Displaying 1 to 5 from 5 results

Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

  •    Python

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). If you work on 3D vision, you might find our recently released Matterport3D dataset useful as well. This dataset was created from 3D-reconstructed spaces captured by our customers who agreed to make them publicly available for academic use. You can see more examples here.

Detectron

  •    Python

Example output of e2e_mask_rcnn-R-101-FPN_2x using Detectron pretrained weight. Corresponding example output from Detectron.

realtime-detectron - Real-time Detectron using webcam.

  •    Python

This is a demo project of a real-time Mask R-CNN using Detectron. We will be using consumer grade webcam for capturing the video stream. Here's an example of demo created by reddit's user _sshin_.

open-solution-googleai-object-detection - Open solution to the Google AI Object Detection Challenge :maple_leaf:

  •    Python

In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍. This competition is special, because it used Open Images Dataset V4, which is quite large: >1.8M images and >0.5TB 😲 To make it more approachable, we are hosting entire dataset in the neptune's public directory 😎. You can use this dataset in neptune.ml with no additional setup 👍.