Work Description

Title: FCAV Simulation Dataset Open Access Deposited

Attribute Value
  • Simulation capture (GTAV)
  • A dataset for computer vision training obtained from long running computer simulations
Contact information
Resource type
Last modified
  • 03/23/2020
  • 03/09/2018
To Cite this Work:
Vasudevan, R., Barto, C., Rosaen, K., Mehta, R., Matthew, J., Nittur Sridhar, S. (2018). FCAV Simulation Dataset [Data set], University of Michigan - Deep Blue Data.


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Files (Count: 10; Size: 61.2 GB)

# Driving in the Matrix Steps to reproduce training results for the paper [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?]( conducted at [UM & Ford Center for Autonomous Vehicles (FCAV)]( Specifically, we will train [MXNet RCNN]( on our [10k dataset]( and evaluate on [KITTI]( ## System requirements To run training, you need [CUDA 8](, [NVIDIA Docker]( and a linux machine with at least one Nvidia GPU installed. Our training was conducted using 4 Titan-X GPUs. Training time per epoch for us was roughly 10k: 40 minutes, 50k: 3.3 hours, 200k: 12.5 hours. We plan on providing the trained parameters from the best performing epoch for 200k soon. ## Download the dataset Create a directory and download the archive files for 10k images, annotations and image sets from [our website]( Assuming you have downloaded these to a directory named `ditm-data` (driving in the matrix data): ``` $ ls -1 ditm-data repro_10k_annotations.tgz repro_10k_images.tgz repro_image_sets.tgz ``` Extract them. ``` $ pushd ditm-data $ tar zxvf repro_10k_images.tgz $ tar zxvf repro_10k_annotations.tgz $ tar zxvf repro_image_sets.tgz $ popd $ ls -1 ditm-data/VOC2012 Annotations ImageSets JPEGImages ``` ## Train on GTA To make training as reproducible (across our own machines, and now for you!) as possible, we ran training within a docker container [as detailed here]( If you are familiar with MXNet and its RCNN example and already have it installed, you will likely feel comfortable adapting these examples to run outside of docker. ### Build the MXNet RCNN Container ``` $ git clone $ pushd nn-dockerfiles $ docker build -t mxnet-rcnn mxnet-rcnn $ popd ``` This will take several minutes. ``` $ docker images | grep mxnet mxnet-rcnn latest bb488173ad1e 25 seconds ago 5.54 GB ``` ### Download pre-trained VGG16 network ``` $ mkdir -p pretrained-networks $ cd pretrained-networks && wget && cd - ``` ### Kick off training ``` $ mkdir -p training-runs/mxnet-rcnn-gta10k $ nvidia-docker run --rm --name run-mxnet-rcnn-end2end \ `#container volume mapping` \ -v `pwd`/training-runs/mxnet-rcnn-gta10k:/media/output \ -v `pwd`/pretrained-networks:/media/pretrained \ -v `pwd`/ditm-data:/root/mxnet/example/rcnn/data/VOCdevkit \ -it mxnet-rcnn \ `# python script` \ python \ --image_set 2012_trainval10k \ --root_path /media/output \ --pretrained /media/pretrained/vgg16 \ --prefix /media/output/e2e \ --gpus 0 \ 2>&1 | tee training-runs/mxnet-rcnn-gta10k/e2e-training-logs.txt ... INFO:root:Epoch[0] Batch [20] Speed: 6.41 samples/sec Train-RPNAcc=0.784970, RPNLogLoss=0.575420, RPNL1Loss=2.604233, RCNNAcc=0.866071, RCNNLogLoss=0.650824, RCNNL1Loss=0.908024, INFO:root:Epoch[0] Batch [40] Speed: 7.10 samples/sec Train-RPNAcc=0.807546, RPNLogLoss=0.539875, RPNL1Loss=2.544102, RCNNAcc=0.895579, RCNNLogLoss=0.461218, RCNNL1Loss=1.019715, INFO:root:Epoch[0] Batch [60] Speed: 6.76 samples/sec Train-RPNAcc=0.822298, RPNLogLoss=0.508551, RPNL1Loss=2.510861, RCNNAcc=0.894723, RCNNLogLoss=0.406725, RCNNL1Loss=1.005053, ... ``` As the epochs complete, the trained parameters will be available inside `training-runs/mxnet-rcnn-gta10k`. ## Training on other segments To train on 50k or 200k, first download and extract `repro_200k_images.tgz` and `repro_200k_annotations.tgz` and then run a similar command as above but with `image_set` set to `2012_trainval50k` or `2012_trainval200k`. ## Evaluate on KITTI ### Download the KITTI object detection dataset ### Convert it to VOC format ### Evaluate GTA10k trained network on KITTI ### Convert VOC evaluations to KITTI format ### Run KITTI's benchmark on results ## Citation If you find this useful in your research please cite: > M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen and R. Vasudevan, “Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?,” in IEEE International Conference on Robotics and Automation, pp. 1–8, 2017. @inproceedings{Johnson-Roberson:2017aa, Author = {M. Johnson-Roberson and Charles Barto and Rounak Mehta and Sharath Nittur Sridhar and Karl Rosaen and Ram Vasudevan}, Booktitle = {{IEEE} International Conference on Robotics and Automation}, Date-Added = {2017-01-17 14:22:19 +0000}, Date-Modified = {2017-02-23 14:37:23 +0000}, Keywords = {conf}, Pages = {1--8}, Title = {Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?}, Year = {2017}}

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