Search Results for "segmentation"

Segmentation models with pre trained backbones. High-level API (just two lines to create a neural network) 9 models architectures for binary and multi class segmentation (including legendary Unet) 124 available encoders (and 500+ encoders from timm) All encoders have pre-trained weights for faster and better convergence. Popular metrics and losses for training routines.

Av1an is a command-line encoding framework designed to efficiently encode video into the AV1 format using distributed and parallel processing techniques. It acts as an orchestrator that splits video files into chunks, processes them in parallel using encoders like SVT-AV1, AOM, or rav1e, and then merges the results into a final output. The tool is optimized for high-quality encoding while reducing total processing time through multi-core and multi-machine support. It includes scene detection...

...Key features include: Flexible Video and Audio Encoding: Utilizes FFmpeg's libraries for various codecs and settings. Subtitle Handling: Include or exclude subtitles as needed. Customizable Settings: Adjust CRF values and aspect ratios to suit your needs. Video Segmentation and Merging: Split videos into segments and merge them back together. Track Disposition Management: Set default tracks for audio, video, and subtitles. Audio Track Extraction: Extract and encode audio tracks independently. Audio normalization: Adjusts audio levels to ensure consistent volume across tracks. The script leverages FFmpeg's built-in libraries and tools for these functions. ...

Golang HLS Streamer is a Go-based implementation of HTTP Live Streaming (HLS) functionality, designed to handle media segmentation and playlist generation for streaming applications. It provides tools for creating and managing HLS streams, including segmenting video into smaller chunks and generating M3U8 playlists. The project is intended for developers building streaming servers or media delivery systems. It focuses on performance and simplicity, leveraging Go’s concurrency model to handle streaming tasks efficiently. gohls can be integrated into backend services to enable adaptive streaming workflows. ...

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...Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. This significantly improves our model's robustness. Our method does not require any auxiliary inputs such as a trimap or a pre-captured background image, so it can be widely applied to existing human matting applications. RVM is specifically designed for robust human video matting.

The Mimas Toolkit is a C++ real-time computer vision library. Algorithms include edge/corner-detection, object recognition/tracking, LSI-filters, segmentation, array-operators, convolution etc. OO wrappers for LAPACK, libxine, V4L, FFTW are provided.