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freeneuro
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storage.googleapis.com
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storage.googleapis.com
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runs/*
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runs/*
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data/*
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!data/images/zidane.jpg
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!data/images/zidane.jpg
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!data/images/bus.jpg
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!data/images/bus.jpg
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!data/coco.names
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!data/coco.names
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# Global Wheat 2020 dataset http://www.global-wheat.com/
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# Train command: python train.py --data GlobalWheat2020.yaml
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# Default dataset location is next to YOLOv3:
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# /parent_folder
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# /datasets/GlobalWheat2020
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# /yolov3
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: # 3422 images
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- ../datasets/GlobalWheat2020/images/arvalis_1
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- ../datasets/GlobalWheat2020/images/arvalis_2
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- ../datasets/GlobalWheat2020/images/arvalis_3
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- ../datasets/GlobalWheat2020/images/ethz_1
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- ../datasets/GlobalWheat2020/images/rres_1
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- ../datasets/GlobalWheat2020/images/inrae_1
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- ../datasets/GlobalWheat2020/images/usask_1
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val: # 748 images (WARNING: train set contains ethz_1)
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- ../datasets/GlobalWheat2020/images/ethz_1
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test: # 1276 images
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- ../datasets/GlobalWheat2020/images/utokyo_1
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- ../datasets/GlobalWheat2020/images/utokyo_2
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- ../datasets/GlobalWheat2020/images/nau_1
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- ../datasets/GlobalWheat2020/images/uq_1
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# number of classes
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nc: 1
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# class names
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names: [ 'wheat_head' ]
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# download command/URL (optional) --------------------------------------------------------------------------------------
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download: |
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from utils.general import download, Path
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# Download
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dir = Path('../datasets/GlobalWheat2020') # dataset directory
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urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
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download(urls, dir=dir)
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# Make Directories
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for p in 'annotations', 'images', 'labels':
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(dir / p).mkdir(parents=True, exist_ok=True)
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# Move
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for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
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'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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(dir / p).rename(dir / 'images' / p) # move to /images
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f = (dir / p).with_suffix('.json') # json file
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if f.exists():
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f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
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# Train command: python train.py --data SKU-110K.yaml
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# Default dataset location is next to YOLOv3:
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# /parent_folder
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# /datasets/SKU-110K
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# /yolov3
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../datasets/SKU-110K/train.txt # 8219 images
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val: ../datasets/SKU-110K/val.txt # 588 images
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test: ../datasets/SKU-110K/test.txt # 2936 images
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# number of classes
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nc: 1
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# class names
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names: [ 'object' ]
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# download command/URL (optional) --------------------------------------------------------------------------------------
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download: |
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import shutil
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from tqdm import tqdm
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from utils.general import np, pd, Path, download, xyxy2xywh
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# Download
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datasets = Path('../datasets') # download directory
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urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
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download(urls, dir=datasets, delete=False)
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# Rename directories
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dir = (datasets / 'SKU-110K')
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if dir.exists():
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shutil.rmtree(dir)
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(datasets / 'SKU110K_fixed').rename(dir) # rename dir
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(dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
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# Convert labels
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names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
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for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
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x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
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images, unique_images = x[:, 0], np.unique(x[:, 0])
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with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
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f.writelines(f'./images/{s}\n' for s in unique_images)
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for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
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cls = 0 # single-class dataset
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with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
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for r in x[images == im]:
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w, h = r[6], r[7] # image width, height
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xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
