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lvis.yaml
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lvis.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
# LVIS dataset http://www.lvisdataset.org by Facebook AI Research.
# Documentation: https://docs.ultralytics.com/datasets/detect/lvis/
# Example usage: yolo train data=lvis.yaml
# parent
# ├── ultralytics
# └── datasets
# └── lvis ← 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: /Users/lorenaderezanin/PycharmProjects/WhatIsPants/datasets/lvis_pants # dataset root dir
train: images/train2017 # train images (relative to 'path') 100170 images
val: images/val2017 # val images (relative to 'path') 19809 images
#minival: minival.txt # minval images (relative to 'path') 5000 images
tensorboard: True
names:
0: this is pants
# Download script/URL (optional)
download: |
from ultralytics.utils.downloads import download
from pathlib import Path
# Download labels
dir = Path(yaml['path']) # dataset root dir
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
urls = [url + 'lvis-labels-segments.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)