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Minimal implementation of YOLOv3 in PyTorch.

Table of Contents


YOLOv3: An Incremental Improvement

Joseph Redmon, Ali Farhadi

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

[Paper] [Original Implementation]


$ git clone
$ cd PyTorch-YOLOv3/
$ sudo pip3 install -r requirements.txt
Download pretrained weights
$ cd weights/
$ bash
Download COCO
$ cd data/
$ bash


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 --image_folder /data/samples


Evaluates the model on COCO test.

$ python3 --weights_path weights/yolov3.weights
Model mAP (min. 50 IoU)
YOLOv3 (paper) 57.9
YOLOv3 (this impl.) 58.2


Data augmentation as well as additional training tricks remains to be implemented. PRs are welcomed! [-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]
                [--n_cpu N_CPU] [--img_size IMG_SIZE]
                [--checkpoint_model CHECKPOINT_MODEL]
                [--checkpoint_interval CHECKPOINT_INTERVAL]
                [--checkpoint_dir CHECKPOINT_DIR]


Track training progress in Tensorboard:

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


  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},