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A car detection model implemented in Tensorflow.
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# KittiBox
KittiBox is a collection of scripts to train out model FastBox on the [Kitti Object Detection Dataset](http://www.cvlibs.net/datasets/kitti/eval_object.php). A detailed description of Fastbox can be found in our [MultiNet paper](https://arxiv.org/abs/1612.07695).
<img src="data/images/007007.png" width="288"> <img src="data/images/007404.png" width="288"> <img src="data/images/007241.png" width="288">
<img src="data/images/007034.png" width="288"> <img src="data/images/007029.png" width="288"> <img src="data/images/007066.png" width="288">
FastBox is designed to archive a high detection performance at a very fast inference speed. On Kitti data the model has a throughput of 28 fps (36ms), and is more than double as fast as FasterRCNN. Despite its impressive speed FastBox outperforms Faster-RCNN significantly.
Task | moderate | easy | hard | speed (ms) | speed (fps)
--------------|------------|--------------|---------------|------------- |----------
FastBox | 86.45 % | 92.80 % | 67.59 % | 35.75 ms | 27.97
Faster-RCNN<sup>[1](#myfootnote1)</sup> | 78.42 % | 91.62 % | 66.85 % | 78.30 ms | 12.77
The code contains for `train`, `evaluate` and `visualize` FastBox in tensorflow. It is build to be compatible with the [TensorVision](http://tensorvision.readthedocs.io/en/master/user/tutorial.html#workflow) backend which allows to organize experiments in a very clean way. Also check out [KittiSeg](https://github.com/MarvinTeichmann/KittiSeg#kittiseg) a similar project implementing a state-of-the-art road segmentation model. The code for joint inference can be found in the [MultiNet](https://github.com/MarvinTeichmann/MultiNet) repository.
## Requirements
The code requires Tensorflow 1.0 as well as the following python libraries:
* matplotlib
* numpy
* Pillow
* scipy
* runcython
Those modules can be installed using: `pip install numpy scipy pillow matplotlib runcython` or `pip install -r requirements.txt`.
## Tensorflow 1.0rc
This code requires `Tensorflow Version >= 1.0rc` to run. There have been a few breaking changes recently. If you are currently running an older tensorflow version, I suggest creating a new `virtualenv` and install 1.0rc using:
```bash
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc0-cp27-none-linux_x86_64.whl
pip install --upgrade $TF_BINARY_URL
```
Above commands will install the linux version with gpu support. For other versions follow the instructions [here](https://www.tensorflow.org/versions/r1.0/get_started/os_setup).
## Setup
1. Clone this repository: `git clone https://github.com/MarvinTeichmann/KittiBox.git`
2. Initialize all submodules: `git submodule update --init --recursive`
3. Run `cd submodules/utils && make` to build cython code
4. [Optional] Download Kitti Object Detection Data
1. Retrieve Kitti data url here: [http://www.cvlibs.net/download.php?file=data_object_image_2.zip](http://www.cvlibs.net/download.php?file=data_object_image_2.zip)
2. Call `python download_data.py --kitti_url URL_YOU_RETRIEVED`
5. [Optional] Run `cd submodules/KittiObjective2/ && make` to build the Kitti evaluation code (see [submodules/KittiObjective2/README.md](submodules/KittiObjective2/README.md) for more information)
Running `demo.py` does not require step 4. and step 5. Those steps are only required if you want to train your own model using `train.py` or bench a model against the official evaluation score using `evaluate.py`. Also note, that I strongly recommend using `download_data.py` instead of downloading the data yourself. The script will also extract and prepare the data. See [Managing Folders](README.md#managing-folders) if you like to control where the data is stored.
#### Microsoft Windows Users
This project is developed, tested and maintained on a Linux operation system. It is written to be compatible with Windows, however a few modification are neExcerpt of 8,675 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b9a5d0029b670be3, topic:deep-learning, topic:tensorflow
matched fp:b9a5d0029b670be3, topic:computer-vision, readme:object detection
matched fp:b9a5d0029b670be3, topic:autonomous-driving