PyTorch-YOLOv3

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]

Table of Contents

• PyTorch-YOLOv3
  • Installation
  • Inference
  • Test
  • Train
  • Credit

Installation

Clone and install requirements

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

Download pretrained weights

$ cd weights/
$ bash download_yolov3_weights.sh   # Downloads default YOLOv3 weights
$ bash download_tiny_weights.sh     # Downloads tiny YOLOv3 weights

Download COCO

$ cd data/
$ bash get_coco_dataset.sh

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.

Backbone	GPU	FPS
ResNet-101	Titan X	53
ResNet-152	Titan X	37
Darknet-53 (paper)	Titan X	76
Darknet-53 (this impl.)	1080ti	74

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

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] [--batch_size BATCH_SIZE]
                [--gradient_accumulations GRADIENT_ACCUMULATIONS]
                [--model_config_path MODEL_CONFIG_PATH]
                [--data_config_path DATA_CONFIG_PATH]
                [--weights_path WEIGHTS_PATH] [--class_path CLASS_PATH]
                [--n_cpu N_CPU] [--img_size IMG_SIZE]
                [--checkpoint_interval CHECKPOINT_INTERVAL]
                [--compute_map COMPUTE_MAP]
                [--multi_scale_training MULTI_SCALE_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

Track training progress in Tensorboard:

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

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

Credit

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