VOC/images/train")
+        
+    line = line.replace('JPEGImages', 'labels')
+    line = line.replace('jpg', 'txt')
+    if (os.path.exists("../" + line)):
+        os.system("cp ../"+ line + " ../VOC/labels/train")
+
+
+print(os.path.exists('../tmp/2007_test.txt'))
+f = open('../tmp/2007_test.txt', 'r')
+lines = f.readlines()
+
+for line in lines:
+    line = "/".join(line.split('/')[-5:]).strip()
+    if (os.path.exists("../" + line)):
+        os.system("cp ../"+ line + " ../VOC/images/val")
+        
+    line = line.replace('JPEGImages', 'labels')
+    line = line.replace('jpg', 'txt')
+    if (os.path.exists("../" + line)):
+        os.system("cp ../"+ line + " ../VOC/labels/val")
+
+END
+
+rm -rf ../tmp # remove temporary directory
+echo "VOC download done."