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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
# Example usage: python train.py --data Argoverse.yaml
# parent
# ├── yolov5
# └── datasets
# └── Argoverse ← downloads here (31.3 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/Argoverse # dataset root dir
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
# Classes
names:
0: person
1: bicycle
2: car
3: motorcycle
4: bus
5: truck
6: traffic_light
7: stop_sign
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import json
from tqdm import tqdm
from utils.general import download, Path
def argoverse2yolo(set):
labels = {}
a = json.load(open(set, "rb"))
for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
img_id = annot['image_id']
img_name = a['images'][img_id]['name']
img_label_name = f'{img_name[:-3]}txt'
cls = annot['category_id'] # instance class id
x_center, y_center, width, height = annot['bbox']
x_center = (x_center + width / 2) / 1920.0 # offset and scale
y_center = (y_center + height / 2) / 1200.0 # offset and scale
width /= 1920.0 # scale
height /= 1200.0 # scale
img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
if not img_dir.exists():
img_dir.mkdir(parents=True, exist_ok=True)
k = str(img_dir / img_label_name)
if k not in labels:
labels[k] = []
labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
for k in labels:
with open(k, "w") as f:
f.writelines(labels[k])
# Download
dir = Path('../datasets/Argoverse') # dataset root dir
urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
download(urls, dir=dir, delete=False)
# Convert
annotations_dir = 'Argoverse-HD/annotations/'
(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
for d in "train.json", "val.json":
argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
# Example usage: python train.py --data GlobalWheat2020.yaml
# parent
# ├── yolov5
# └── datasets
# └── GlobalWheat2020 ← downloads here (7.0 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/GlobalWheat2020 # dataset root dir
train: # train images (relative to 'path') 3422 images
- images/arvalis_1
- images/arvalis_2
- images/arvalis_3
- images/ethz_1
- images/rres_1
- images/inrae_1
- images/usask_1
val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
- images/ethz_1
test: # test images (optional) 1276 images
- images/utokyo_1
- images/utokyo_2
- images/nau_1
- images/uq_1
# Classes
names:
0: wheat_head
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
from utils.general import download, Path
# Download
dir = Path(yaml['path']) # dataset root dir
urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
download(urls, dir=dir)
# Make Directories
for p in 'annotations', 'images', 'labels':
(dir / p).mkdir(parents=True, exist_ok=True)
# Move
for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
(dir / p).rename(dir / 'images' / p) # move to /images
f = (dir / p).with_suffix('.json') # json file
if f.exists():
f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Objects365 dataset https://www.objects365.org/ by Megvii
# Example usage: python train.py --data Objects365.yaml
# parent
# ├── yolov5
# └── datasets
# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/Objects365 # dataset root dir
train: images/train # train images (relative to 'path') 1742289 images
val: images/val # val images (relative to 'path') 80000 images
test: # test images (optional)
# Classes
names:
0: Person
1: Sneakers
2: Chair
3: Other Shoes
4: Hat
5: Car
6: Lamp
7: Glasses
8: Bottle
9: Desk
10: Cup
11: Street Lights
12: Cabinet/shelf
13: Handbag/Satchel
14: Bracelet
15: Plate
16: Picture/Frame
17: Helmet
18: Book
19: Gloves
20: Storage box
21: Boat
22: Leather Shoes
23: Flower
24: Bench
25: Potted Plant
26: Bowl/Basin
27: Flag
28: Pillow
29: Boots
30: Vase
31: Microphone
32: Necklace
33: Ring
34: SUV
35: Wine Glass
36: Belt
37: Monitor/TV
38: Backpack
39: Umbrella
40: Traffic Light
41: Speaker
42: Watch
43: Tie
44: Trash bin Can
45: Slippers
46: Bicycle
47: Stool
48: Barrel/bucket
49: Van
50: Couch
51: Sandals
52: Basket
53: Drum
54: Pen/Pencil
55: Bus
56: Wild Bird
57: High Heels
58: Motorcycle
59: Guitar
60: Carpet
61: Cell Phone
62: Bread
63: Camera
64: Canned
65: Truck
66: Traffic cone
67: Cymbal
68: Lifesaver
69: Towel
70: Stuffed Toy
71: Candle
72: Sailboat
73: Laptop
74: Awning
75: Bed
76: Faucet
77: Tent
78: Horse
79: Mirror
80: Power outlet
81: Sink
82: Apple
83: Air Conditioner
84: Knife
85: Hockey Stick
86: Paddle
87: Pickup Truck
88: Fork
89: Traffic Sign
90: Balloon
91: Tripod
92: Dog
93: Spoon
94: Clock
95: Pot
96: Cow
97: Cake
98: Dinning Table
99: Sheep
100: Hanger
101: Blackboard/Whiteboard
102: Napkin
103: Other Fish
104: Orange/Tangerine
105: Toiletry
106: Keyboard
107: Tomato
108: Lantern
109: Machinery Vehicle
110: Fan
111: Green Vegetables
112: Banana
113: Baseball Glove
114: Airplane
115: Mouse
116: Train
117: Pumpkin
118: Soccer
119: Skiboard
120: Luggage
121: Nightstand
122: Tea pot
123: Telephone
124: Trolley
125: Head Phone
126: Sports Car
127: Stop Sign
128: Dessert
129: Scooter
130: Stroller
131: Crane
132: Remote
133: Refrigerator
134: Oven
135: Lemon
136: Duck
137: Baseball Bat
138: Surveillance Camera
139: Cat
140: Jug
141: Broccoli
142: Piano
143: Pizza
144: Elephant
145: Skateboard
146: Surfboard
147: Gun
148: Skating and Skiing shoes
149: Gas stove
150: Donut
151: Bow Tie
152: Carrot
153: Toilet
154: Kite
155: Strawberry
156: Other Balls
157: Shovel
158: Pepper
159: Computer Box
160: Toilet Paper
161: Cleaning Products
162: Chopsticks
163: Microwave
164: Pigeon
165: Baseball
166: Cutting/chopping Board
167: Coffee Table
168: Side Table
169: Scissors
170: Marker
171: Pie
172: Ladder
173: Snowboard
174: Cookies
175: Radiator
176: Fire Hydrant
177: Basketball
178: Zebra
179: Grape
180: Giraffe
181: Potato
182: Sausage
183: Tricycle
184: Violin
185: Egg
186: Fire Extinguisher
187: Candy
188: Fire Truck
189: Billiards
190: Converter
191: Bathtub
192: Wheelchair
193: Golf Club
194: Briefcase
195: Cucumber
196: Cigar/Cigarette
197: Paint Brush
198: Pear
199: Heavy Truck
200: Hamburger
201: Extractor
202: Extension Cord
203: Tong
204: Tennis Racket
205: Folder
206: American Football
207: earphone
208: Mask
209: Kettle
210: Tennis
211: Ship
212: Swing
213: Coffee Machine
214: Slide
215: Carriage
216: Onion
217: Green beans
218: Projector
219: Frisbee
220: Washing Machine/Drying Machine
221: Chicken
222: Printer
223: Watermelon
224: Saxophone
225: Tissue
226: Toothbrush
227: Ice cream
228: Hot-air balloon
229: Cello
230: French Fries
231: Scale
232: Trophy
233: Cabbage
234: Hot dog
235: Blender
236: Peach
237: Rice
238: Wallet/Purse
239: Volleyball
240: Deer
241: Goose
242: Tape
243: Tablet
244: Cosmetics
245: Trumpet
246: Pineapple
247: Golf Ball
248: Ambulance
249: Parking meter
250: Mango
251: Key
252: Hurdle
253: Fishing Rod
254: Medal
255: Flute
256: Brush
257: Penguin
258: Megaphone
259: Corn
260: Lettuce
261: Garlic
262: Swan
263: Helicopter
264: Green Onion
265: Sandwich
266: Nuts
267: Speed Limit Sign
268: Induction Cooker
269: Broom
270: Trombone
271: Plum
272: Rickshaw
273: Goldfish
274: Kiwi fruit
275: Router/modem
276: Poker Card
277: Toaster
278: Shrimp
279: Sushi
280: Cheese
281: Notepaper
282: Cherry
283: Pliers
284: CD
285: Pasta
286: Hammer
287: Cue
288: Avocado
289: Hamimelon
290: Flask
291: Mushroom
292: Screwdriver
293: Soap
294: Recorder
295: Bear
296: Eggplant
297: Board Eraser
298: Coconut
299: Tape Measure/Ruler
300: Pig
301: Showerhead
302: Globe
303: Chips
304: Steak
305: Crosswalk Sign
306: Stapler
307: Camel
308: Formula 1
309: Pomegranate
310: Dishwasher
311: Crab
312: Hoverboard
313: Meat ball
314: Rice Cooker
315: Tuba
316: Calculator
317: Papaya
318: Antelope
319: Parrot
320: Seal
321: Butterfly
322: Dumbbell
323: Donkey
324: Lion
325: Urinal
326: Dolphin
327: Electric Drill
328: Hair Dryer
329: Egg tart
330: Jellyfish
331: Treadmill
332: Lighter
333: Grapefruit
334: Game board
335: Mop
336: Radish
337: Baozi
338: Target
339: French
340: Spring Rolls
341: Monkey
342: Rabbit
343: Pencil Case
344: Yak
345: Red Cabbage
346: Binoculars
347: Asparagus
348: Barbell
349: Scallop
350: Noddles
351: Comb
352: Dumpling
353: Oyster
354: Table Tennis paddle
355: Cosmetics Brush/Eyeliner Pencil
356: Chainsaw
357: Eraser
358: Lobster
359: Durian
360: Okra
361: Lipstick
362: Cosmetics Mirror
363: Curling
364: Table Tennis
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
from tqdm import tqdm
from utils.general import Path, check_requirements, download, np, xyxy2xywhn
check_requirements(('pycocotools>=2.0',))
from pycocotools.coco import COCO
# Make Directories
dir = Path(yaml['path']) # dataset root dir
for p in 'images', 'labels':
(dir / p).mkdir(parents=True, exist_ok=True)
for q in 'train', 'val':
(dir / p / q).mkdir(parents=True, exist_ok=True)
# Train, Val Splits
for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
print(f"Processing {split} in {patches} patches ...")
images, labels = dir / 'images' / split, dir / 'labels' / split
# Download
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
if split == 'train':
download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
elif split == 'val':
download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
# Move
for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
f.rename(images / f.name) # move to /images/{split}
# Labels
coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
for cid, cat in enumerate(names):
catIds = coco.getCatIds(catNms=[cat])
imgIds = coco.getImgIds(catIds=catIds)
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
width, height = im["width"], im["height"]
path = Path(im["file_name"]) # image filename
try:
with open(labels / path.with_suffix('.txt').name, 'a') as file:
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
for a in coco.loadAnns(annIds):
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
except Exception as e:
print(e)

