์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

๋ฐ”์ด๋‘( RT-DETR): ๋น„์ „ ํŠธ๋žœ์Šคํฌ๋จธ ๊ธฐ๋ฐ˜ ์‹ค์‹œ๊ฐ„ ๋ฌผ์ฒด ๊ฐ์ง€๊ธฐ

๊ฐœ์š”

๋ฐ”์ด๋‘๊ฐ€ ๊ฐœ๋ฐœํ•œ ์‹ค์‹œ๊ฐ„ ๊ฐ์ง€ ํŠธ๋žœ์Šคํฌ๋จธ(RT-DETR)๋Š” ๋†’์€ ์ •ํ™•๋„๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ ์‹ค์‹œ๊ฐ„ ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•˜๋Š” ์ตœ์ฒจ๋‹จ ์—”๋“œํˆฌ์—”๋“œ ๊ฐ์ฒด ๊ฐ์ง€๊ธฐ์ž…๋‹ˆ๋‹ค. ์‹ค์‹œ๊ฐ„ ์†๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ปจ๋ฒ„ํŒ… ๊ธฐ๋ฐ˜ ๋ฐฑ๋ณธ๊ณผ ํšจ์œจ์ ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ธ์ฝ”๋”๋ฅผ ๋„์ž…ํ•˜๋Š” ํ•œํŽธ, NMS๊ฐ€ ํ•„์š” ์—†๋Š” ํ”„๋ ˆ์ž„์›Œํฌ์ธ DETR์˜ ์•„์ด๋””์–ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•ฉ๋‹ˆ๋‹ค. RT-DETR )๋Š” ๊ทœ๋ชจ ๋‚ด ์ƒํ˜ธ ์ž‘์šฉ๊ณผ ๊ทœ๋ชจ ๊ฐ„ ์œตํ•ฉ์„ ๋ถ„๋ฆฌํ•˜์—ฌ ๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ๊ธฐ๋Šฅ์„ ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ชจ๋ธ์€ ์ ์‘์„ฑ์ด ๋›ฐ์–ด๋‚˜ ์žฌํ•™์Šต ์—†์ด ๋‹ค์–‘ํ•œ ๋””์ฝ”๋” ๋ ˆ์ด์–ด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”๋ก  ์†๋„๋ฅผ ์œ ์—ฐํ•˜๊ฒŒ ์กฐ์ •ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. RT-DETR ์€ CUDA ๊ณผ ๊ฐ™์€ ๊ฐ€์† ๋ฐฑ์—”๋“œ์—์„œ ๋‹ค๋ฅธ ๋งŽ์€ ์‹ค์‹œ๊ฐ„ ๊ฐ์ฒด ๊ฐ์ง€๊ธฐ๋ณด๋‹ค ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ฐœํœ˜ํ•ฉ๋‹ˆ๋‹ค( TensorRT).



Watch: ์‹ค์‹œ๊ฐ„ ๊ฐ์ง€ ํŠธ๋žœ์Šคํฌ๋จธ (RT-DETR)

๋ชจ๋ธ ์˜ˆ์‹œ ์ด๋ฏธ์ง€ ๋ฐ”์ด๋‘์˜ ๊ฐœ์š” RT-DETR. RT-DETR ๋ชจ๋ธ ์•„ํ‚คํ…์ฒ˜ ๋‹ค์ด์–ด๊ทธ๋žจ์€ ์ธ์ฝ”๋”์— ๋Œ€ํ•œ ์ž…๋ ฅ์œผ๋กœ ๋ฐฑ๋ณธ์˜ ๋งˆ์ง€๋ง‰ ์„ธ ๋‹จ๊ณ„ {S3, S4, S5}๋ฅผ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ํšจ์œจ์ ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ธ์ฝ”๋”๋Š” ์Šค์ผ€์ผ ๋‚ด ํŠน์ง• ์ƒํ˜ธ ์ž‘์šฉ(AIFI)๊ณผ ์Šค์ผ€์ผ ๊ฐ„ ํŠน์ง• ์œตํ•ฉ ๋ชจ๋“ˆ(CCFM)์„ ํ†ตํ•ด ๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ํŠน์ง•์„ ์ด๋ฏธ์ง€ ํŠน์ง• ์‹œํ€€์Šค๋กœ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค. IoU ์ธ์‹ ์ฟผ๋ฆฌ ์„ ํƒ์€ ๋””์ฝ”๋”์˜ ์ดˆ๊ธฐ ์˜ค๋ธŒ์ ํŠธ ์ฟผ๋ฆฌ๋กœ ์‚ฌ์šฉํ•  ๊ณ ์ •๋œ ์ˆ˜์˜ ์ด๋ฏธ์ง€ ํŠน์ง•์„ ์„ ํƒํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ณด์กฐ ์˜ˆ์ธก ํ—ค๋“œ๊ฐ€ ์žˆ๋Š” ๋””์ฝ”๋”๋Š” ๊ฐ์ฒด ์ฟผ๋ฆฌ๋ฅผ ๋ฐ˜๋ณต์ ์œผ๋กœ ์ตœ์ ํ™”ํ•˜์—ฌ ๋ฐ•์Šค ๋ฐ ์‹ ๋ขฐ ์ ์ˆ˜(์ถœ์ฒ˜).

