์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

Roboflow ์œ ๋‹ˆ๋ฒ„์Šค ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ

The Roboflow ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ์ปดํ“จํ„ฐ ๋น„์ „ ๋ถ„์•ผ์˜ ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™”์™€ ๊ด€๋ จ๋œ ์ž‘์—…์„ ์œ„ํ•ด ํŠน๋ณ„ํžˆ ๋งž์ถคํ™”๋œ ์ด๋ฏธ์ง€ ๋ชจ์Œ์ž…๋‹ˆ๋‹ค. ์ด ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ํŒจํ‚ค์ง€ ์‹๋ณ„, ๋ถ„๋ฅ˜ ๋ฐ ์ฒ˜๋ฆฌ์™€ ๊ด€๋ จ๋œ ํ”„๋กœ์ ํŠธ๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ์—ฐ๊ตฌ์ž, ๊ฐœ๋ฐœ์ž ๋ฐ ์• ํ˜ธ๊ฐ€๋ฅผ ๋•๊ธฐ ์œ„ํ•ด ๊ณ ์•ˆ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.

๋‹ค์–‘ํ•œ ์ƒํ™ฉ๊ณผ ํ™˜๊ฒฝ์—์„œ ๋‹ค์–‘ํ•œ ํŒจํ‚ค์ง€๋ฅผ ๋ณด์—ฌ์ฃผ๋Š” ๋‹ค์–‘ํ•œ ์ด๋ฏธ์ง€ ์„ธํŠธ๊ฐ€ ํฌํ•จ๋œ ์ด ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ์„ธ๋ถ„ํ™” ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š” ๋ฐ ์œ ์šฉํ•œ ๋ฆฌ์†Œ์Šค๋กœ ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค. ๋ฌผ๋ฅ˜, ์ฐฝ๊ณ  ์ž๋™ํ™” ๋˜๋Š” ์ •๋ฐ€ํ•œ ํŒจํ‚ค์ง€ ๋ถ„์„์ด ํ•„์š”ํ•œ ๋ชจ๋“  ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์—์„œ ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ์ปดํ“จํ„ฐ ๋น„์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋Œ€์ƒ์— ๋งž๋Š” ํฌ๊ด„์ ์ธ ์ด๋ฏธ์ง€ ์„ธํŠธ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ์„ธํŠธ ๊ตฌ์กฐ

ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ์˜ ๋ฐ์ดํ„ฐ ๋ถ„ํฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๊ตฌ์กฐํ™”๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค:

  • ํŠธ๋ ˆ์ด๋‹ ์„ธํŠธ: ํ•ด๋‹น ์ฃผ์„๊ณผ ํ•จ๊ป˜ 1920๊ฐœ์˜ ์ด๋ฏธ์ง€๊ฐ€ ํฌํ•จ๋ฉ๋‹ˆ๋‹ค.
  • ํ…Œ์ŠคํŠธ ์„ธํŠธ: 89๊ฐœ์˜ ์ด๋ฏธ์ง€๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ๊ฐ ์ด๋ฏธ์ง€๋Š” ๊ฐ๊ฐ์˜ ์ฃผ์„๊ณผ ์ง์„ ์ด๋ฃน๋‹ˆ๋‹ค.
  • ์œ ํšจ์„ฑ ๊ฒ€์‚ฌ ์„ธํŠธ: ๊ฐ๊ฐ ํ•ด๋‹น ์ฃผ์„์ด ์žˆ๋Š” 188๊ฐœ์˜ ์ด๋ฏธ์ง€๋กœ ๊ตฌ์„ฑ๋ฉ๋‹ˆ๋‹ค.

