在线时间:8:00-16:00
迪恩网络APP
随时随地掌握行业动态
扫描二维码
关注迪恩网络微信公众号
开源软件名称:robot-grasp-detection开源软件地址:https://gitee.com/soldatjiang/robot-grasp-detection开源软件介绍:Detecting grasping positions with deep neural networks using RGB images(The model is uploaded but you can train better yourself if you have the time and the machine or if you are learning Tensorflow/ML. Please bear in mind that you need to read and adapt to your needs some parts of the code. Feel free to open an issue if you need help. I will try to update README and comment the code.) This implementation is mainly based on the algorithm from Redmon and Angelova described in arXiv:1412.3128v2. The method uses an RGB image to find a single grasp. A deep convolutional neural network is applied to an image of an ohject and as a result one gets the coordinates, dimensions, and orientation of one possible grasp. The images used to train the network are from Cornell Grasping Dataset. Problem descriptionHaving in mind a parallel plate griper before it closes, a simple and natural way of picturing the grasping position in an image would be a rectangle (see figure 1). One way representing it uniquely is as g = {x, y, \theta, h, w} where (x,y) is the center of the rectangle, \theta is the orientation of the rectangle to the horizontal axis of the image, h and w are the dimensions (height and width) of the rectangle. The sole purpose of this small library is to train a network that given a RGB image is able (with some accuracy) to predict a possible grasp g. How to train from scratchThe procedure follows these steps:
Preparing ImagenetBefore running the script you will need to download and convert the ImageNet data to native TFRecord format. Check this link from the Inception model from Google. I found the whole Inception model in Github very useful. Training on ImagenetRunning Check also in the end of the file the options that you can use, for example: ./imagenet_classifier.py --batch_size=128 --model_path=./models/imagenet/m1/m1.ckpt --train_or_validation=train Running on a GTX 980 and a very^2 good Xeon it needs around two days (I didn't time it). Check in the begining if the model is saving/restoring the weights. Preparing Cornell Grasping DatasetAfter downloading and decompressing run
Train on grasping datasetJust run |
请发表评论