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f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
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# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
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# Train command: python train.py --data VisDrone.yaml
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# Default dataset location is next to YOLOv3:
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# /parent_folder
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# /VisDrone
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# /yolov3
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../VisDrone/VisDrone2019-DET-train/images # 6471 images
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val: ../VisDrone/VisDrone2019-DET-val/images # 548 images
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test: ../VisDrone/VisDrone2019-DET-test-dev/images # 1610 images
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# number of classes
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nc: 10
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# class names
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names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
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# download command/URL (optional) --------------------------------------------------------------------------------------
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download: |
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from utils.general import download, os, Path
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def visdrone2yolo(dir):
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from PIL import Image
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from tqdm import tqdm
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def convert_box(size, box):
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# Convert VisDrone box to YOLO xywh box
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dw = 1. / size[0]
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dh = 1. / size[1]
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return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
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(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
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pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
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for f in pbar:
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img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
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lines = []
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with open(f, 'r') as file: # read annotation.txt
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for row in [x.split(',') for x in file.read().strip().splitlines()]:
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if row[4] == '0': # VisDrone 'ignored regions' class 0
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continue
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cls = int(row[5]) - 1
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box = convert_box(img_size, tuple(map(int, row[:4])))
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lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
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with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
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fl.writelines(lines) # write label.txt
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# Download
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dir = Path('../VisDrone') # dataset directory
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urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
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download(urls, dir=dir)
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# Convert
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for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
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visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
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# Train command: python train.py --data argoverse_hd.yaml
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# Default dataset location is next to YOLOv3:
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# /parent_folder
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# /argoverse
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# /yolov3
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# download command/URL (optional)
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download: bash data/scripts/get_argoverse_hd.sh
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images
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val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges
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test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview
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# number of classes
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nc: 8
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# class names
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ]
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# COCO 2017 dataset http://cocodataset.org - first 128 training images
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# Train command: python train.py --data coco128.yaml
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# Default dataset location is next to YOLOv5:
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# /parent
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# /datasets/coco128
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# /yolov5
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# 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, ..]
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path: F:/PythonPro/DualAttentionAttack/data/phy_attack # dataset root dir
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train: train_new # train_new train_label_new
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val: train_new # val images (relative to 'path') 128 images
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test: # test images (optional)
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# Classes
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nc: 80 # number of classes
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush' ] # class names
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# COCO 2017 dataset http://cocodataset.org
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# Train command: python train.py --data coco.yaml
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# Default dataset location is next to YOLOv3:
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# /parent_folder
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# /coco
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# /yolov3
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# download command/URL (optional)
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download: bash data/scripts/get_coco.sh
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../coco/train2017.txt # 118287 images
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val: ../coco/val2017.txt # 5000 images
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test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
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# number of classes
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nc: 80
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# class names
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush' ]
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# Print classes
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# with open('data/coco.yaml') as f:
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# d = yaml.safe_load(f) # dict
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# for i, x in enumerate(d['names']):
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# print(i, x)