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
# Example usage: python train.py --data SKU-110K.yaml
# parent
# ├── yolov5
# └── datasets
# └── SKU-110K ← downloads here (13.6 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/SKU-110K # dataset root dir
train: train.txt # train images (relative to 'path') 8219 images
val: val.txt # val images (relative to 'path') 588 images
test: test.txt # test images (optional) 2936 images
# Classes
names:
0: object
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import shutil
from tqdm import tqdm
from utils.general import np, pd, Path, download, xyxy2xywh
# Download
dir = Path(yaml['path']) # dataset root dir
parent = Path(dir.parent) # download dir
urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
download(urls, dir=parent, delete=False)
# Rename directories
if dir.exists():
shutil.rmtree(dir)
(parent / 'SKU110K_fixed').rename(dir) # rename dir
(dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
# Convert labels
names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
images, unique_images = x[:, 0], np.unique(x[:, 0])
with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
f.writelines(f'./images/{s}\n' for s in unique_images)
for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
cls = 0 # single-class dataset
with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
for r in x[images == im]:
w, h = r[6], r[7] # image width, height
xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
# Example usage: python train.py --data VOC.yaml
# parent
# ├── yolov5
# └── datasets
# └── VOC ← downloads here (2.8 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/VOC
train: # train images (relative to 'path') 16551 images
- images/train2012
- images/train2007
- images/val2012
- images/val2007
val: # val images (relative to 'path') 4952 images
- images/test2007
test: # test images (optional)
- images/test2007
# Classes
names:
0: aeroplane
1: bicycle
2: bird
3: boat
4: bottle
5: bus
6: car
7: cat
8: chair
9: cow
10: diningtable
11: dog
12: horse
13: motorbike
14: person
15: pottedplant
16: sheep
17: sofa
18: train
19: tvmonitor
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import xml.etree.ElementTree as ET
from tqdm import tqdm
from utils.general import download, Path
def convert_label(path, lb_path, year, image_id):
def convert_box(size, box):
dw, dh = 1. / size[0], 1. / size[1]
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
return x * dw, y * dh, w * dw, h * dh
in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
out_file = open(lb_path, '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)
names = list(yaml['names'].values()) # names list
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in names and int(obj.find('difficult').text) != 1:
xmlbox = obj.find('bndbox')
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
cls_id = names.index(cls) # class id
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
# Download
dir = Path(yaml['path']) # dataset root dir
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
# Convert
path = dir / 'images/VOCdevkit'
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
imgs_path = dir / 'images' / f'{image_set}{year}'
lbs_path = dir / 'labels' / f'{image_set}{year}'
imgs_path.mkdir(exist_ok=True, parents=True)
lbs_path.mkdir(exist_ok=True, parents=True)
with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
image_ids = f.read().strip().split()
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
f.rename(imgs_path / f.name) # move image
convert_label(path, lb_path, year, id) # convert labels to YOLO format

