AP50:95 - FPS (Tesla V100) Paper: 
What is this?
Darknet is an open source neural network framework written in C and CUDA, used for training and running deep learning models for computer vision tasks like object detection (YOLO).
Darknet is an open source neural network framework written in C and CUDA, used for training and running deep learning models for computer vision tasks like object detection (YOLO).Is it a framework or a library?It's a low-level deep learning framework (not a wrapper around another library) that you compile and use via its own CLI and config files, mainly known as the home of the YOLO object detection models.
It's a low-level deep learning framework (not a wrapper around another library) that you compile and use via its own CLI and config files, mainly known as the home of the YOLO object detection models.How do I get it running?Build it with 'make' from source; the repo includes a Makefile for compilation, with references to Darknet's own documentation site and wiki for build options and usage.
Build it with 'make' from source; the repo includes a Makefile for compilation, with references to Darknet's own documentation site and wiki for build options and usage.Do I need CUDA or a GPU to use this?No, it supports both CPU and GPU computation, though GPU/CUDA acceleration is used for faster training and inference.
No, it supports both CPU and GPU computation, though GPU/CUDA acceleration is used for faster training and inference.Is this the official state-of-the-art YOLO implementation now?The repo README mostly documents older results (YOLOv4, Scaled-YOLOv4, YOLOv7) with links to separate PyTorch reproductions; it functions more as the original reference/legacy codebase than an actively evolving cutting-edge detector repo.
The repo README mostly documents older results (YOLOv4, Scaled-YOLOv4, YOLOv7) with links to separate PyTorch reproductions; it functions more as the original reference/legacy codebase than an actively evolving cutting-edge detector repo.Does this benefit from a GPU?Yes, GPU computation (via CUDA) is supported for faster performance, especially referenced in the YOLO benchmark comparisons run on Tesla V100/A100 GPUs.
Yes, GPU computation (via CUDA) is supported for faster performance, especially referenced in the YOLO benchmark comparisons run on Tesla V100/A100 GPUs.What is YOLO and how does it relate to Darknet?YOLO (You Only Look Once) is a family of real-time object detection models originally developed and released through Darknet; the README tracks the progression from YOLOv4 to Scaled-YOLOv4 to YOLOv7.
YOLO (You Only Look Once) is a family of real-time object detection models originally developed and released through Darknet; the README tracks the progression from YOLOv4 to Scaled-YOLOv4 to YOLOv7.How fast is YOLOv7 compared to other detectors?YOLOv7 is reported as 120% faster than YOLOv5, 180% faster than YOLOX, and up to 1200% faster than Dual-Swin-T, while achieving the highest accuracy (56.8% AP) among real-time detectors running at 30+ FPS on a V100 GPU.
YOLOv7 is reported as 120% faster than YOLOv5, 180% faster than YOLOX, and up to 1200% faster than Dual-Swin-T, while achieving the highest accuracy (56.8% AP) among real-time detectors running at 30+ FPS on a V100 GPU.Which topics is it in?darknet, yolo, cnn, object-detection, neural-network, artificial-intelligence, c, nvidia, computer-vision, cuda, pytorch, benchmark
darknet, yolo, cnn, object-detection, neural-network, artificial-intelligence, c, nvidia, computer-vision, cuda, pytorch, benchmark