Glenn-jocher's workspace
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11
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Tags
Created
Runtime
Sweep
artifact_alias
batch_size
bbox_interval
bucket
cache
cfg
cos_lr
data
data_dict.download
data_dict.names
data_dict.nc
data_dict.path
data_dict.test
data_dict.train
data_dict.val
device
epochs
exist_ok
freeze
hyp.anchor_t
hyp.anchors
hyp.box
hyp.cls
hyp.cls_pw
hyp.copy_paste
hyp.degrees
hyp.fl_gamma
hyp.fliplr
hyp.flipud
hyp.hsv_h
hyp.hsv_s
hyp.hsv_v
hyp.iou_t
hyp.lr0
hyp.lrf
hyp.mixup
hyp.momentum
hyp.mosaic
hyp.obj
hyp.obj_pw
hyp.perspective
hyp.scale
hyp.shear
hyp.translate
Finished
glenn-jocher
1h 53s
-
latest
64
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
50
false
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Finished
glenn-jocher
12s
-
latest
64
-1
ram
false
/content/yolov5/data/VOC.yaml
-
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Finished
glenn-jocher
4h 37m 53s
-
latest
16
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
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Finished
glenn-jocher
2h 43m 8s
-
latest
32
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
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Finished
glenn-jocher
1h 42m 16s
-
latest
64
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
50
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Finished
glenn-jocher
1h 6m 45s
-
latest
64
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
50
false
[0]
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Finished
glenn-jocher
4h 15m 5s
-
latest
16
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
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false
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Finished
glenn-jocher
2h 31m 22s
-
latest
32
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
50
false
[0]
3.3744
3.412
0.02
0.21638
0.5
0
0
0
0.5
0
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0.2
0.00334
0.15135
0.04266
0.74832
0.85834
0.51728
0.67198
0
0.75544
0
0.04591
Finished
glenn-jocher
1h 37m 58s
-
latest
64
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
50
false
[0]
3.3744
3.412
0.02
0.21638
0.5
0
0
0
0.5
0
0.01041
0.54703
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0.00334
0.15135
0.04266
0.74832
0.85834
0.51728
0.67198
0
0.75544
0
0.04591
Finished
glenn-jocher
1h 1m 1s
-
latest
64
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
50
false
[0]
3.3744
3.412
0.02
0.21638
0.5
0
0
0
0.5
0
0.01041
0.54703
0.27739
0.2
0.00334
0.15135
0.04266
0.74832
0.85834
0.51728
0.67198
0
0.75544
0
0.04591
Finished
glenn-jocher
55m 53s
-
latest
64
-1
ram
false
/content/yolov5/data/VOC.yaml
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)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
download(urls, dir=dir / 'images', delete=False, threads=3)
# Convert
path = dir / f'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)
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
20
../datasets/VOC
["/content/datasets/VOC/images/test2007"]
["/content/datasets/VOC/images/train2012","/content/datasets/VOC/images/train2007","/content/datasets/VOC/images/val2012","/content/datasets/VOC/images/val2007"]
["/content/datasets/VOC/images/test2007"]
50
false
[0]
3.3744
3.412
0.02
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0.5
0
0
0
0.5
0
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0.54703
0.27739
0.2
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0.04266
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0.75544
0
0.04591
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