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# COCO 2017 dataset http://cocodataset.org - first 128 training images
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# Train command: python train.py --data coco128.yaml
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# Default dataset location is next to YOLOv3:
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# /parent_folder
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# /coco128
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# /yolov3
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# download command/URL (optional)
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download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: F:/DataSource/coco128/images/train2017/ # 128 images
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val: F:/DataSource/coco128/images/train2017/ # 128 images
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# number of classes
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nc: 80
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# class names
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush' ]
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# Hyperparameters for VOC finetuning
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# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
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# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
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# Hyperparameter Evolution Results
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||||||
|
# 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: 0.0296
|
||||||
|
cls: 0.243
|
||||||
|
cls_pw: 0.631
|
||||||
|
obj: 0.301
|
||||||
|
obj_pw: 0.911
|
||||||
|
iou_t: 0.2
|
||||||
|
anchor_t: 2.91
|
||||||
|
# anchors: 3.63
|
||||||
|
fl_gamma: 0.0
|
||||||
|
hsv_h: 0.0138
|
||||||
|
hsv_s: 0.664
|
||||||
|
hsv_v: 0.464
|
||||||
|
degrees: 0.373
|
||||||
|
translate: 0.245
|
||||||
|
scale: 0.898
|
||||||
|
shear: 0.602
|
||||||
|
perspective: 0.0
|
||||||
|
flipud: 0.00856
|
||||||
|
fliplr: 0.5
|
||||||
|
mosaic: 1.0
|
||||||
|
mixup: 0.243
|
||||||
|
|
@ -0,0 +1,28 @@
|
||||||
|
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
|
||||||
|
|
@ -0,0 +1,33 @@
|
||||||
|
# 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: 0.05 # 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)
|
||||||
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|
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|
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|
|
@ -0,0 +1,102 @@
|
||||||
|
# Objects365 dataset https://www.objects365.org/
|
||||||
|
# Train command: python train.py --data objects365.yaml
|
||||||
|
# Default dataset location is next to YOLOv3:
|
||||||
|
# /parent_folder
|
||||||
|
# /datasets/objects365
|
||||||
|
# /yolov3
|
||||||
|
|
||||||
|
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||||
|
train: ../datasets/objects365/images/train # 1742289 images
|
||||||
|
val: ../datasets/objects365/images/val # 5570 images
|
||||||
|
|
||||||
|
# number of classes
|
||||||
|
nc: 365
|
||||||
|
|
||||||
|
# class names
|
||||||
|
names: [ 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
|
||||||
|
'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
|
||||||
|
'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
|
||||||
|
'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
|
||||||
|
'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
|
||||||
|
'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
|
||||||
|
'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
|
||||||
|
'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
|
||||||
|
'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
|
||||||
|
'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
|
||||||
|
'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
|
||||||
|
'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
|
||||||
|
'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
|
||||||
|
'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
|
||||||
|
'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
|
||||||
|
'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
|
||||||
|
'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
|
||||||
|
'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
|
||||||
|
'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
|
||||||
|
'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
|
||||||
|
'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
|
||||||
|
'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
|
||||||
|
'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
|
||||||
|
'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
|
||||||
|
'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
|
||||||
|
'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
|
||||||
|
'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
|
||||||
|
'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
|
||||||
|
'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
|
||||||
|
'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
|
||||||
|
'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
|
||||||
|
'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
|
||||||
|
'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
|
||||||
|
'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
|
||||||
|
'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
|
||||||
|
'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
|
||||||
|
'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
|
||||||
|
'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
|
||||||
|
'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
|
||||||
|
'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
|
||||||
|
'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis' ]
|
||||||
|
|
||||||
|
|
||||||
|
# download command/URL (optional) --------------------------------------------------------------------------------------
|
||||||
|
download: |
|
||||||
|
from pycocotools.coco import COCO
|
||||||
|
from tqdm import tqdm
|
||||||
|
|
||||||
|
from utils.general import download, Path
|
||||||
|
|
||||||
|
# Make Directories
|
||||||
|
dir = Path('../datasets/objects365') # dataset directory
|
||||||
|
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)
|
||||||
|
|
||||||
|
# Download
|
||||||
|
url = "https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/"
|
||||||
|
download([url + 'zhiyuan_objv2_train.tar.gz'], dir=dir, delete=False) # annotations json
|
||||||
|
download([url + f for f in [f'patch{i}.tar.gz' for i in range(51)]], dir=dir / 'images' / 'train',
|
||||||
|
curl=True, delete=False, threads=8)
|
||||||
|
|
||||||
|
# Move
|
||||||
|
train = dir / 'images' / 'train'
|
||||||
|
for f in tqdm(train.rglob('*.jpg'), desc=f'Moving images'):
|
||||||
|
f.rename(train / f.name) # move to /images/train
|
||||||
|
|
||||||
|
# Labels
|
||||||
|
coco = COCO(dir / 'zhiyuan_objv2_train.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(dir / 'labels' / 'train' / 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)
|
||||||
|
x, y = x + w / 2, y + h / 2 # xy to center
|
||||||
|
file.write(f"{cid} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(e)
|
||||||
|
|
@ -0,0 +1,61 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
|
||||||
|
# Download command: bash data/scripts/get_argoverse_hd.sh
|
||||||
|
# Train command: python train.py --data argoverse_hd.yaml
|
||||||
|
# Default dataset location is next to YOLOv3:
|
||||||
|
# /parent_folder
|
||||||
|
# /argoverse
|
||||||
|
# /yolov3
|
||||||
|
|
||||||
|
# Download/unzip images
|
||||||
|
d='../argoverse/' # unzip directory
|
||||||
|
mkdir $d
|
||||||
|
url=https://argoverse-hd.s3.us-east-2.amazonaws.com/
|
||||||
|
f=Argoverse-HD-Full.zip
|
||||||
|
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background
|
||||||
|
wait # finish background tasks
|
||||||
|
|
||||||
|
cd ../argoverse/Argoverse-1.1/
|
||||||
|
ln -s tracking images
|
||||||
|
|
||||||
|
cd ../Argoverse-HD/annotations/
|
||||||
|
|
||||||
|
python3 - "$@" <<END
|
||||||
|
import json
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
annotation_files = ["train.json", "val.json"]
|
||||||
|
print("Converting annotations to YOLOv3 format...")
