A PyTorch implementation of YOLOv3, with support for training, inference and evaluation. Pretrained on LISA dataset and GTSDB dataset.
$ git clone https://github.com/chien-lung/PyTorch-YOLOv3.git
$ cd PyTorch-YOLOv3/
$ python3 -m venv yolo_venv
$ source yolo_venv/bin/activate
$ sudo pip install -r requirements.txt
$ cd weights
$ bash download_weights.sh
$ cd datasets
$ bash download_datasets.sh
Uses pretrained weights to make predictions on images.
$ python detect.py --image_folder IMAGE_FOLDER
--model_def MODEL_CONFIG.cfg
--weights_path WEIGHT.pth
--class_path CLASS_DEFINITION.names
Example:
$ python detect.py --image_folder data/samples
--model_def config/yolov3.cfg
--weights_path weights/yolov3.weights
--class_path data/coco.names
$ 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]
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
---- [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
GTSDB dataset only contain 600 labeled images for training and 300 unlabeled images for testing. Due to the limited number of images in GTSDB dataset, we integrate GTSRB when training. Specifically, a random pasting algorithm is proposed here, as shown below.
$ mv datasets/signs data/GTSDB # Running download_dataset.sh before executing this
To train on the GTSDB dataset run:
$ python train_GTSDB.py --model_def config/yolov3-GTSDB.cfg --data_config config/GTSDB.data
Add --pretrained_weights weights/darknet53.conv.74 to train using a backend pretrained on ImageNet.
Note: here we run train_GTSDB.py because the the loading image method is different, which including random pasting GTSRB data.
Run the commands below to create a custom model definition, replacing NUM_CLASSES with the number of classes in your dataset.
$ # Remind: DO NOT run this script multiple times.
$ cd config/ # Navigate to config dir
$ bash create_custom_model.sh NUM_CLASSES # Create custom model 'yolov3-custom.cfg'
To train on the custom dataset run:
$ python 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.
Take custom as example, files should be modified:
data
└─custom
├─classes.names
├─images
├─labels
├─train.txt
└─valid.txt
config
├─custom.data
└─yolov3-custom.cfg
Add class names to data/custom/classes.names. This file should have one row per class name.
Move the images of your dataset to data/custom/images/.
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.
If the annotation file doesn't define, then it will consider no object in the image.
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.
In data/custom/train.txt and data/custom/valid.txt, add paths to images that will be used as train and validation data respectively.
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] [Project Webpage] [Authors' Implementation]
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}