์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

์ง€์›๋˜๋Š” ๋ชจ๋ธ Ultralytics

Ultralytics' ๋ชจ๋ธ ๋ฌธ์„œ์— ์˜ค์‹  ๊ฒƒ์„ ํ™˜์˜ํ•ฉ๋‹ˆ๋‹ค! ์ €ํฌ๋Š” ๋ฌผ์ฒด ๊ฐ์ง€, ์ธ์Šคํ„ด์Šค ๋ถ„ํ• , ์ด๋ฏธ์ง€ ๋ถ„๋ฅ˜, ํฌ์ฆˆ ์ถ”์ •, ๋‹ค์ค‘ ๋ฌผ์ฒด ์ถ”์ ๊ณผ ๊ฐ™์€ ํŠน์ • ์ž‘์—…์— ๋งž๊ฒŒ ์กฐ์ •๋œ ๋‹ค์–‘ํ•œ ๋ชจ๋ธ์— ๋Œ€ํ•œ ์ง€์›์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๋ชจ๋ธ ์•„ํ‚คํ…์ฒ˜๋ฅผ Ultralytics ์— ๊ธฐ์—ฌํ•˜๊ณ  ์‹ถ์œผ์‹œ๋‹ค๋ฉด ๊ธฐ์—ฌ ๊ฐ€์ด๋“œ๋ฅผ ํ™•์ธํ•˜์„ธ์š”.

๋‹ค์Œ์€ ์ง€์›๋˜๋Š” ์ฃผ์š” ๋ชจ๋ธ ์ค‘ ์ผ๋ถ€์ž…๋‹ˆ๋‹ค:

  1. YOLOv3: ํšจ์œจ์ ์ธ ์‹ค์‹œ๊ฐ„ ๊ฐ์ฒด ๊ฐ์ง€ ๊ธฐ๋Šฅ์œผ๋กœ ์œ ๋ช…ํ•œ ์กฐ์…‰ ๋ ˆ๋“œ๋ชฌ์ด ๊ฐœ๋ฐœํ•œ YOLO ๋ชจ๋ธ ์ œํ’ˆ๊ตฐ์˜ ์„ธ ๋ฒˆ์งธ ๋ฐ˜๋ณต ๋ฒ„์ „์ž…๋‹ˆ๋‹ค.
  2. YOLOv4: ์•Œ๋ ‰์„ธ์ด ๋ณดํ์ฝ”๋ธŒ์Šคํ‚ค๊ฐ€ 2020๋…„์— ๋ฐœํ‘œํ•œ YOLOv3์˜ ๋‹คํฌ๋„ท ๋„ค์ดํ‹ฐ๋ธŒ ์—…๋ฐ์ดํŠธ์ž…๋‹ˆ๋‹ค.
  3. YOLOv5: YOLO ์•„ํ‚คํ…์ฒ˜์˜ ๊ฐœ์„ ๋œ ๋ฒ„์ „์œผ๋กœ ์ด์ „ ๋ฒ„์ „์— ๋น„ํ•ด ์„ฑ๋Šฅ๊ณผ ์†๋„ ์ ˆ์ถฉ์ ์„ ์ œ๊ณตํ•˜๋Š” Ultralytics.
  4. YOLOv6: ๋ฉ”์ดํˆฌ์•ˆ์—์„œ 2022๋…„์— ์ถœ์‹œํ–ˆ์œผ๋ฉฐ, ๋ฉ”์ดํˆฌ์•ˆ์˜ ๋งŽ์€ ์ž์œจ ๋ฐฐ์†ก ๋กœ๋ด‡์— ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
  5. YOLOv7: ์—…๋ฐ์ดํŠธ๋จ YOLO YOLOv4์˜ ์ €์ž๊ฐ€ 2022๋…„์— ์ถœ์‹œํ•œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค.
  6. YOLOv8 ์‹ ๊ทœ ๐Ÿš€: ์ธ์Šคํ„ด์Šค ์„ธ๋ถ„ํ™”, ํฌ์ฆˆ/ํ‚คํฌ์ธํŠธ ์ถ”์ •, ๋ถ„๋ฅ˜ ๋“ฑ ํ–ฅ์ƒ๋œ ๊ธฐ๋Šฅ์„ ๊ฐ–์ถ˜ YOLO ์ œํ’ˆ๊ตฐ์˜ ์ตœ์‹  ๋ฒ„์ „์ž…๋‹ˆ๋‹ค.
  7. YOLOv9: ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ฐ€๋Šฅํ•œ ๊ธฐ์šธ๊ธฐ ์ •๋ณด๋ฅผ ๊ตฌํ˜„ํ•˜๋Š” Ultralytics YOLOv5 ํ”„๋กœ๊ทธ๋ž˜๋จธ๋ธ” ๊ทธ๋ผ๋ฐ์ด์…˜ ์ •๋ณด(PGI)๋ฅผ ๊ตฌํ˜„ํ•˜๋Š” ์ฝ”๋“œ๋ฒ ์ด์Šค.
  8. YOLOv10: ์นญํ™”๋Œ€ํ•™๊ต์—์„œ ์ œ๊ณต, NMS๊ฐ€ ํ•„์š” ์—†๋Š” ํŠธ๋ ˆ์ด๋‹๊ณผ ํšจ์œจ์„ฑ ๋ฐ ์ •ํ™•๋„ ์ค‘์‹ฌ ์•„ํ‚คํ…์ฒ˜๋กœ ์ตœ์ฒจ๋‹จ ์„ฑ๋Šฅ๊ณผ ์ง€์—ฐ ์‹œ๊ฐ„์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค.
  9. ๋ฌด์—‡์ด๋“  ์„ธ๊ทธ๋จผํŠธ ๋ชจ๋ธ (SAM): ๋ฉ”ํƒ€์˜ ์„ธ๊ทธ๋จผํŠธ ์• ๋‹ˆ์”ฝ ๋ชจ๋ธ (SAM).
  10. ๋ชจ๋ฐ”์ผ ์„ธ๊ทธ๋จผํŠธ ์• ๋‹ˆ์”ฝ ๋ชจ๋ธ (MobileSAM)MobileSAM , ๊ฒฝํฌ๋Œ€ํ•™๊ต ๋ชจ๋ฐ”์ผ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜.
  11. ๋น ๋ฅธ ์„ธ๊ทธ๋จผํŠธ ๋ฌด์—‡์ด๋“  ๋ชจ๋ธ (FastSAM)FastSAM ์ค‘๊ตญ๊ณผํ•™์› ์ž๋™ํ™”์—ฐ๊ตฌ์†Œ ์ด๋ฏธ์ง€ ๋ฐ ๋น„๋””์˜ค ๋ถ„์„ ๊ทธ๋ฃน์—์„œ ์ œ๊ณต.
  12. YOLO-NAS: YOLO ์‹ ๊ฒฝ ์•„ํ‚คํ…์ฒ˜ ๊ฒ€์ƒ‰(NAS) ๋ชจ๋ธ.
  13. ์‹ค์‹œ๊ฐ„ ๊ฐ์ง€ ํŠธ๋žœ์Šคํฌ๋จธ (RT-DETR): ๋ฐ”์ด๋‘์˜ PaddlePaddle ์‹ค์‹œ๊ฐ„ ๊ฐ์ง€ ํŠธ๋žœ์Šคํฌ๋จธ (RT-DETR) ๋ชจ๋ธ.
  14. YOLO-์„ธ๊ณ„: ์‹ค์‹œ๊ฐ„ ๊ฐœ๋ฐฉํ˜• ์–ดํœ˜ ๊ฐœ์ฒด ๊ฐ์ง€ ๋ชจ๋ธ: Tencent AI Lab.



Watch: ๋ช‡ ์ค„์˜ ์ฝ”๋“œ๋งŒ์œผ๋กœ Ultralytics YOLO ๋ชจ๋ธ์„ ์‹คํ–‰ํ•˜์„ธ์š”.

์‹œ์ž‘ํ•˜๊ธฐ: ์‚ฌ์šฉ ์˜ˆ์‹œ

์ด ์˜ˆ๋Š” ๊ฐ„๋‹จํ•œ YOLO ํ•™์Šต ๋ฐ ์ถ”๋ก  ์˜ˆ์ œ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ชจ๋“œ ๋ฐ ๊ธฐํƒ€ ๋ชจ๋“œ์— ๋Œ€ํ•œ ์ „์ฒด ์„ค๋ช…์„œ๋Š” ์˜ˆ์ธก, ํ•™์Šต, Val ๋ฐ ๋‚ด๋ณด๋‚ด๊ธฐ ๋ฌธ์„œ ํŽ˜์ด์ง€๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”.

์•„๋ž˜ ์˜ˆ์‹œ๋Š” YOLOv8 ๊ฐ์ฒด ๊ฐ์ง€ ๋ชจ๋ธ์— ๋Œ€ํ•œ ์˜ˆ์‹œ์ž…๋‹ˆ๋‹ค. ์ถ”๊ฐ€๋กœ ์ง€์›๋˜๋Š” ์ž‘์—…์€ ์„ธ๊ทธ๋จผํŠธ, ๋ถ„๋ฅ˜ ๋ฐ ํฌ์ฆˆ ๋ฌธ์„œ๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”.