|
||||||
|
|
||||||
|
for val in annotation_files:
|
||||||
|
a = json.load(open(val, "rb"))
|
||||||
|
|
||||||
|
label_dict = {}
|
||||||
|
for annot in a['annotations']:
|
||||||
|
img_id = annot['image_id']
|
||||||
|
img_name = a['images'][img_id]['name']
|
||||||
|
img_label_name = 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. # offset and scale
|
||||||
|
y_center = (y_center + height / 2) / 1200. # offset and scale
|
||||||
|
width /= 1920. # scale
|
||||||
|
height /= 1200. # scale
|
||||||
|
|
||||||
|
img_dir = "./labels/" + a['seq_dirs'][a['images'][annot['image_id']]['sid']]
|
||||||
|
|
||||||
|
Path(img_dir).mkdir(parents=True, exist_ok=True)
|
||||||
|
if img_dir + "/" + img_label_name not in label_dict:
|
||||||
|
label_dict[img_dir + "/" + img_label_name] = []
|
||||||
|
|
||||||
|
label_dict[img_dir + "/" + img_label_name].append(f"{cls} {x_center} {y_center} {width} {height}\n")
|
||||||
|
|
||||||
|
for filename in label_dict:
|
||||||
|
with open(filename, "w") as file:
|
||||||
|
for string in label_dict[filename]:
|
||||||
|
file.write(string)
|
||||||
|
|
||||||
|
END
|
||||||
|
|
||||||
|
mv ./labels ../../Argoverse-1.1/
|
||||||
|
|
@ -0,0 +1,27 @@
|
||||||
|
#!/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 YOLOv3:
|
||||||
|
# /parent_folder
|
||||||
|
# /coco
|
||||||
|
# /yolov3
|
||||||
|
|
||||||
|
# Download/unzip labels
|
||||||
|
d='../' # unzip directory
|
||||||
|
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||||
|
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||||
|
echo 'Downloading' $url$f ' ...'
|
||||||
|
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||||
|
|
||||||
|
# Download/unzip images
|
||||||
|
d='../coco/images' # unzip directory
|
||||||
|
url=http://images.cocodataset.org/zips/
|
||||||
|
f1='train2017.zip' # 19G, 118k images
|
||||||
|
f2='val2017.zip' # 1G, 5k images
|
||||||
|
f3='test2017.zip' # 7G, 41k images (optional)
|
||||||
|
for f in $f1 $f2; do
|
||||||
|
echo 'Downloading' $url$f '...'
|
||||||
|
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||||
|
done
|
||||||
|
wait # finish background tasks
|
||||||
|
|
@ -0,0 +1,17 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128
|
||||||
|
# Download command: bash data/scripts/get_coco128.sh
|
||||||
|
# Train command: python train.py --data coco128.yaml
|
||||||
|
# Default dataset location is next to YOLOv3:
|
||||||
|
# /parent_folder
|
||||||
|
# /coco128
|
||||||
|
# /yolov3
|
||||||
|
|
||||||
|
# Download/unzip images and labels
|
||||||
|
d='../' # unzip directory
|
||||||
|
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||||
|
f='coco128.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||||
|
echo 'Downloading' $url$f ' ...'
