2681 papers with code • 1 benchmarks • 2 datasets
These leaderboards are used to track progress in object-detection
LibrariesUse these libraries to find object-detection models and implementations
Most implemented papers
Res2Net: A New Multi-scale Backbone Architecture
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.
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.
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
Light-weight convolutional neural networks (CNNs) are the de-facto for mobile vision tasks.
HarDNet: A Low Memory Traffic Network
We propose a Harmonic Densely Connected Network to achieve high efficiency in terms of both low MACs and memory traffic.
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
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.
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
SOLOv2: Dynamic and Fast Instance Segmentation
Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
Random Erasing Data Augmentation
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
Visual Attention Network
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.
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.