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
# Example usage: python train.py --data VisDrone.yaml
# parent
# ├── yolov5
# └── datasets
# └── VisDrone ← downloads here (2.3 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/VisDrone # dataset root dir
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
# Classes
names:
0: pedestrian
1: people
2: bicycle
3: car
4: van
5: truck
6: tricycle
7: awning-tricycle
8: bus
9: motor
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
from utils.general import download, os, Path
def visdrone2yolo(dir):
from PIL import Image
from tqdm import tqdm
def convert_box(size, box):
# Convert VisDrone box to YOLO xywh box
dw = 1. / size[0]
dh = 1. / size[1]
return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
for f in pbar:
img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
lines = []
with open(f, 'r') as file: # read annotation.txt
for row in [x.split(',') for x in file.read().strip().splitlines()]:
if row[4] == '0': # VisDrone 'ignored regions' class 0
continue
cls = int(row[5]) - 1
box = convert_box(img_size, tuple(map(int, row[:4])))
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
fl.writelines(lines) # write label.txt
# Download
dir = Path(yaml['path']) # dataset root dir
urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
download(urls, dir=dir, curl=True, threads=4)
# Convert
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: python train.py --data coco.yaml
# parent
# ├── yolov5
# └── datasets
# └── coco ← downloads here (20.1 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco # dataset root dir
train: train2017.txt # train images (relative to 'path') 118287 images
val: val2017.txt # val images (relative to 'path') 5000 images
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
# Classes
names:
0: person
1: bicycle
2: car
3: motorcycle
4: airplane
5: bus
6: train
7: truck
8: boat
9: traffic light
10: fire hydrant
11: stop sign
12: parking meter
13: bench
14: bird
15: cat
16: dog
17: horse
18: sheep
19: cow
20: elephant
21: bear
22: zebra
23: giraffe
24: backpack
25: umbrella
26: handbag
27: tie
28: suitcase
29: frisbee
30: skis
31: snowboard
32: sports ball
33: kite
34: baseball bat
35: baseball glove
36: skateboard
37: surfboard
38: tennis racket
39: bottle
40: wine glass
41: cup
42: fork
43: knife
44: spoon
45: bowl
46: banana
47: apple
48: sandwich
49: orange
50: broccoli
51: carrot
52: hot dog
53: pizza
54: donut
55: cake
56: chair
57: couch
58: potted plant
59: bed
60: dining table
61: toilet
62: tv
63: laptop
64: mouse
65: remote
66: keyboard
67: cell phone
68: microwave
69: oven
70: toaster
71: sink
72: refrigerator
73: book
74: clock
75: vase
76: scissors
77: teddy bear
78: hair drier
79: toothbrush
# Download script/URL (optional)
download: |
from utils.general import download, Path
# Download labels
segments = False # segment or box labels
dir = Path(yaml['path']) # dataset root dir
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
download(urls, dir=dir.parent)
# Download data
urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
download(urls, dir=dir / 'images', threads=3)

