# PyTorch-YOLOv3-kitti

**Repository Path**: zhang--hao/PyTorch-YOLOv3-kitti

## Basic Information

- **Project Name**: PyTorch-YOLOv3-kitti
- **Description**: use yolov3 pytorch to train kitti 
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PyTorch-YOLOv3-Kitti
Minimal implementation of YOLOv3 in PyTorch.
And Training from Kitti dataset

## Table of Contents
- [PyTorch-YOLOv3](#pytorch-yolov3-kitti)
  * [Table of Contents](#table-of-contents)
  * [Paper](#paper)
  * [Installation](#installation)
  * [Inference](#inference)
  * [Video](#video)
  * [Test](#test)
  * [Detect](#detect)
  * [Train](#train)
  * [Credit](#credit)


## HI THERE~ THIS repo is forked from [eriklindernoren](https://github.com/eriklindernoren/PyTorch-YOLOv3)

## Installation
    $ git clone https://github.com/packyan/PyTorch-YOLOv3-kitti.git
    $ cd PyTorch-YOLOv3-kitti/
    $ sudo pip3 install -r requirements.txt


##### Download pretrained weights
if you wan use pretrained darknet-53 on IMAGENET weights, please download [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74),and put it into `checkpoints/`

if you just want a pretrained weights on kitti dataset for test or detect, please download [pretrained weights file](https://drive.google.com/file/d/1BRJDDCMRXdQdQs6-x-3PmlzcEuT9wxJV/view?usp=sharing), and put it into `weights` folder, the path:
`weights/yolov3-kitti.weights`

##### Download Kitti 

[The KITTI Vision Benchmark Suite](http://www.cvlibs.net/datasets/kitti/eval_object.php)

and you should transfrom kitti lable to coco label, by using [label_transform](label_transform/README.md)

## Inference
Uses pretrained weights to make predictions on images. `weights/yolov3-kitti.weights` was trained by kitti data set.
`python3 detect.py --image_folder /data/samples`

<p align="center"><img src="assets/12.png" width="480"\></p>
<p align="center"><img src="assets/20.png" width="480"\></p>
<p align="center"><img src="assets/18.png" width="480"\></p>
<p align="center"><img src="assets/3.png" width="480"\></p>

Small objects detection

<p align="center"><img src="assets/4.png" width="480"\></p>

## Detect

run`detect.py` to detect objects, and please  put samples into `data/samples`
defult weights files is `weights/kitti.weights`

## Video

run `video.py` to detect objects from a webcam or a video file.
On I7 7700 8G GTX1070 FPS is 22 cause some problems, test resized images is really faster than resizeing images and then inference.
## Test

run `test.py`

## Train
Please run `python3 -m visdom.server` first to vislizer your training loss.

Data augmentation as well as additional training tricks remains to be implemented. PRs are welcomed!
```
    train.py [-h] [--epochs EPOCHS] [--image_folder IMAGE_FOLDER]
                [--batch_size BATCH_SIZE]
                [--model_config_path MODEL_CONFIG_PATH]
                [--data_config_path DATA_CONFIG_PATH]
                [--weights_path WEIGHTS_PATH] [--class_path CLASS_PATH]
                [--conf_thres CONF_THRES] [--nms_thres NMS_THRES]
                [--n_cpu N_CPU] [--img_size IMG_SIZE]
                [--checkpoint_interval CHECKPOINT_INTERVAL]
                [--checkpoint_dir CHECKPOINT_DIR]
```
## Paper
### YOLOv3: An Incremental Improvement
_Joseph Redmon, Ali Farhadi_ <br>

**Abstract** <br>
We present some updates to YOLO! We made a bunch
of little design changes to make it better. We also trained
this new network that’s pretty swell. It’s a little bigger than
last time but more accurate. It’s still fast though, don’t
worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP,
as accurate as SSD but three times faster. When we look
at the old .5 IOU mAP detection metric YOLOv3 is quite
good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared
to 57.5 AP50 in 198 ms by RetinaNet, similar performance
but 3.8× faster. As always, all the code is online at
https://pjreddie.com/yolo/.

[[Paper]](https://pjreddie.com/media/files/papers/YOLOv3.pdf) [[Original Implementation]](https://github.com/pjreddie/darknet)


## Credit
```
@article{yolov3,
  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},
  year={2018}
}
```