UAV Object Detection using PyTorch-YOLOv3

Use Tello drones to perform object detection, using PyTorch implementation of YOLOv3 to support training, inference and evaluation. Visit https://github.com/ssling0817/Drone-Autonomous-Navigation-Project-Guildline for algorithm explanation.

Installation

Clone and install requirements

$ git clone https://github.com/ssling0817/UAV_Object_Detection
$ cd PyTorch-YOLOv3/
$ sudo pip3 install -r requirements.txt

Train on Custom Dataset

Custom model

Run the commands below to create a custom model definition, replacing <num-classes> with the number of classes in your dataset.

$ cd config/                                # Navigate to config dir
$ bash create_custom_model.sh <num-classes> # Will create custom model 'yolov3-custom.cfg'

Classes

Add class names to data/custom/classes.names. This file should have one row per class name.

Image Folder

Move the images of your dataset to data/custom/images/.

Annotation Folder

Move your annotations to data/custom/labels/. The dataloader expects that the annotation file corresponding to the image data/custom/images/train.jpg has the path data/custom/labels/train.txt. Each row in the annotation file should define one bounding box, using the syntax label_idx x_center y_center width height. The coordinates should be scaled [0, 1], and the label_idx should be zero-indexed and correspond to the row number of the class name in data/custom/classes.names.

Create Train and Validation Sets

To train on the custom dataset run:

$ python3 merge_names.py 

It will automatically create train and validation sets in data/custom/train.txt anddata/custom/vak.txt which are yout paths to images that will be used as train and validation data respectively.

Train

To train on the custom dataset run:

$ python3 train.py --model_def config/yolov3-custom.cfg --data_config config/custom.data

Add --pretrained_weights weights/darknet53.conv.74 to train using a backend pretrained on ImageNet.

Test with UAV

To test on the custom dataset using UAV:

$ python3 test_detection.py --model_def config/yolov3-custom.cfg --data_config config/custom.data

Change detect target

Change target in detect_UAV.py, it will change the detect target of the drone. Make sure this target exists in your data/custom/classes.names.

Download pretrained weights

$ cd weights/
$ bash download_weights.sh

Download COCO

$ cd data/
$ bash get_coco_dataset.sh

Test

Evaluates the model on COCO test.

$ python3 test.py --weights_path weights/yolov3.weights

Model	mAP (min. 50 IoU)
YOLOv3 608 (paper)	57.9
YOLOv3 608 (this impl.)	57.3
YOLOv3 416 (paper)	55.3
YOLOv3 416 (this impl.)	55.5

Train

$ train.py [-h] [--epochs EPOCHS]
                [--model_def MODEL_DEF] [--data_config DATA_CONFIG]
                [--pretrained_weights PRETRAINED_WEIGHTS] [--n_cpu N_CPU]
                [--img_size IMG_SIZE]
                [--checkpoint_interval CHECKPOINT_INTERVAL]
                [--evaluation_interval EVALUATION_INTERVAL]
                [--multiscale_training MULTISCALE_TRAINING]

Example (COCO)

To train on COCO using a Darknet-53 backend pretrained on ImageNet run:

$ python3 train.py --data_config config/coco.data  --pretrained_weights weights/darknet53.conv.74

Training log

---- [Epoch 7/100, Batch 7300/14658] ----
+------------+--------------+--------------+--------------+
| Metrics    | YOLO Layer 0 | YOLO Layer 1 | YOLO Layer 2 |
+------------+--------------+--------------+--------------+
| grid_size  | 16           | 32           | 64           |
| loss       | 1.554926     | 1.446884     | 1.427585     |
| x          | 0.028157     | 0.044483     | 0.051159     |
| y          | 0.040524     | 0.035687     | 0.046307     |
| w          | 0.078980     | 0.066310     | 0.027984     |
| h          | 0.133414     | 0.094540     | 0.037121     |
| conf       | 1.234448     | 1.165665     | 1.223495     |
| cls        | 0.039402     | 0.040198     | 0.041520     |
| cls_acc    | 44.44%       | 43.59%       | 32.50%       |
| recall50   | 0.361111     | 0.384615     | 0.300000     |
| recall75   | 0.222222     | 0.282051     | 0.300000     |
| precision  | 0.520000     | 0.300000     | 0.070175     |
| conf_obj   | 0.599058     | 0.622685     | 0.651472     |
| conf_noobj | 0.003778     | 0.004039     | 0.004044     |
+------------+--------------+--------------+--------------+
Total Loss 4.429395
---- ETA 0:35:48.821929

Tensorboard

Track training progress in Tensorboard:

• Initialize training
• Run the command below
• Go to http://localhost:6006/

$ tensorboard --logdir='logs' --port=6006

Storing the logs on a slow drive possibly leads to a significant training speed decrease.

You can adjust the log directory using --logdir <path> when running tensorboard or the train.py.

Credit

YOLOv3: An Incremental Improvement

Joseph Redmon, Ali Farhadi

[Paper] [Project Webpage] [Authors' Implementation]

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