Popular GitHub Repositories for Pascal Voc

mean Average Precision - This code evaluates the performance of your neural net for object recognition.

Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes

CVNets: A library for training computer vision networks

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

DeepLab-ResNet rebuilt in TensorFlow

Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.

Label images and video for Computer Vision applications

PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help

DeepLabv3+ built in TensorFlow

Real-time object detection on Android using the YOLO network with TensorFlow

DeepLab resnet v2 model in pytorch

Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.

[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation

This repository contains the source code of our work on designing efficient CNNs for computer vision

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]

Convert between visual object detection datasets

A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.

目标检测 - R-CNN算法实现

Code, data and benchmark from the paper "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" (NeurIPS 2019 ML4AD)

Lacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.