Drone-Detection_YOLOv3_PyTorch

PyTorch based YOLOv3 implementation for drone detection in drone gimbal

reference repo : https://github.com/eriklindernoren/PyTorch-YOLOv3

Installation

Clone and install requirements

$ git clone https://github.com/dnjstjr93/Drone-Detection_YOLOv3_PyTorch.git

$ cd Drone-Detection_YOLOv3_PyTorch

$ sudo pip3 install -r requirements.txt

Download pretrained weights

$ cd weights/

$ bash download_weights.sh

Download COCO

$ cd data/

$ bash get_coco_dataset.sh

Download Drone and Airplane Dataset

Download custom dataset such as drone, airplane in here

Inference

Uses pretrained weights to make predictions on images. Below table displays the inference times when using as inputs images scaled to 256x256. The ResNet backbone measurements are taken from the YOLOv3 paper. The Darknet-53 measurement marked shows the inference time of this implementation on my 1080ti card.

$ python3 detect.py --image_folder data/samples/

Detect Video

$ python3 detect_video.py --video_path ./data/video_samples/drone_sample.mp4

Train


$ train.py [-h] [--epochs EPOCHS] [--batch_size BATCH_SIZE]

[--gradient_accumulations GRADIENT_ACCUMULATIONS]

[--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]

[--compute_map COMPUTE_MAP]

[--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

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.

Define Train and Validation Sets

In data/custom/train.txt and data/custom/valid.txt, add paths to images that will be used as train and validation data respectively.

Train

To train on the custom dataset run:


$ python3 train-drone.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.

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