์ฃผ์š” ๊ธฐ๋Šฅ

  • ํšจ์œจ์ ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ธ์ฝ”๋”: ๋ฐ”์ด๋‘์˜ RT-DETR ๋Š” ํšจ์œจ์ ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ธ์ฝ”๋”๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์Šค์ผ€์ผ ๋‚ด ์ƒํ˜ธ ์ž‘์šฉ๊ณผ ์Šค์ผ€์ผ ๊ฐ„ ์œตํ•ฉ์„ ๋ถ„๋ฆฌํ•˜์—ฌ ๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ๊ธฐ๋Šฅ์„ ์ฒ˜๋ฆฌํ•ฉ๋‹ˆ๋‹ค. ์ด ๋…ํŠนํ•œ ๋น„์ „ ํŠธ๋žœ์Šคํฌ๋จธ ๊ธฐ๋ฐ˜ ์„ค๊ณ„๋Š” ๊ณ„์‚ฐ ๋น„์šฉ์„ ์ ˆ๊ฐํ•˜๊ณ  ์‹ค์‹œ๊ฐ„ ๋ฌผ์ฒด ๊ฐ์ง€๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค.
  • IoU ์ธ์‹ ์ฟผ๋ฆฌ ์„ ํƒ: Baidu์˜ RT-DETR ๋Š” IoU ์ธ์‹ ์ฟผ๋ฆฌ ์„ ํƒ์„ ํ™œ์šฉํ•˜์—ฌ ๊ฐ์ฒด ์ฟผ๋ฆฌ ์ดˆ๊ธฐํ™”๋ฅผ ๊ฐœ์„ ํ•ฉ๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ชจ๋ธ์ด ์žฅ๋ฉด์—์„œ ๊ฐ€์žฅ ๊ด€๋ จ์„ฑ์ด ๋†’์€ ๊ฐ์ฒด์— ์ง‘์ค‘ํ•˜์—ฌ ๊ฐ์ง€ ์ •ํ™•๋„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
  • ์ ์‘ํ˜• ์ถ”๋ก  ์†๋„: ๋ฐ”์ด๋‘์˜ RT-DETR ๋Š” ์žฌ๊ต์œก ์—†์ด๋„ ๋‹ค์–‘ํ•œ ๋””์ฝ”๋” ๋ ˆ์ด์–ด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”๋ก  ์†๋„๋ฅผ ์œ ์—ฐํ•˜๊ฒŒ ์กฐ์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์›ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ์‘์„ฑ ๋•๋ถ„์— ๋‹ค์–‘ํ•œ ์‹ค์‹œ๊ฐ„ ๊ฐ์ฒด ๊ฐ์ง€ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ์‹ค์ œ ์ ์šฉ์ด ์šฉ์ดํ•ฉ๋‹ˆ๋‹ค.

์‚ฌ์ „ ํ•™์Šต๋œ ๋ชจ๋ธ

Ultralytics Python API๋Š” ๋‹ค์–‘ํ•œ ์Šค์ผ€์ผ๋กœ ์‚ฌ์ „ ํ•™์Šต๋œ PaddlePaddle RT-DETR ๋ชจ๋ธ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค:

  • RT-DETR-L: COCO val2017์—์„œ 53.0% AP, T4์—์„œ 114 FPS GPU
  • RT-DETR-X: COCO val2017์—์„œ 54.8% AP, T4์—์„œ 74 FPS GPU

์‚ฌ์šฉ ์˜ˆ

์ด ์˜ˆ๋Š” ๊ฐ„๋‹จํ•œ RT-DETR ํ•™์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”.

