icip2022 paper: sahi benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors

ECCV2018(Challenge-Object Detection in Images)

Many yolov8 model are trained on the VisDrone dataset.

Modern PyTorch toolkit for the VisDrone aerial object detection dataset with production-ready training pipelines, real-time inference, and format converters. Features state-of-the-art models (Faster R-CNN, FCOS, RetinaNet), mixed precision training, rich progress tracking, and optimizations for small object detection in drone imagery.

Simple implement of CenterNet on VisDrone dataset.

YOLO-TLP: detected and classified tiny objects with bounding box dimensions smaller than 15 pixels, outperforming other one-stage detectors. maximum resolution for target observation in real-time applications.

Hybrid RT DETR: Hybrid encoder-decoder network for end-to-end object detection in UAV imagery

dataset or annotation file format conversion

Object Detection on the Visdrone dataset

Smart traffic analysis application.

Attention-guided object detection on VisDrone using Grad-CAM++ and YOLO, with metrics and visual outputs.

Object detection format converter from VisDrone2019-DET to Yolo.

Evaluating Temporal Context for Robustness to Perturbations in Video Object Detection Models