object-detection
4188 papers with code • 2 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in object-detection
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Libraries
Use these libraries to find object-detection models and implementationsDatasets
IP102
RF100
DiaMOS Plant
Br35H :: Brain Tumor Detection 2020
AODRaw
Underwater Object Detection Dataset
Most implemented papers
Res2Net: A New Multi-scale Backbone Architecture
rwightman/pytorch-image-models
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We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
Light-weight convolutional neural networks (CNNs) are the de-facto for mobile vision tasks.
MnasNet: Platform-Aware Neural Architecture Search for Mobile
In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.
HarDNet: A Low Memory Traffic Network
PingoLH/Pytorch-HarDNet
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ICCV 2019
We propose a Harmonic Densely Connected Network to achieve high efficiency in terms of both low MACs and memory traffic.
Group Normalization
ppwwyyxx/GroupNorm-reproduce
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ECCV 2018
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Visual Attention Network
Visual-Attention-Network/VAN-Classification
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In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.
CenterNet: Keypoint Triplets for Object Detection
Duankaiwen/CenterNet
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ICCV 2019
In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions.
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zzh-tju/DIoU
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By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e. g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
This is due to the very invariance properties that make DCNNs good for high level tasks.
Random Erasing Data Augmentation
zhunzhong07/Random-Erasing
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In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