์˜ˆ

from ultralytics import RTDETR

# Load a COCO-pretrained RT-DETR-l model
model = RTDETR("rtdetr-l.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 RT-DETR-l model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
# Load a COCO-pretrained RT-DETR-l model and train it on the COCO8 example dataset for 100 epochs
yolo train model=rtdetr-l.pt data=coco8.yaml epochs=100 imgsz=640

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

์ง€์›๋˜๋Š” ์ž‘์—… ๋ฐ ๋ชจ๋“œ

์ด ํ‘œ์—๋Š” ๋ชจ๋ธ ์œ ํ˜•, ์‚ฌ์ „ ํ•™์Šต๋œ ํŠน์ • ๊ฐ€์ค‘์น˜, ๊ฐ ๋ชจ๋ธ์ด ์ง€์›ํ•˜๋Š” ์ž‘์—… ๋ฐ ์ง€์›๋˜๋Š” ๋‹ค์–‘ํ•œ ๋ชจ๋“œ(ํ•™์Šต, Val, ์˜ˆ์ธก, ๋‚ด๋ณด๋‚ด๊ธฐ)๊ฐ€ โœ… ์ด๋ชจํ‹ฐ์ฝ˜์œผ๋กœ ํ‘œ์‹œ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.

๋ชจ๋ธ ์œ ํ˜• ์‚ฌ์ „ ํ•™์Šต๋œ ๊ฐ€์ค‘์น˜ ์ง€์›๋˜๋Š” ์ž‘์—… ์ถ”๋ก  ์œ ํšจ์„ฑ ๊ฒ€์‚ฌ ๊ต์œก ๋‚ด๋ณด๋‚ด๊ธฐ
RT-DETR ๋Œ€ํ˜• rtdetr-l.pt ๋ฌผ์ฒด ๊ฐ์ง€ โœ… โœ… โœ… โœ…
RT-DETR ์ดˆ๋Œ€ํ˜• rtdetr-x.pt ๋ฌผ์ฒด ๊ฐ์ง€ โœ… โœ… โœ… โœ…

์ธ์šฉ ๋ฐ ๊ฐ์‚ฌ

์—ฐ๊ตฌ ๋˜๋Š” ๊ฐœ๋ฐœ ์ž‘์—…์— Baidu์˜ RT-DETR ์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฝ์šฐ ์›๋ณธ ๋…ผ๋ฌธ์„ ์ธ์šฉํ•ด ์ฃผ์„ธ์š”:

@misc{lv2023detrs,
      title={DETRs Beat YOLOs on Real-time Object Detection},
      author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
      year={2023},
      eprint={2304.08069},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

์ปดํ“จํ„ฐ ๋น„์ „ ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ๊ท€์ค‘ํ•œ ๋ฆฌ์†Œ์Šค๋ฅผ ์ œ์ž‘ํ•˜๊ณ  ์œ ์ง€ ๊ด€๋ฆฌํ•ด ์ฃผ์‹  Baidu์™€ PaddlePaddle์ปดํ“จํ„ฐ ๋น„์ „ ์ปค๋ฎค๋‹ˆํ‹ฐ๋ฅผ ์œ„ํ•ด ์ด ๊ท€์ค‘ํ•œ ๋ฆฌ์†Œ์Šค๋ฅผ ๋งŒ๋“ค๊ณ  ์œ ์ง€ ๊ด€๋ฆฌํ•ด ์ฃผ์‹  ํŒ€์—๊ฒŒ ๊ฐ์‚ฌ์˜ ๋ง์”€์„ ์ „ํ•ฉ๋‹ˆ๋‹ค. ๋น„์ „ ํŠธ๋žœ์Šคํฌ๋จธ ๊ธฐ๋ฐ˜ ์‹ค์‹œ๊ฐ„ ๋ฌผ์ฒด ๊ฐ์ง€๊ธฐ( RT-DETR)๋ฅผ ๊ฐœ๋ฐœํ•˜์—ฌ ์ด ๋ถ„์•ผ์— ๊ธฐ์—ฌํ•œ ๊ทธ๋“ค์˜ ๋…ธ๊ณ ์— ๊นŠ์€ ๊ฐ์‚ฌ๋ฅผ ํ‘œํ•ฉ๋‹ˆ๋‹ค.

์ž์ฃผ ๋ฌป๋Š” ์งˆ๋ฌธ

๋ฐ”์ด๋‘์˜ RT-DETR ๋ชจ๋ธ์ด๋ž€ ๋ฌด์—‡์ด๋ฉฐ ์–ด๋–ป๊ฒŒ ์ž‘๋™ํ•˜๋‚˜์š”?