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── datasets
# └── coco128 ← downloads here (7 MB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco128 # dataset root dir
train: images/train2017 # train images (relative to 'path') 128 images
val: images/train2017 # val images (relative to 'path') 128 images
test: # test images (optional)
# Classes
names:
0: person
1: bicycle
2: car
3: motorcycle
4: airplane
5: bus
6: train
7: truck
8: boat
9: traffic light
10: fire hydrant
11: stop sign
12: parking meter
13: bench
14: bird
15: cat
16: dog
17: horse
18: sheep
19: cow
20: elephant
21: bear
22: zebra
23: giraffe
24: backpack
25: umbrella
26: handbag
27: tie
28: suitcase
29: frisbee
30: skis
31: snowboard
32: sports ball
33: kite
34: baseball bat
35: baseball glove
36: skateboard
37: surfboard
38: tennis racket
39: bottle
40: wine glass
41: cup
42: fork
43: knife
44: spoon
45: bowl
46: banana
47: apple
48: sandwich
49: orange
50: broccoli
51: carrot
52: hot dog
53: pizza
54: donut
55: cake
56: chair
57: couch
58: potted plant
59: bed
60: dining table
61: toilet
62: tv
63: laptop
64: mouse
65: remote
66: keyboard
67: cell phone
68: microwave
69: oven
70: toaster
71: sink
72: refrigerator
73: book
74: clock
75: vase
76: scissors
77: teddy bear
78: hair drier
79: toothbrush
# Download script/URL (optional)
download: https://ultralytics.com/assets/coco128.zip

