object-detection
3417 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in object-detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find object-detection models and implementations
IP102
RF100
DiaMOS Plant
Br35H :: Brain Tumor Detection 2020
Most implemented papers
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.
PointPillars: Fast Encoders for Object Detection from Point Clouds
nutonomy/second.pytorch
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CVPR 2019
These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
RandAugment: Practical automated data augmentation with a reduced search space
rwightman/pytorch-image-models
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NeurIPS 2020
Additionally, due to the separate search phase, these approaches are unable to adjust the regularization strength based on model or dataset size.
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks
mindspore-ai/models
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CVPR 2020
Our method outperforms BN and other alternatives in a variety of settings for all batch sizes.
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
rwightman/pytorch-image-models
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Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation.
nuScenes: A multimodal dataset for autonomous driving
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
yhenon/keras-spp
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This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale.
Speed/accuracy trade-offs for modern convolutional object detectors
tensorflow/models
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CVPR 2017
On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
zhirongw/lemniscate.pytorch
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Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so.
Attention Augmented Convolutional Networks
leaderj1001/Attention-Augmented-Conv2d
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ICCV 2019
Convolutional networks have been the paradigm of choice in many computer vision applications.
