# dmlc/gluon-cv

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5,922 stars · 1,204 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/dmlc/gluon-cv
- Homepage: http://gluon-cv.mxnet.io
- awesome-repositories: https://awesome-repositories.com/repository/dmlc-gluon-cv.md

## Topics

`action-recognition` `computer-vision` `deep-learning` `gan` `gluon` `image-classification` `machine-learning` `mxnet` `neural-network` `object-detection` `person-reid` `pose-estimation` `semantic-segmentation`

## Description

Gluon-CV is an MXNet computer vision library that provides a comprehensive collection of pre-implemented vision architectures and training pipelines. It serves as a deep learning research toolkit and a model zoo containing state-of-the-art pre-trained weights for image and video analysis.

The project includes a specialized human pose estimation library and a model compression toolkit. These tools allow for the pruning and quantization of deep learning models to increase inference speed and facilitate deployment on constrained edge hardware.

The library covers a broad range of vision capabilities, including image classification, object detection, and semantic and instance segmentation. It also provides tools for video analysis, such as action recognition, object tracking, and monocular depth estimation.

Training is supported through automated pipelines and distributed multi-GPU workloads to accelerate model convergence.

## Tags

### Artificial Intelligence & ML

- [Computer Vision](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision.md) — Serves as a comprehensive computer vision library for training and deploying models using the MXNet framework.
- [Pretrained Model Zoos](https://awesome-repositories.com/f/artificial-intelligence-ml/pretrained-model-zoos.md) — Serves as a comprehensive model zoo providing state-of-the-art pre-trained weights and architectures for vision analysis.
- [Action Recognition](https://awesome-repositories.com/f/artificial-intelligence-ml/action-recognition.md) — Identifies human actions within video clips using pre-trained 2D and 3D deep learning architectures. ([source](https://gluon-cv.mxnet.io/model_zoo/action_recognition.html))
- [Instance Segmentation Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-libraries/instance-segmentation-engines.md) — Generates precise pixel-level masks to identify and isolate individual object instances. ([source](https://cdn.jsdelivr.net/gh/dmlc/gluon-cv@master/README.md))
- [Computer Vision Research](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-research.md) — Provides a research toolkit for prototyping and testing new visual architectures using pre-implemented models and pipelines.
- [Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/object-detection.md) — Implements systems to identify and locate multiple objects using bounding boxes and instance segmentation.
- [Image Segmentation](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/image-segmentation.md) — Provides techniques for partitioning images into pixel-level regions for semantic and instance segmentation.
- [Multiclass Segmentations](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/image-segmentation/multiclass-segmentations.md) — Processes image tensors to create multiclass maps for instance and semantic segmentation.
- [Deep Learning Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-toolkits.md) — Provides a set of tools for prototyping and accelerating deep learning research in vision.
- [High-Level Training APIs](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-training-pipelines/high-level-training-apis.md) — Implements high-level controllers that automate the training loop and eliminate repetitive boilerplate code for vision tasks.
- [Distributed GPU Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-gpu-training.md) — Enables distributing computationally heavy training workloads across multiple GPUs to accelerate model convergence.
- [Human Activity Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/human-activity-analysis.md) — Recognizes human actions in video sequences and estimates anatomical keypoints for pose estimation.
- [Image Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification.md) — Categorizes images into predefined classes using pre-trained weights or custom datasets. ([source](https://cdn.jsdelivr.net/gh/dmlc/gluon-cv@master/README.md))
- [Tensor Computing Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries.md) — Utilizes MXNet for scalable tensor computation supporting both imperative and symbolic programming paradigms.
- [Pre-trained Model Zoos](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/pre-trained-model-zoos.md) — Maintains a comprehensive repository of state-of-the-art pre-trained weights and network architectures for vision tasks.
- [Model Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-frameworks.md) — Provides a framework for building and optimizing vision models with distributed multi-GPU support.
- [Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/object-detection.md) — Identifies and locates multiple objects within images or video streams using bounding box models. ([source](https://cdn.jsdelivr.net/gh/dmlc/gluon-cv@master/README.md))
- [Semantic Image Segmentations](https://awesome-repositories.com/f/artificial-intelligence-ml/semantic-image-segmentations.md) — Classifies every pixel in an image into a specific category using pre-trained or custom-trained models. ([source](http://gluon-cv.mxnet.io/build/examples_segmentation/index.html))
- [Specialized Network Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/specialized-network-architectures.md) — Provides a wide variety of specialized neural network layers and blocks for semantic segmentation and object detection.
- [Video Object Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/video-object-tracking.md) — Estimates the trajectory of single or multiple objects across consecutive video frames. ([source](https://gluon-cv.mxnet.io/tutorials/index.html))
- [Human](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/language-model-integrations/computer-vision-models/pose-estimation-models/human.md) — Includes specialized models for identifying anatomical keypoints and tracking human body movement.
- [Detection Data Loaders](https://awesome-repositories.com/f/artificial-intelligence-ml/data-loading-utilities/detection-data-loaders.md) — Provides specialized data loaders that import detection data from raw images, metadata lists, or binary files. ([source](https://gluon-cv.mxnet.io/api/data.datasets.html))
- [Classification Training](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification/classification-training.md) — Provides comprehensive training pipelines for optimizing image classification models on various datasets. ([source](http://gluon-cv.mxnet.io/build/examples_classification/index.html))
- [Inference Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-model-deployment.md) — Exports trained networks to portable formats like JSON or quantized versions for optimized C++ inference. ([source](https://gluon-cv.mxnet.io/tutorials/index.html))
- [Pre-trained Weight Loading](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-model-training/vision-transformer-pre-training/pre-trained-model-checkpoints/pre-trained-weight-loading.md) — Downloads and initializes pre-trained computer vision weights for immediate inference on image data. ([source](https://gluon-cv.mxnet.io/model_zoo/classification.html))
- [Edge AI Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment.md) — Offers quantization and pruning tools to enable efficient model inference on constrained edge hardware.
- [Vision Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/model-training-frameworks/vision-model-training.md) — Scales the training of computationally expensive video and vision models across multiple computing nodes. ([source](https://gluon-cv.mxnet.io/tutorials/index.html))
- [Model Pruning](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/compression-techniques/model-pruning.md) — Increases processing speed by removing unnecessary parameters from models while maintaining prediction accuracy. ([source](https://gluon-cv.mxnet.io/model_zoo/classification.html))
- [Model Compression Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/compression-techniques/model-pruning/model-compression-suites.md) — Offers a toolkit for pruning and quantizing models to increase inference speed on edge hardware.
- [Model Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization.md) — Implements weight precision reduction techniques to decrease model size and increase inference speed on edge hardware.
- [Model Finetuning](https://awesome-repositories.com/f/artificial-intelligence-ml/object-detection/model-finetuning.md) — Adapts pre-trained detection models to specific tasks by reusing weights on new datasets. ([source](http://gluon-cv.mxnet.io/build/examples_detection/index.html))
- [Training](https://awesome-repositories.com/f/artificial-intelligence-ml/object-detection/training.md) — Implements end-to-end training processes for optimizing networks to perform object localization and classification. ([source](http://gluon-cv.mxnet.io/build/examples_detection/index.html))
- [Pre-training Transfer Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/pre-training-transfer-learning.md) — Supports adapting pre-trained computer vision models to new image datasets to improve accuracy with limited data. ([source](http://gluon-cv.mxnet.io/build/examples_classification/index.html))
- [Quantized Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes.md) — Accelerates inference speed on constrained hardware using integer-quantized versions of model architectures. ([source](https://gluon-cv.mxnet.io/model_zoo/classification.html))
- [Quantized Model Exporters](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization/quantized-model-implementations/on-load-quantizers/quantized-model-exporters.md) — Converts trained networks into portable and quantized formats specifically for deployment on edge devices. ([source](https://gluon-cv.mxnet.io/tutorials/index.html))
- [Vision Dataset Preparation](https://awesome-repositories.com/f/artificial-intelligence-ml/training-dataset-preparation/vision-dataset-preparation.md) — Provides tools for formatting and preprocessing image and video datasets to ensure consistency for model training. ([source](https://gluon-cv.mxnet.io/tutorials/index.html))
- [Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/training-pipelines.md) — Automates training pipelines for classifiers and detectors using specialized estimators to reduce boilerplate. ([source](https://gluon-cv.mxnet.io/tutorials/index.html))
- [Video Feature Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/video-analysis/video-feature-extraction.md) — Processes video files through pre-trained models to generate numerical feature representations for analysis. ([source](https://gluon-cv.mxnet.io/build/examples_action_recognition/index.html))

