PaddleSeg

PP-MobileSeg is a state-of-the-art (SOTA) semantic segmentation model designed specifically for mobile devices, optimizing the tradeoff between parameters, accuracy, and latency. It utilizes three key components:

• StrideFormer Backbone: Uses a four-stage MobileNetV3 block for local feature extraction and strided sea attention to provide a global view in the final two stages.
• Aggregated Attention Module (AAM): Fuses features by filtering detail features with ensemble voting and adding semantic information.
• Valid Interpolate Module (VIM): Reduces inference latency by only interpolating classes present in the final prediction, significantly speeding up the upsampling process.

PaddleX is a low-code development tool built on PaddleSeg technology that enables efficient model usage, combination, and customization for image segmentation.

Key features include:

• One-click Model Access: 19 models integrated into 2 main pipelines (Universal Semantic Segmentation and Image Anomaly Detection) accessible via simple Python APIs.
• Flexible Deployment: Supports high-performance inference, service-based deployment, and edge-side deployment.
• Hardware Compatibility: Seamlessly switches between NVIDIA GPUs and domestic Chinese hardware including Kunlun, Ascend, Cambricon, and Haiguang.
• Development Modes: Supports both unified CLI commands and a graphical user interface (GUI) for model orchestration.

PSS-DET is a practical server-side object detection solution provided by PaddlePaddle. It utilizes a ResNet50_vd pre-trained model (trained via the SSLD distillation scheme from PaddleClas, achieving 82.39% Top1 Acc on ImageNet1k) combined with operators from PaddleDetection.

When evaluated on the COCO2017 dataset using a single V100 GPU, it achieves a prediction speed of 61 FPS and a COCO mAP of 41.2%.

The Remote Sensing Semantic Segmentation Benchmark is a specialized toolkit within PaddleSeg designed for land cover classification, disaster prediction, and urban planning. It provides:

• Standardized Benchmark: Standardized data processing, parameter configurations, and a set of comparable baseline models.
• Self-Supervised Learning: Multiple pre-trained models based on self-supervised learning methods to support research in the remote sensing domain.
• C2FNet: A model designed to improve segmentation accuracy for small objects by moving from coarse to fine-grained segmentation.

The Panoptic Segmentation Toolkit is built on top of PaddleSeg and unifies semantic segmentation (pixel-level class labeling) and instance segmentation (object instance detection and segmentation).

Key features include:

• High-Performance Models: Provides state-of-the-art panoptic segmentation models ready for out-of-the-box use.
• High Efficiency: Supports multi-process asynchronous I/O, multi-card parallel training, and evaluation, leveraging PaddlePaddle's memory optimization to reduce training costs.
• Complete Workflow: Supports the entire lifecycle from model design to deployment.

EISeg (Efficient Interactive Segmentation) is an efficient and intelligent interactive segmentation annotation software built on PaddlePaddle. It provides high-quality interactive segmentation models for various domains including general imagery, portrait/human segmentation, remote sensing, medical imaging (including 3D medical imaging), and video segmentation.

Key features include:

• Semi-automatic annotation tools for high efficiency.
• Support for specialized vertical scenarios (Remote Sensing, Medical, Video, 3D Medical).
• Multi-platform compatibility and multi-category label management.
• Integration with the PaddleSeg workflow: annotations obtained via EISeg can be used to train customized high-precision models in PaddleSeg.

U-Net is a standard encoder-decoder architecture designed for medical image segmentation. It features:

• Encoder: A shrinking structure consisting of four sub-modules. Each sub-module contains two convolutional layers followed by max pooling to reduce spatial dimensions and capture contextual information.
• Decoder: A symmetrical expansion structure that uses upsampling to restore spatial dimensions and details.
• Skip Connections: Fuses feature maps from the encoder and decoder at corresponding resolutions using a splicing (concatenation) method to help recover fine-grained target details.

U-Net is characterized by having few parameters, fast calculation, and high adaptability to general scenes.

PaDiM is an unsupervised anomaly detection algorithm based on representation modeling. It is a PaddlePaddle implementation of the paper PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization.

How it works:

• Training: Input images are passed through a pre-trained CNN backbone to extract multi-scale feature maps. These feature maps are dimensionality-reduced via random dimension selection and then fitted to a multivariate Gaussian distribution.
• Inference: The algorithm uses Mahalanobis distance to calculate an anomaly score for each position in the feature map. Higher scores indicate a higher probability of an anomaly in that region.