Segmentation
4138 papers with code • 1 benchmarks • 10 datasets
Benchmarks
These leaderboards are used to track progress in Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Segmentation models and implementations
CRACK500
MMFlood
LES-AV
LLM-Seg40K
UIIS
BHSD
NPO
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
•
•
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Mask R-CNN
tensorflow/models
•
•
ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Rethinking Atrous Convolution for Semantic Image Segmentation
To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Searching for MobileNetV3
tensorflow/models
•
•
ICCV 2019
We achieve new state of the art results for mobile classification, detection and segmentation.
Fully Convolutional Networks for Semantic Segmentation
pochih/fcn-pytorch
•
•
CVPR 2015
Convolutional networks are powerful visual models that yield hierarchies of features.
YOLACT: Real-time Instance Segmentation
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features.
Microsoft COCO: Common Objects in Context
PaddlePaddle/PaddleDetection
•
•
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.
YOLACT++: Better Real-time Instance Segmentation
dbolya/yolact
•
•
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