### Part of an Awesome List

- [Human Pose Estimation](https://awesome-repositories.com/f/awesome-lists/ai/pose-estimation-frameworks/human-pose-estimation.md) — Predicts human joint locations in images using pre-trained architectures optimized for accuracy or edge performance. ([source](https://cdn.jsdelivr.net/gh/dmlc/gluon-cv@master/README.md))
- [Action Recognition Training](https://awesome-repositories.com/f/awesome-lists/ai/action-recognition-models/action-recognition-training.md) — Implements training pipelines to classify human actions in video sequences. ([source](https://gluon-cv.mxnet.io/build/examples_action_recognition/index.html))
- [Vision Architectures](https://awesome-repositories.com/f/awesome-lists/ai/vision-architectures.md) — Provides pre-built implementations of state-of-the-art vision architectures for research. ([source](https://gluon-cv.mxnet.io))

### Data & Databases

- [Binary Record Data Loading](https://awesome-repositories.com/f/data-databases/binary-record-data-loading.md) — Provides optimized mechanisms for reading large-scale image datasets from binary files to reduce I/O overhead during training.
- [Video Action](https://awesome-repositories.com/f/data-databases/dataset-loading/video-action.md) — Provides specialized loaders for importing video-based datasets used in action recognition tasks. ([source](https://gluon-cv.mxnet.io/api/data.datasets.html))
- [Vision Dataset Loading](https://awesome-repositories.com/f/data-databases/vision-dataset-loading.md) — Includes utilities for importing standard public datasets for image classification, detection, and segmentation. ([source](https://gluon-cv.mxnet.io/api/data.datasets.html))

### DevOps & Infrastructure

- [Edge Deployment Tools](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/deployment-strategies/edge-deployment-tools.md) — Runs object detection inference on specialized hardware modules for real-time local processing. ([source](http://gluon-cv.mxnet.io/build/examples_detection/index.html))