Most popular metrics used to evaluate object detection algorithms.

We write your reusable computer vision tools. 💜

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

Free to use online tool for labelling photos. https://makesense.ai

CVNets: A library for training computer vision networks

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes

DeepLab-ResNet rebuilt in TensorFlow

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆20 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.

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.

To speedup and simplify image labeling/ annotation process with multiple supported formats.

Label images and video for Computer Vision applications

DeepLabv3+ built in TensorFlow

Object Detection

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

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.

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]