text
YOLO-World: Real-Time, Zero-Shot Object Detection 🔥 🔥 🔥
YOLO-World: Real-Time, Zero-Shot Object Detection 🔥 🔥 🔥
new_line
new_line
text
YOLO-World was designed to solve a limitation of existing zero-shot object detection models: speed. Whereas other state-of-the-art models use Transformers, a powerful but typically slower architecture, YOLO-World uses the faster CNN-based YOLO architecture.
YOLO-World was designed to solve a limitation of existing zero-shot object detection models: speed. Whereas other state-of-the-art models use Transformers, a powerful but typically slower architecture, YOLO-World uses the faster CNN-based YOLO architecture.
new_line
new_line
text
YOLO-World provides three models: small with 13M (re-parametrized 77M), medium with 29M (re-parametrized 92M), and large with 48M (re-parametrized 110M) parameters.
YOLO-World provides three models: small with 13M (re-parametrized 77M), medium with 29M (re-parametrized 92M), and large with 48M (re-parametrized 110M) parameters.
new_line
new_line
text
The YOLO-World team benchmarked the model on the LVIS dataset and measured their performance on the V100 without any performance acceleration mechanisms like quantization or TensorRT.
The YOLO-World team benchmarked the model on the LVIS dataset and measured their performance on the V100 without any performance acceleration mechanisms like quantization or TensorRT.
new_line
new_line
text
According to the paper, YOLO-World reached 35.4 AP with 52.0 FPS for the L version and 26.2 AP with 74.1 FPS for the S version. While the V100 is a powerful GPU, achieving such high FPS on any device is impressive.
According to the paper, YOLO-World reached 35.4 AP with 52.0 FPS for the L version and 26.2 AP with 74.1 FPS for the S version. While the V100 is a powerful GPU, achieving such high FPS on any device is impressive.
new_line
new_line
text
- 🔗 YOLO-World arXiv paper: 
- 🔗 YOLO-World arXiv paper: 
link
https://lnkd.in/ddRBKCCX
https://lnkd.in/ddRBKCCX
new_line
text
- 🔗 my YOLO-World technical report: 
- 🔗 my YOLO-World technical report: 
link
https://blog.roboflow.com/what-is-yolo-world
https://blog.roboflow.com/what-is-yolo-world
new_line
text
- 🤗 YOLO-World space: 
- 🤗 YOLO-World space: 
resource
https://huggingface.co/spaces/SkalskiP/YOLO-World
space
SkalskiP/YOLO-World
https://huggingface.co/spaces/SkalskiP/YOLO-World
YOLO-World: Real-Time, Zero-Shot Object Detection 🔥 🔥 🔥

YOLO-World was designed to solve a limitation of existing zero-shot object detection models: speed. Whereas other state-of-the-art models use Transformers, a powerful but typically slower architecture, YOLO-World uses the faster CNN-based YOLO architecture.

YOLO-World provides three models: small with 13M (re-parametrized 77M), medium with 29M (re-parametrized 92M), and large with 48M (re-parametrized 110M) parameters.

The YOLO-World team benchmarked the model on the LVIS dataset and measured their performance on the V100 without any performance acceleration mechanisms like quantization or TensorRT.

According to the paper, YOLO-World reached 35.4 AP with 52.0 FPS for the L version and 26.2 AP with 74.1 FPS for the S version. While the V100 is a powerful GPU, achieving such high FPS on any device is impressive.

- 🔗 YOLO-World arXiv paper: https://lnkd.in/ddRBKCCX
- 🔗 my YOLO-World technical report: https://blog.roboflow.com/what-is-yolo-world
- 🤗 YOLO-World space: https://huggingface.co/spaces/SkalskiP/YOLO-World
❤️
clem adirik samusenps Kukedlc Pretam soulteary Citaman osanseviero DiamanteAmarelo sweat89 MiguelDu rreed-pha victor SkalskiP
🤗
SkalskiP DmitryRyumin vladbogo VictorSanh notune osanseviero DiamanteAmarelo MiguelDu