๋ฐ”์ด๋‘์˜ RT-DETR (์‹ค์‹œ๊ฐ„ ๊ฐ์ง€ ํŠธ๋žœ์Šคํฌ๋จธ)๋Š” ๋น„์ „ ํŠธ๋žœ์Šคํฌ๋จธ ์•„ํ‚คํ…์ฒ˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ตฌ์ถ•๋œ ๊ณ ๊ธ‰ ์‹ค์‹œ๊ฐ„ ๋ฌผ์ฒด ๊ฐ์ง€๊ธฐ์ž…๋‹ˆ๋‹ค. ํšจ์œจ์ ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ธ์ฝ”๋”๋ฅผ ํ†ตํ•ด ์Šค์ผ€์ผ ๋‚ด ์ƒํ˜ธ ์ž‘์šฉ๊ณผ ์Šค์ผ€์ผ ๊ฐ„ ์œตํ•ฉ์„ ๋ถ„๋ฆฌํ•˜์—ฌ ๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ๊ธฐ๋Šฅ์„ ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ชจ๋ธ์€ IoU ์ธ์‹ ์ฟผ๋ฆฌ ์„ ํƒ์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐ€์žฅ ๊ด€๋ จ์„ฑ์ด ๋†’์€ ๊ฐ์ฒด์— ์ง‘์ค‘ํ•จ์œผ๋กœ์จ ๊ฐ์ง€ ์ •ํ™•๋„๋ฅผ ๋†’์ž…๋‹ˆ๋‹ค. ์žฌ๊ต์œก ์—†์ด ๋””์ฝ”๋” ๋ ˆ์ด์–ด๋ฅผ ์กฐ์ •ํ•˜์—ฌ ์ถ”๋ก  ์†๋„๋ฅผ ์กฐ์ •ํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ RT-DETR ๋‹ค์–‘ํ•œ ์‹ค์‹œ๊ฐ„ ๊ฐ์ฒด ๊ฐ์ง€ ์‹œ๋‚˜๋ฆฌ์˜ค์— ์ ํ•ฉํ•ฉ๋‹ˆ๋‹ค. RT-DETR ๊ธฐ๋Šฅ์— ๋Œ€ํ•ด ์ž์„ธํžˆ ์•Œ์•„๋ณด์„ธ์š”.

Ultralytics ์—์„œ ์ œ๊ณตํ•˜๋Š” ์‚ฌ์ „ ํ•™์Šต๋œ RT-DETR ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜๋ ค๋ฉด ์–ด๋–ป๊ฒŒ ํ•ด์•ผ ํ•˜๋‚˜์š”?

Ultralytics Python API๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‚ฌ์ „ ํ•™์Šต๋œ PaddlePaddle RT-DETR ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, COCO val2017์—์„œ ์‚ฌ์ „ ํ•™์Šต๋œ RT-DETR-l ๋ชจ๋ธ์„ ๋กœ๋“œํ•˜๊ณ  T4 GPU ์—์„œ ๋†’์€ FPS๋ฅผ ๋‹ฌ์„ฑํ•˜๋ ค๋ฉด ๋‹ค์Œ ์˜ˆ์ œ๋ฅผ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

์˜ˆ

from ultralytics import RTDETR

# Load a COCO-pretrained RT-DETR-l model
model = RTDETR("rtdetr-l.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 RT-DETR-l model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
# Load a COCO-pretrained RT-DETR-l model and train it on the COCO8 example dataset for 100 epochs
yolo train model=rtdetr-l.pt data=coco8.yaml epochs=100 imgsz=640

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

๋‹ค๋ฅธ ์‹ค์‹œ๊ฐ„ ๋ฌผ์ฒด ๊ฐ์ง€๊ธฐ๊ฐ€ ์•„๋‹Œ Baidu์˜ RT-DETR ์„ ์„ ํƒํ•ด์•ผ ํ•˜๋Š” ์ด์œ ๋Š” ๋ฌด์—‡์ธ๊ฐ€์š”?

