PyTorch-YOLOv3-Kitti

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

PyTorch-YOLOv3
- Table of Contents
- Paper
- Installation
- Inference
- Video
- Test
- Detect
- Train
- Credit

HITHERE THIS repo is forked from eriklindernoren

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,and put it into checkpoints/

if you just want a pretrained weights on kitti dataset for test or detect, please download pretrained weights file, and put it into weights folder, the path: weights/yolov3-kitti.weights

Download Kitti

The KITTI Vision Benchmark Suite

and you should transfrom kitti lable to coco label, by using label_transform

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

Small objects detection

Detect

rundetect.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.

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

Abstract
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] [Original Implementation]

Credit

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

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