mx-rcnn
Object detector
An implementation of Faster R-CNN using MXNet for object detection tasks
Parallel Faster R-CNN implementation with MXNet.
671 stars
44 watching
290 forks
Language: Python
last commit: over 6 years ago
Linked from 2 awesome lists
Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of Mask R-CNN using MXNet and Resnet-50-FPN for object detection and segmentation in images. | 1,755 |
| A custom implementation of Faster R-CNN with ResNet architecture and Online Hard Example Mining. | 207 |
| A TensorFlow-based implementation of Faster R-CNN object detection using pre-trained ResNet networks and custom datasets. | 875 |
| An implementation of Faster R-CNN detection framework in PyTorch | 1,816 |
| A Caffe-based implementation of A-Fast-RCNN, a method for object detection using adversarial networks. | 482 |
| An implementation of R-FCN, an object detection algorithm using region-based fully convolutional networks. | 1,048 |
| A research project providing a deep learning-based object detection framework | 835 |
| An implementation of Faster R-CNN object detection in PyTorch, modified from DenseCap. | 85 |
| This repository provides code for training a Faster R-CNN object detection model on DOTA datasets. | 337 |
| A Python-based object detection framework utilizing transformers and computer vision techniques to detect salient objects in RGB-thermal images | 16 |
| A MATLAB implementation of an object detection framework that leverages deep fully convolutional networks to accurately and efficiently detect objects in images. | 1,245 |
| An implementation of the fast R-CNN object detection algorithm using Chainer and OpenCV. | 43 |
| Develops a deep neural network model for detecting salient objects in RGBT images using correlation information from other colors. | 13 |
| Implementation of object detection using Faster R-CNN with Chainer deep learning framework | 288 |
| An implementation of Mask-RCNN in Caffe for object detection and instance segmentation tasks | 231 |