We write your reusable computer vision tools. 💜

Most popular metrics used to evaluate object detection algorithms.

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

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

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

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

DeepLabv3 built in TensorFlow

DeepLab-ResNet rebuilt in Pytorch