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path: null
train: E:/aicheck/data_set/11442136178662604800/trained/images/train/
val: E:/aicheck/data_set/11442136178662604800/trained/images/val/
test: null
names:
0: logo
1: 3C

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for Objects365 training
# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
lr0: 0.00258
lrf: 0.17
momentum: 0.779
weight_decay: 0.00058
warmup_epochs: 1.33
warmup_momentum: 0.86
warmup_bias_lr: 0.0711
box: 0.0539
cls: 0.299
cls_pw: 0.825
obj: 0.632
obj_pw: 1.0
iou_t: 0.2
anchor_t: 3.44
anchors: 3.2
fl_gamma: 0.0
hsv_h: 0.0188
hsv_s: 0.704
hsv_v: 0.36
degrees: 0.0
translate: 0.0902
scale: 0.491
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for VOC training
# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
# YOLOv5 Hyperparameter Evolution Results
# Best generation: 467
# Last generation: 996
# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss
# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865
lr0: 0.00334
lrf: 0.15135
momentum: 0.74832
weight_decay: 0.00025
warmup_epochs: 3.3835
warmup_momentum: 0.59462
warmup_bias_lr: 0.18657
box: 0.02
cls: 0.21638
cls_pw: 0.5
obj: 0.51728
obj_pw: 0.67198
iou_t: 0.2
anchor_t: 3.3744
fl_gamma: 0.0
hsv_h: 0.01041
hsv_s: 0.54703
hsv_v: 0.27739
degrees: 0.0
translate: 0.04591
scale: 0.75544
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 0.85834
mixup: 0.04266
copy_paste: 0.0
anchors: 3.412

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for high-augmentation COCO training from scratch
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --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: 0.1 # 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: 0.05 # box loss gain
cls: 0.3 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 0.7 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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: 0.9 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.1 # image mixup (probability)
copy_paste: 0.1 # segment copy-paste (probability)

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for low-augmentation COCO training from scratch
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # 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: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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
flipud: 0.0 # 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)
copy_paste: 0.0 # segment copy-paste (probability)

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for medium-augmentation COCO training from scratch
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --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: 0.1 # 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: 0.05 # box loss gain
cls: 0.3 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 0.7 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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: 0.9 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.1 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)

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#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download latest models from https://github.com/ultralytics/yolov5/releases
# Example usage: bash data/scripts/download_weights.sh
# parent
# └── yolov5
# ├── yolov5s.pt ← downloads here
# ├── yolov5m.pt
# └── ...
python - <<EOF
from utils.downloads import attempt_download
p5 = ['n', 's', 'm', 'l', 'x'] # P5 models
p6 = [f'{x}6' for x in p5] # P6 models
cls = [f'{x}-cls' for x in p5] # classification models
for x in p5 + p6 + cls:
attempt_download(f'weights/yolov5{x}.pt')
EOF

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#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download COCO 2017 dataset http://cocodataset.org
# Example usage: bash data/scripts/get_coco.sh
# parent
# ├── yolov5
# └── datasets
# └── coco ← downloads here
# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
if [ "$#" -gt 0 ]; then
for opt in "$@"; do
case "${opt}" in
--train) train=true ;;
--val) val=true ;;
--test) test=true ;;
--segments) segments=true ;;
esac
done
else
train=true
val=true
test=false
segments=false
fi
# Download/unzip labels
d='../datasets' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
if [ "$segments" == "true" ]; then
f='coco2017labels-segments.zip' # 168 MB
else
f='coco2017labels.zip' # 168 MB
fi
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
# Download/unzip images
d='../datasets/coco/images' # unzip directory
url=http://images.cocodataset.org/zips/
if [ "$train" == "true" ]; then
f='train2017.zip' # 19G, 118k images
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
fi
if [ "$val" == "true" ]; then
f='val2017.zip' # 1G, 5k images
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
fi
if [ "$test" == "true" ]; then
f='test2017.zip' # 7G, 41k images (optional)
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
fi
wait # finish background tasks