์• ํ”Œ๋ฆฌ์ผ€์ด์…˜

ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ์„ธํŠธ๋ฅผ ํ†ตํ•ด ์ด‰์ง„๋˜๋Š” ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™”๋Š” ๋ฌผ๋ฅ˜ ์ตœ์ ํ™”, ๋ผ์ŠคํŠธ ๋งˆ์ผ ๋ฐฐ์†ก ๊ฐœ์„ , ์ œ์กฐ ํ’ˆ์งˆ ๊ด€๋ฆฌ ๊ฐœ์„ , ์Šค๋งˆํŠธ ์‹œํ‹ฐ ์†”๋ฃจ์…˜์— ๊ธฐ์—ฌํ•˜๋Š” ๋ฐ ๋งค์šฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ์ „์ž์ƒ๊ฑฐ๋ž˜์—์„œ ๋ณด์•ˆ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ์ด ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ๋‹ค์–‘ํ•˜๊ณ  ํšจ์œจ์ ์ธ ํŒจํ‚ค์ง€ ๋ถ„์„ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์œ„ํ•œ ์ปดํ“จํ„ฐ ๋น„์ „์˜ ํ˜์‹ ์„ ์ด‰์ง„ํ•˜๋Š” ํ•ต์‹ฌ ๋ฆฌ์†Œ์Šค์ž…๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ์„ธํŠธ YAML

๋ฐ์ดํ„ฐ ์„ธํŠธ ๊ตฌ์„ฑ์„ ์ •์˜ํ•˜๋Š” ๋ฐ๋Š” YAML(๋˜ ๋‹ค๋ฅธ ๋งˆํฌ์—… ์–ธ์–ด) ํŒŒ์ผ์ด ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์—๋Š” ๋ฐ์ดํ„ฐ ์„ธํŠธ์˜ ๊ฒฝ๋กœ, ํด๋ž˜์Šค ๋ฐ ๊ธฐํƒ€ ๊ด€๋ จ ์ •๋ณด์— ๋Œ€ํ•œ ์ •๋ณด๊ฐ€ ํฌํ•จ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ์˜ ๊ฒฝ์šฐ, ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ์˜ package-seg.yaml ํŒŒ์ผ์€ ๋‹ค์Œ ์œ„์น˜์—์„œ ์œ ์ง€๋ฉ๋‹ˆ๋‹ค. https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml.

ultralytics/cfg/๋ฐ์ดํ„ฐ์„ธํŠธ/ํŒจํ‚ค์ง€-seg.yaml

# Ultralytics YOLO ๐Ÿš€, AGPL-3.0 license
# Package-seg dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/
# Example usage: yolo train data=package-seg.yaml
# parent
# โ”œโ”€โ”€ ultralytics
# โ””โ”€โ”€ datasets
#     โ””โ”€โ”€ package-seg  โ† downloads here (102 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/package-seg # dataset root dir
train: images/train # train images (relative to 'path') 1920 images
val: images/val # val images (relative to 'path') 89 images
test: test/images # test images (relative to 'path') 188 images

# Classes
names:
  0: package

# Download script/URL (optional)
download: https://ultralytics.com/assets/package-seg.zip

์‚ฌ์šฉ๋ฒ•

์ด๋ฏธ์ง€ ํฌ๊ธฐ๊ฐ€ 640์ธ ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ์— ๋Œ€ํ•ด 100๊ฐœ์˜ ์—ํฌํฌ์— ๋Œ€ํ•œ Ultralytics YOLOv8n ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๋ ค๋ฉด ๋‹ค์Œ ์ฝ”๋“œ ์กฐ๊ฐ์„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ์ธ์ˆ˜์˜ ์ „์ฒด ๋ชฉ๋ก์€ ๋ชจ๋ธ ํ•™์Šต ํŽ˜์ด์ง€๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”.

์—ด์ฐจ ์˜ˆ์‹œ

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
# Start training from a pretrained *.pt model
yolo segment train data=package-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640

์ƒ˜ํ”Œ ๋ฐ์ดํ„ฐ ๋ฐ ์ฃผ์„

ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ๋‹ค์–‘ํ•œ ๊ด€์ ์—์„œ ์บก์ฒ˜ํ•œ ๋‹ค์–‘ํ•œ ์ด๋ฏธ์ง€์™€ ๋™์˜์ƒ ๋ชจ์Œ์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ์•„๋ž˜๋Š” ๋ฐ์ดํ„ฐ ์„ธํŠธ์˜ ๋ฐ์ดํ„ฐ ์ธ์Šคํ„ด์Šค์™€ ๊ฐ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์ฃผ์„์ž…๋‹ˆ๋‹ค:

