Displaying 1 to 9 from 9 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.

simpledet - A Simple and Versatile Framework for Object Detection and Instance Recognition

  •    Python

Everything is configurable from the config file, all the changes should be out of source. One experiment is a directory in experiments folder with the same name as the config file.

labelme - Image Polygonal Annotation with Python (polygon, rectangle, line, point and image-level flag annotation)

  •    Python

Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu. It is written in Python and uses Qt for its graphical interface. Fig 2. VOC dataset example of instance segmentation.




tfvos - Semi-Supervised Video Object Segmentation (VOS) with Tensorflow

  •    Jupyter

The aim of this project is to implement and compare implementations of several video object segmentation (VOS) algorithms using Tensorflow. As part of the NIPS Paper Implementation Challenge, we chose MaskRNN: Instance Level Video Object Segmentation (NIPS 2017) [2017h] as our first implementation. See the MaskRNN Tensorflow Implementation Section for more info.

tfwss - Weakly Supervised Segmentation with Tensorflow

  •    Jupyter

This repo contains a TensorFlow implementation of weakly supervised instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017). The idea behind weakly supervised segmentation is to train a model using cheap-to-generate label approximations (e.g., bounding boxes) as substitute/guiding labels for computer vision classification tasks that usually require very detailed labels. In semantic labelling, each image pixel is assigned to a specific class (e.g., boat, car, background, etc.). In instance segmentation, all the pixels belonging to the same object instance are given the same instance ID.

seg-by-interaction - Unsupervised instance segmentation via active robot interaction

  •    Python

This is the implementation for the paper on Learning Instance Segmentation by Interaction. We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-supervised manner. The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels. To be released soon. Email me in case you need early access.