#Semantic Segmentation

Explore the MIT Deep Learning repository, which features a well-rounded set of tutorials focused on neural network basics, driving scene segmentation, and advanced techniques like generative adversarial networks. The DeepTraffic competition further enriches your learning experience by offering practical challenges in deep reinforcement learning. This evolving resource, aligned with MIT's ongoing courses, serves as a beneficial tool for newcomers and experienced practitioners in artificial intelligence.

Examine state-of-the-art semantic segmentation models equipped with versatile datasets in PyTorch. The project provides practical tools, seamless integration with leading backbone architectures, and accommodates various parsing tasks such as scene, human, and medical image segmentation. Future updates aim to revamp the training pipeline, deliver baseline pre-trained models, implement distributed training, and offer tutorials for custom datasets. Compatibility with ONNX and TFLite ensures widespread adaptability, serving developers who demand precision and flexibility in segmentation applications. Anticipate significant enhancements in the scheduled May 2024 release.

CVNets is a dynamic computer vision toolkit designed for training various models like EfficientNet, Swin Transformer, and CLIP on tasks such as classification, detection, and segmentation. The toolkit's latest update includes features like Bytes Are All You Need and RangeAugment to boost model efficiency. Suitable for use by researchers and engineers, it offers comprehensive documentation and examples, including model conversion to CoreML.

The RS-Mamba project features an innovative Recurrent State Space Model designed for dense prediction in large remote sensing images. By utilizing a state space model for the first time in this context, RS-Mamba achieves an effective global receptive field with linear complexity, setting new standards in semantic segmentation and change detection. This model is structured to optimally map spatial features across various directions, ensuring efficiency and power even with straightforward training methods. Explore the code and documentation to enhance remote sensing projects.

Explore state-of-the-art methods for AI explainability in computer vision, including advanced Pixel Attribution and benchmarking tools. Supports diverse CNNs and Vision Transformers across use cases like classification and segmentation, with methods like GradCAM for enhanced visualization and interpretability metrics.

The ScanNet dataset offers over 2.5 million RGB-D views from 1500 scans, complete with 3D camera poses, surface reconstructions, and semantic segmentations. It is applicable in scene understanding tasks such as 3D object classification and semantic voxel labeling. The dataset is organized by RGB-D sequence, integrating scan data, camera poses, and semantic annotations. Tools like the ScanNet C++ Toolkit and BundleFusion enhance data handling and 3D modeling. Check the ScanNet Terms of Use for accessing the data.

Explore how HRDA uses multi-resolution training to improve unsupervised domain adaptation by combining high-resolution for detail and low-resolution for context, enhancing performance on datasets like GTA→Cityscapes. Learn about HRDA's advancements in domain generalization and its impact on state-of-the-art results.

The bpycv project equips Blender with specialized tools for computer vision and deep learning, facilitating effective rendering of semantic, instance, and panoptic segmentation annotations. Features include 6DoF pose and depth data generation, domain randomization, and straightforward installation with Docker. Utilizing Blender’s native API, it supports the creation of synthetic datasets and conversion to common annotation formats. Recognized for its capabilities, bpycv secured second place in the OCRTOC at IROS 2020, making it valuable for advanced dataset management within Blender.

Discover deep learning techniques structured for satellite and aerial image analysis, covering classification, segmentation, and object detection among others. This resource details architectures, models, and algorithms devised to tackle the unique challenges posed by large image sizes and varied object classes. Uncover methods such as regression, cloud and change detection, time series analysis, and crop classification, emphasizing practical applications in remote sensing.

The SSSegmentation toolbox is an open-source project focused on supervised semantic segmentation using PyTorch. It features high-performance algorithms within a modular framework and offers a unified system for benchmarking, supporting various backbones and segmentors such as SAMV2, EdgeSAM, and Mask2Former. The project regularly incorporates updates to include modern semantic segmentation models, prioritizing efficiency and reduced dependencies. Access a wide range of models and detailed documentation for enhancing segmentation tasks.

Mask3D uses leading-edge mask transformer technology for precise 3D instance segmentation, topping benchmarks on key datasets like ScanNet and S3DIS. The project is built on robust, modular code from Mix3D, promoting high adaptability in sophisticated 3D tasks. It includes resources like pre-trained networks for both research and practical applications, recently updated for easier installation and expanded dataset support. Compatible with PyTorch and Lightning, Mask3D seamlessly fits into modern AI workflows.

OpenScene introduces a zero-shot methodology for 3D scene understanding through open-vocabulary queries, featuring real-time and interactive tools for element identification across diverse queries, including objects and activities. Utilizing datasets like ScanNet and Matterport3D alongside multi-view fused features, it enhances 3D environment comprehension and supports foundational tasks like semantic segmentation, scene exploration, and 3D object detection to aid computer vision researchers and developers.

Masked Image Consistency (MIC) advances unsupervised domain adaptation by focusing on spatial context relations in target domains. Through consistency between masked image predictions and pseudo-labels, MIC enhances visual recognition performance in tasks like image classification, semantic segmentation, and object detection. Suitable for various UDA challenges, including synthetic-to-real and clear-to-adverse-weather scenarios, MIC achieves high performance in benchmarks such as GTA to Cityscapes and VisDA-2017, contributing significantly to domain adaptation research.

Vision-RWKV is an AI project offering efficient and scalable solutions for visual perception through RWKV-like architectures. It excels in high-resolution image processing with a global receptive field, achieving superior performance and stability, especially after pre-training on large datasets. Outperforming window-based and global attention ViTs in classification tasks, it boasts lower flops and faster speeds. Recent support for RWKV6 further boosts classification performance. The project provides multiple pre-trained models on ImageNet, suited for object detection and semantic segmentation, with straightforward access to checkpoints and configuration files for customization.