์˜ˆ

PyTorch ์‚ฌ์ „ ๊ต์œก *.pt ๋ชจ๋ธ ๋ฐ ๊ตฌ์„ฑ *.yaml ํŒŒ์ผ์„ YOLO(), SAM(), NAS() ๊ทธ๋ฆฌ๊ณ  RTDETR() ํด๋ž˜์Šค๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ Python ์—์„œ ๋ชจ๋ธ ์ธ์Šคํ„ด์Šค๋ฅผ ์ƒ์„ฑํ•ฉ๋‹ˆ๋‹ค:

from ultralytics import YOLO

# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Display model information (optional)
model.info()

# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")

CLI ๋ช…๋ น์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ชจ๋ธ์„ ์ง์ ‘ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

# Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640

# Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image
yolo predict model=yolov8n.pt source=path/to/bus.jpg

์ƒˆ๋กœ์šด ๋ชจ๋ธ ๊ธฐ์—ฌํ•˜๊ธฐ

Ultralytics ์— ๋ชจ๋ธ ๊ธฐ๊ณ ์— ๊ด€์‹ฌ์ด ์žˆ์œผ์‹ ๊ฐ€์š”? ์ข‹์•„์š”! ๋ชจ๋ธ ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ํ™•์žฅํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋Š” ์–ธ์ œ๋‚˜ ์—ด๋ ค ์žˆ์Šต๋‹ˆ๋‹ค.

  1. ๋ฆฌํฌ์ง€ํ† ๋ฆฌ ํฌํฌ: Ultralytics GitHub ๋ฆฌํฌ์ง€ํ† ๋ฆฌ๋ฅผ ํฌํฌํ•˜์—ฌ ์‹œ์ž‘ํ•˜์„ธ์š”.

  2. ํฌํฌ ๋ณต์ œ: ํฌํฌ๋ฅผ ๋กœ์ปฌ ๋จธ์‹ ์— ๋ณต์ œํ•˜๊ณ  ์ž‘์—…ํ•  ์ƒˆ ๋ธŒ๋žœ์น˜๋ฅผ ๋งŒ๋“ญ๋‹ˆ๋‹ค.

  3. ๋ชจ๋ธ ๊ตฌํ˜„ํ•˜๊ธฐ: ๊ธฐ์—ฌ ๊ฐ€์ด๋“œ์— ์ œ๊ณต๋œ ์ฝ”๋”ฉ ํ‘œ์ค€๊ณผ ๊ฐ€์ด๋“œ๋ผ์ธ์— ๋”ฐ๋ผ ๋ชจ๋ธ์„ ์ถ”๊ฐ€ํ•˜์„ธ์š”.

  4. ์ฒ ์ €ํ•˜๊ฒŒ ํ…Œ์ŠคํŠธํ•˜์„ธ์š”: ๋ชจ๋ธ์„ ๊ฐœ๋ณ„์ ์œผ๋กœ ๋˜๋Š” ํŒŒ์ดํ”„๋ผ์ธ์˜ ์ผ๋ถ€๋กœ ์—„๊ฒฉํ•˜๊ฒŒ ํ…Œ์ŠคํŠธํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

  5. ํ’€ ๋ฆฌํ€˜์ŠคํŠธ๋ฅผ ๋งŒ๋“ญ๋‹ˆ๋‹ค: ๋ชจ๋ธ์— ๋งŒ์กฑํ•˜๋ฉด ๋ฉ”์ธ ๋ฆฌํฌ์ง€ํ† ๋ฆฌ์— ํ’€ ๋ฆฌํ€˜์ŠคํŠธ๋ฅผ ๋งŒ๋“ค์–ด ๊ฒ€ํ† ๋ฅผ ์š”์ฒญํ•˜์„ธ์š”.

  6. ์ฝ”๋“œ ๊ฒ€ํ†  ๋ฐ ๋ณ‘ํ•ฉ: ๊ฒ€ํ†  ํ›„ ๋ชจ๋ธ์ด ๊ธฐ์ค€์„ ์ถฉ์กฑํ•˜๋ฉด ๊ธฐ๋ณธ ๋ฆฌํฌ์ง€ํ† ๋ฆฌ์— ๋ณ‘ํ•ฉ๋ฉ๋‹ˆ๋‹ค.

์ž์„ธํ•œ ๋‹จ๊ณ„๋Š” ๊ธฐ์—ฌ ๊ฐ€์ด๋“œ๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”.



Created 2023-11-12, Updated 2024-06-10
Authors: glenn-jocher (11), Laughing-q (1)

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