Search Results for "reference"

...The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. The project encourages experimentation—swap optimizers, change augmentations, or plug the transformer backbone into downstream tasks.

...DenseFuse, FusionGAN, NestFuse, etc.), links to code implementations, and describes evaluation metrics. The repository includes a “General Evaluation Metric” subfolder containing objective fusion metrics. It is not a single monolithic tool, but rather a curated reference and aggregation of methods, code and performance comparisons in the domain of image fusion. Survey style description of method taxonomy, architectures, loss types. Compilation of many state-of-the-art image fusion methods (infrared + visible, multi-focus, multi-exposure). Survey style description of method taxonomy, architectures, loss types.

...Visualization utilities and diagnostic scripts make it straightforward to inspect predictions, proposals, and losses while training. Although the project has since been superseded by Detectron2, the original Detectron remains a historically important, reproducible reference that still informs many productions.

pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are...

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box.

...The framework integrates critical components—region proposal networks (RPNs), RoIAlign layers, mask heads, and backbone architectures such as ResNet and FPN—optimized for both accuracy and speed. It supports multi-GPU distributed training, mixed precision, and custom data loaders for new datasets. Built as a reference implementation, it became a foundation for the next-generation Detectron2, yet remains widely used for research needing a stable, reproducible environment. Visualization tools, model zoo checkpoints, and benchmark scripts make it easy to replicate state-of-the-art results or fine-tune models for custom tasks.

...The project is now available for the iCub platform to work on YARP [https://github.com/stino78/vergence-control/][1] The algorithm works on the top of a distributed representation of binocular disparity supplied by a population of binocular energy-model neural units. The project allows a robust control and adaptive binocular coordination for different robot stereo platforms. Reference publications: Gibaldi, A., Vanegas, M., Canessa, A., & Sabatini, S. P. (2017). A portable bio-inspired architecture for efficient robotic vergence control. International Journal of Computer Vision,. Gibaldi, A., Canessa, A., Chessa, M., Sabatini, S. P., & Solari, F. (2011, October). A neuromorphic control module for real-time vergence eye movements on the iCub robot head. ...