๋ฐ”์ด๋‘์˜ RT-DETR ๋Š” ํšจ์œจ์ ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ธ์ฝ”๋”์™€ IoU ์ธ์‹ ์ฟผ๋ฆฌ ์„ ํƒ์œผ๋กœ ๋†’์€ ์ •ํ™•๋„๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ ๊ณ„์‚ฐ ๋น„์šฉ์„ ๋Œ€ํญ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ์ ์ด ๋‹๋ณด์ž…๋‹ˆ๋‹ค. ์žฌ๊ต์œก ์—†์ด ๋‹ค์–‘ํ•œ ๋””์ฝ”๋” ๋ ˆ์ด์–ด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”๋ก  ์†๋„๋ฅผ ์กฐ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๊ณ ์œ ํ•œ ๊ธฐ๋Šฅ์œผ๋กœ ์ƒ๋‹นํ•œ ์œ ์—ฐ์„ฑ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ CUDA ์™€ ๊ฐ™์€ ๊ฐ€์†ํ™”๋œ ๋ฐฑ์—”๋“œ์—์„œ ์‹ค์‹œ๊ฐ„ ์„ฑ๋Šฅ์ด ํ•„์š”ํ•œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์— ํŠนํžˆ ์œ ๋ฆฌํ•˜๋ฉฐ, ๋‹ค๋ฅธ ๋งŽ์€ ์‹ค์‹œ๊ฐ„ ๊ฐ์ฒด ํƒ์ง€๊ธฐ๋ฅผ ๋Šฅ๊ฐ€ํ•˜๋Š” TensorRT ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค.

RT-DETR ์€ ๋‹ค์–‘ํ•œ ์‹ค์‹œ๊ฐ„ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์— ์ ํ•ฉํ•œ ์ถ”๋ก  ์†๋„๋ฅผ ์–ด๋–ป๊ฒŒ ์ง€์›ํ•˜๋‚˜์š”?

๋ฐ”์ด๋‘์˜ RT-DETR ์—์„œ๋Š” ์žฌ๊ต์œก ์—†์ด๋„ ๋‹ค์–‘ํ•œ ๋””์ฝ”๋” ๋ ˆ์ด์–ด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”๋ก  ์†๋„๋ฅผ ์œ ์—ฐํ•˜๊ฒŒ ์กฐ์ •ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ์‘์„ฑ์€ ๋‹ค์–‘ํ•œ ์‹ค์‹œ๊ฐ„ ๊ฐ์ฒด ๊ฐ์ง€ ์ž‘์—…์—์„œ ์„ฑ๋Šฅ์„ ํ™•์žฅํ•˜๋Š” ๋ฐ ๋งค์šฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ๋‚ฎ์€ ์ •๋ฐ€๋„๋ฅผ ์œ„ํ•ด ๋” ๋น ๋ฅธ ์ฒ˜๋ฆฌ๊ฐ€ ํ•„์š”ํ•˜๋“ , ๋” ๋Š๋ฆฌ๊ณ  ์ •ํ™•ํ•œ ๊ฐ์ง€๊ฐ€ ํ•„์š”ํ•˜๋“ , RT-DETR ์€ ํŠน์ • ์š”๊ตฌ ์‚ฌํ•ญ์„ ์ถฉ์กฑํ•˜๋„๋ก ๋งž์ถคํ™”ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

RT-DETR ๋ชจ๋ธ์„ ๊ต์œก, ์œ ํšจ์„ฑ ๊ฒ€์‚ฌ ๋ฐ ๋‚ด๋ณด๋‚ด๊ธฐ์™€ ๊ฐ™์€ ๋‹ค๋ฅธ Ultralytics ๋ชจ๋“œ์™€ ํ•จ๊ป˜ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‚˜์š”?

์˜ˆ, RT-DETR ๋ชจ๋ธ์€ ํ•™์Šต, ๊ฒ€์ฆ, ์˜ˆ์ธก, ๋‚ด๋ณด๋‚ด๊ธฐ ๋“ฑ ๋‹ค์–‘ํ•œ Ultralytics ๋ชจ๋“œ์™€ ํ˜ธํ™˜๋ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ชจ๋“œ๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์ž์„ธํ•œ ์ง€์นจ์€ ๊ฐ ์„ค๋ช…์„œ๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”: ํ›ˆ๋ จ, Val, ์˜ˆ์ธก, ๋‚ด๋ณด๋‚ด๊ธฐ. ์ด๋ฅผ ํ†ตํ•ด ๊ฐ์ฒด ๊ฐ์ง€ ์†”๋ฃจ์…˜ ๊ฐœ๋ฐœ ๋ฐ ๋ฐฐํฌ๋ฅผ ์œ„ํ•œ ํฌ๊ด„์ ์ธ ์›Œํฌํ”Œ๋กœ์šฐ๋ฅผ ๋ณด์žฅํ•ฉ๋‹ˆ๋‹ค.

๐Ÿ“…1 ๋…„ ์ „ ์ƒ์„ฑ๋จ โœ๏ธ 2๊ฐœ์›” ์ „ ์—…๋ฐ์ดํŠธ๋จ

๋Œ“๊ธ€