|
||||||
|
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||||
|
|
||||||
|
wait # finish background tasks
|
||||||
|
|
@ -0,0 +1,116 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||||
|
# Download command: bash data/scripts/get_voc.sh
|
||||||
|
# Train command: python train.py --data voc.yaml
|
||||||
|
# Default dataset location is next to YOLOv3:
|
||||||
|
# /parent_folder
|
||||||
|
# /VOC
|
||||||
|
# /yolov3
|
||||||
|
|
||||||
|
start=$(date +%s)
|
||||||
|
mkdir -p ../tmp
|
||||||
|
cd ../tmp/
|
||||||
|
|
||||||
|
# Download/unzip images and labels
|
||||||
|
d='.' # unzip directory
|
||||||
|
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||||
|
f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
|
||||||
|
f2=VOCtest_06-Nov-2007.zip # 438MB, 4953 images
|
||||||
|
f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
|
||||||
|
for f in $f3 $f2 $f1; do
|
||||||
|
echo 'Downloading' $url$f '...'
|
||||||
|
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||||
|
done
|
||||||
|
wait # finish background tasks
|
||||||
|
|
||||||
|
end=$(date +%s)
|
||||||
|
runtime=$((end - start))
|
||||||
|
echo "Completed in" $runtime "seconds"
|
||||||
|
|
||||||
|
echo "Splitting dataset..."
|
||||||
|
python3 - "$@" <<END
|
||||||
|
import os
|
||||||
|
import xml.etree.ElementTree as ET
|
||||||
|
from os import getcwd
|
||||||
|
|
||||||
|
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_box(size, box):
|
||||||
|
dw = 1. / (size[0])
|
||||||
|
dh = 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
|
||||||
|
|
||||||
|
|
||||||
|
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_box((w, h), b)
|
||||||
|
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
|
||||||
|
|
||||||
|
|
||||||
|
cwd = 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' % (cwd, 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
|
||||||
|
|
||||||
|
mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val
|
||||||
|
mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val
|
||||||
|
|
||||||
|
python3 - "$@" <<END
|
||||||
|
import os
|
||||||
|
|
||||||
|
print(os.path.exists('../tmp/train.txt'))
|
||||||
|
with open('../tmp/train.txt', 'r') as f:
|
||||||
|
for line in f.readlines():
|
||||||
|
line = "/".join(line.split('/')[-5:]).strip()
|
||||||
|
if os.path.exists("../" + line):
|
||||||
|
os.system("cp ../" + line + " ../VOC/images/train")
|
||||||
|
|
||||||
|
line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
|
||||||
|
if os.path.exists("../" + line):
|
||||||
|
os.system("cp ../" + line + " ../VOC/labels/train")
|
||||||
|
|
||||||
|
print(os.path.exists('../tmp/2007_test.txt'))
|
||||||
|
with open('../tmp/2007_test.txt', 'r') as f:
|
||||||
|
for line in f.readlines():
|
||||||
|
line = "/".join(line.split('/')[-5:]).strip()
|
||||||
|
if os.path.exists("../" + line):
|
||||||
|
os.system("cp ../" + line + " ../VOC/images/val")
|
||||||
|
|
||||||
|
line = line.replace('JPEGImages', 'labels').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."
|
||||||
|
|
@ -0,0 +1,21 @@
|
||||||
|
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||||
|
# Train command: python train.py --data voc.yaml
|
||||||
|
# Default dataset location is next to YOLOv3:
|
||||||
|
# /parent_folder
|
||||||
|
# /VOC
|
||||||
|
# /yolov3
|
||||||
|
|
||||||
|
|
||||||
|
# download command/URL (optional)
|
||||||
|
download: bash data/scripts/get_voc.sh
|
||||||
|
|
||||||
|
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||||
|
train: ../VOC/images/train/ # 16551 images
|
||||||
|
val: ../VOC/images/val/ # 4952 images
|
||||||
|
|
||||||
|
# number of classes
|
||||||
|
nc: 20
|
||||||
|
|
||||||
|
# class names
|
||||||
|
names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
|
||||||
|
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ]
|
||||||
Loading…
Reference in New Issue