Displaying 1 to 7 from 7 results

soft-nms - Object Detection

  •    Jupyter

This repository includes the code for Soft-NMS. Soft-NMS is integrated with two object detectors, R-FCN and Faster-RCNN. The Soft-NMS paper can be found here. Soft-NMS is complementary to multi-scale testing and iterative bounding box regression. Check MSRA slides from the COCO 2017 challenge.

android-yolo - Real-time object detection on Android using the YOLO network with TensorFlow

  •    C++

android-yolo is the first implementation of YOLO for TensorFlow on an Android device. It is compatible with Android Studio and usable out of the box. It can detect the 20 classes of objects in the Pascal VOC dataset: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train and tv/monitor. The network only outputs one predicted bounding box at a time for now. The code can and will be extended in the future to output several predictions. To use this demo first clone the repository. Download the TensorFlow YOLO model and put it in android-yolo/app/src/main/assets. Then open the project on Android Studio. Once the project is open you can run the project on your Android device using the Run 'app' command and selecting your device.

mAP - mean Average Precision - This code evaluates the performance of your neural net for object recognition

  •    Python

This code will evaluate the performance of your neural net for object recognition. In practice, a higher mAP value indicates a better performance of your neural net, given your ground-truth and set of classes.

imglab - To speedup and simplify image labeling/ annotation process with multiple supported formats.

  •    HTML

A web based tool to label images for objects that can be used to train dlib or other object detectors. With most users switching over to the new version of imglab, the legacy version of imglab has been removed.




chainer-pspnet - PSPNet in Chainer

  •    Python

This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Caffe is NOT needed to convert .caffemodel to Chainer model. Use caffe_pb2.py.

DetectionMetrics - Tool to evaluate deep-learning detection and segmentation models, and to create datasets

  •    C++

Detection Metrics is a set of tools to evaluate object detection neural networks models over the common object detection datasets. The tools can be accessed using the GUI or the command line applications. In the picture below, the general architecture is displayed. To quickly get started with Detection Metrics, we provide a docker image.






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