๋ฐ์ดํ„ฐ ์„ธํŠธ ์ƒ˜ํ”Œ ์ด๋ฏธ์ง€

  • ์ด ์ด๋ฏธ์ง€๋Š” ์ธ์‹๋œ ๋ฌผ์ฒด์˜ ์œค๊ณฝ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๋งˆ์Šคํฌ๊ฐ€ ์žˆ๋Š” ์ฃผ์„์ด ๋‹ฌ๋ฆฐ ๊ฒฝ๊ณ„ ์ƒ์ž๊ฐ€ ํŠน์ง•์ธ ์ด๋ฏธ์ง€ ๋ฌผ์ฒด ๊ฐ์ง€ ์ธ์Šคํ„ด์Šค๋ฅผ ํ‘œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ฐ์ดํ„ฐ ์„ธํŠธ์—๋Š” ๋‹ค์–‘ํ•œ ์œ„์น˜, ํ™˜๊ฒฝ, ๋ฐ€๋„์—์„œ ์ดฌ์˜๋œ ๋‹ค์–‘ํ•œ ์ด๋ฏธ์ง€ ๋ชจ์Œ์ด ํฌํ•จ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ์ด ์ž‘์—…์— ํŠนํ™”๋œ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•œ ํฌ๊ด„์ ์ธ ๋ฆฌ์†Œ์Šค ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค.
  • ์ด ์˜ˆ๋Š” ๋“œ๋ก ๊ณผ ๊ด€๋ จ๋œ ์ปดํ“จํ„ฐ ๋น„์ „ ์ž‘์—…์—์„œ ๊ณ ํ’ˆ์งˆ ์„ผ์„œ ๋ฐ์ดํ„ฐ์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๋ฉด์„œ VisDrone ๋ฐ์ดํ„ฐ ์„ธํŠธ์— ์กด์žฌํ•˜๋Š” ๋‹ค์–‘์„ฑ๊ณผ ๋ณต์žก์„ฑ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค.

์ธ์šฉ ๋ฐ ๊ฐ์‚ฌ

ํฌ๋ž™ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ๋ฅผ ์—ฐ๊ตฌ ๋˜๋Š” ๊ฐœ๋ฐœ ์ด๋‹ˆ์…”ํ‹ฐ๋ธŒ์— ํ†ตํ•ฉํ•˜๋Š” ๊ฒฝ์šฐ ๋‹ค์Œ ๋…ผ๋ฌธ์„ ์ธ์šฉํ•ด ์ฃผ์„ธ์š”:

@misc{ factory_package_dataset,
    title = { factory_package Dataset },
    type = { Open Source Dataset },
    author = { factorypackage },
    howpublished = { \url{ https://universe.roboflow.com/factorypackage/factory_package } },
    url = { https://universe.roboflow.com/factorypackage/factory_package },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2024 },
    month = { jan },
    note = { visited on 2024-01-24 },
}

๋ฌผ๋ฅ˜ ๋ฐ ์—ฐ๊ตฌ ํ”„๋กœ์ ํŠธ์— ๊ท€์ค‘ํ•œ ์ž์‚ฐ์ธ ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ์„ธํŠธ๋ฅผ ๋งŒ๋“ค๊ณ  ์œ ์ง€ ๊ด€๋ฆฌํ•˜๋Š” Roboflow ํŒ€์˜ ๋…ธ๊ณ ์— ๊ฐ์‚ฌ์˜ ๋ง์”€์„ ์ „ํ•ฉ๋‹ˆ๋‹ค. ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ์™€ ์ œ์ž‘์ž์— ๋Œ€ํ•œ ์ž์„ธํ•œ ๋‚ด์šฉ์€ ํŒจํ‚ค์ง€ ์„ธ๋ถ„ํ™” ๋ฐ์ดํ„ฐ ์„ธํŠธ ํŽ˜์ด์ง€๋ฅผ ์ฐธ์กฐํ•˜์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค.



์ƒ์„ฑ 2024-01-25, ์—…๋ฐ์ดํŠธ 2024-05-18
์ž‘์„ฑ์ž: glenn-jocher (2), RizwanMunawar (1)

๋Œ“๊ธ€