Download
imgclsmob is a deep learning research repository focused on implementing and experimenting with convolutional neural networks for computer vision tasks. The project serves as a sandbox for training and evaluating a wide variety of neural network architectures used in image analysis. It includes implementations of models used for tasks such as image classification, object detection, semantic segmentation, and pose estimation. The repository also contains scripts that help train models, evaluate performance, and convert trained networks between different frameworks. Several deep learning frameworks are supported, allowing researchers to experiment with architectures in different environments. The project is frequently used by developers who want to study modern convolutional neural network designs and compare their performance across datasets.
Features
- Research framework for training deep convolutional neural networks
- Implementations of models for classification, detection, and segmentation
- Scripts for training, evaluating, and converting neural network models
- Support for multiple deep learning frameworks
- Tools for experimenting with modern computer vision architectures
- Benchmarking environment for comparing model performance
Project Samples