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#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
# Example usage: bash data/scripts/get_coco128.sh
# parent
# ├── yolov5
# └── datasets
# └── coco128 ← downloads here
# Download/unzip images and labels
d='../datasets' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
wait # finish background tasks

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#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download ILSVRC2012 ImageNet dataset https://image-net.org
# Example usage: bash data/scripts/get_imagenet.sh
# parent
# ├── yolov5
# └── datasets
# └── imagenet ← downloads here
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
if [ "$#" -gt 0 ]; then
for opt in "$@"; do
case "${opt}" in
--train) train=true ;;
--val) val=true ;;
esac
done
else
train=true
val=true
fi
# Make dir
d='../datasets/imagenet' # unzip directory
mkdir -p $d && cd $d
# Download/unzip train
if [ "$train" == "true" ]; then
wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME; do
mkdir -p "${NAME%.tar}"
tar -xf "${NAME}" -C "${NAME%.tar}"
rm -f "${NAME}"
done
cd ..
fi
# Download/unzip val
if [ "$val" == "true" ]; then
wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
fi
# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
# rm train/n04266014/n04266014_10835.JPEG
# TFRecords (optional)
# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
# Example usage: python train.py --data xView.yaml
# parent
# ├── yolov5
# └── datasets
# └── xView ← downloads here (20.7 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/xView # dataset root dir
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
# Classes
names:
0: Fixed-wing Aircraft
1: Small Aircraft
2: Cargo Plane
3: Helicopter
4: Passenger Vehicle
5: Small Car
6: Bus
7: Pickup Truck
8: Utility Truck
9: Truck
10: Cargo Truck
11: Truck w/Box
12: Truck Tractor
13: Trailer
14: Truck w/Flatbed
15: Truck w/Liquid
16: Crane Truck
17: Railway Vehicle
18: Passenger Car
19: Cargo Car
20: Flat Car
21: Tank car
22: Locomotive
23: Maritime Vessel
24: Motorboat
25: Sailboat
26: Tugboat
27: Barge
28: Fishing Vessel
29: Ferry
30: Yacht
31: Container Ship
32: Oil Tanker
33: Engineering Vehicle
34: Tower crane
35: Container Crane
36: Reach Stacker
37: Straddle Carrier
38: Mobile Crane
39: Dump Truck
40: Haul Truck
41: Scraper/Tractor
42: Front loader/Bulldozer
43: Excavator
44: Cement Mixer
45: Ground Grader
46: Hut/Tent
47: Shed
48: Building
49: Aircraft Hangar
50: Damaged Building
51: Facility
52: Construction Site
53: Vehicle Lot
54: Helipad
55: Storage Tank
56: Shipping container lot
57: Shipping Container
58: Pylon
59: Tower
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import json
import os
from pathlib import Path
import numpy as np
from PIL import Image
from tqdm import tqdm
from utils.datasets import autosplit
from utils.general import download, xyxy2xywhn
def convert_labels(fname=Path('xView/xView_train.geojson')):
# Convert xView geoJSON labels to YOLO format
path = fname.parent
with open(fname) as f:
print(f'Loading {fname}...')
data = json.load(f)
# Make dirs
labels = Path(path / 'labels' / 'train')
os.system(f'rm -rf {labels}')
labels.mkdir(parents=True, exist_ok=True)
# xView classes 11-94 to 0-59
xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
shapes = {}
for feature in tqdm(data['features'], desc=f'Converting {fname}'):
p = feature['properties']
if p['bounds_imcoords']:
id = p['image_id']
file = path / 'train_images' / id
if file.exists(): # 1395.tif missing
try:
box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
cls = p['type_id']
cls = xview_class2index[int(cls)] # xView class to 0-60
assert 59 >= cls >= 0, f'incorrect class index {cls}'
# Write YOLO label
if id not in shapes:
shapes[id] = Image.open(file).size
box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
with open((labels / id).with_suffix('.txt'), 'a') as f:
f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
except Exception as e:
print(f'WARNING: skipping one label for {file}: {e}')
# Download manually from https://challenge.xviewdataset.org
dir = Path(yaml['path']) # dataset root dir
# urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
# 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
# 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
# download(urls, dir=dir, delete=False)
# Convert labels
convert_labels(dir / 'xView_train.geojson')
# Move images
images = Path(dir / 'images')
images.mkdir(parents=True, exist_ok=True)
Path(dir / 'train_images').rename(dir / 'images' / 'train')
Path(dir / 'val_images').rename(dir / 'images' / 'val')
# Split
autosplit(dir / 'images' / 'train')