Update: 2022-05-31

Recently, I have released an anchor-free YOLO, inspired by YOLOX & YOLOv7:

This YOLO-Family project has been discontinued for updates and maintenance.

A new and strong YOLO family

Recently, I rebuild my YOLO-Family project !!

Requirements

We recommend you to use Anaconda to create a conda environment:

conda create -n yolo python=3.6

Then, activate the environment:

conda activate yolo

Requirements:

pip install -r requirements.txt

PyTorch >= 1.1.0 and Torchvision >= 0.3.0

Visualize positive samples

You can run following command to visualize positiva sample:

python train.py \
        -d voc \
        --root path/to/your/dataset \
        -m yolov2 \
        --batch_size 2 \
        --vis_targets

Come soon

My better YOLO family

This project

In this project, you can enjoy:

a new and stronger YOLOv1
a new and stronger YOLOv2
a stronger YOLOv3
a stronger YOLOv3 with SPP
a stronger YOLOv3 with DilatedEncoder
YOLOv4 (I'm trying to make it better)
YOLO-Tiny
YOLO-Nano

Future work

Try to make my YOLO-v4 better.
Train my YOLOv1/YOLOv2 with ViT-Base (pretrained by MaskAutoencoder)

Weights

You can download all weights including my DarkNet-53, CSPDarkNet-53, MAE-ViT and YOLO weights from the following links.

Backbone

My Backbone:

YOLOX-Backbone:

YOLO

Experiments

Tricks

Tricks in this project:

Experiments

All experiment results are evaluated on COCO val. All FPS results except YOLO-Nano's are measured on a 2080ti GPU. We will measure the speed of YOLO-Nano on a CPU.

YOLOv1

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLOv1-320	151	25.4	41.5	26.0	4.2	25.0	49.8	10.49	44.54M
YOLOv1-416	128	30.1	47.8	30.9	7.8	31.9	53.3	17.73	44.54M
YOLOv1-512	114	33.1	52.2	34.0	10.8	35.9	54.9	26.85	44.54M
YOLOv1-640	75	35.2	54.7	37.1	14.3	39.5	53.4	41.96	44.54M
YOLOv1-800								65.56	44.54M

YOLOv2

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLOv2-320	147	26.8	44.1	27.1	4.7	27.6	50.8	10.53	44.89M
YOLOv2-416	123	31.6	50.3	32.4	9.1	33.8	54.0	17.79	44.89M
YOLOv2-512	108	34.3	54.0	35.4	12.3	37.8	55.2	26.94	44.89M
YOLOv2-640	73	36.3	56.6	37.7	15.1	41.1	54.0	42.10	44.89M
YOLOv2-800								65.78	44.89M

YOLOv3

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLOv3-320	111	30.8	50.3	31.8	10.0	33.1	50.0	19.57	61.97M
YOLOv3-416	89	34.8	55.8	36.1	14.6	37.5	52.9	33.08	61.97M
YOLOv3-512	77	36.9	58.1	39.3	18.0	40.3	52.2	50.11	61.97M
YOLOv3-608	51	37.0	58.9	39.3	20.5	41.2	49.0	70.66	61.97M
YOLOv3-640	49	36.9	59.0	39.7	21.6	41.6	47.7	78.30	61.97M

YOLOv3 with SPP

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLOv3-SPP-320	110	31.0	50.8	32.0	10.5	33.0	50.4	19.68	63.02M
YOLOv3-SPP-416	88	35.0	56.1	36.4	14.9	37.7	52.8	33.26	63.02M
YOLOv3-SPP-512	75	37.2	58.7	39.1	19.1	40.0	53.0	50.38	63.02M
YOLOv3-SPP-608	50	38.3	60.1	40.7	20.9	41.1	51.2	71.04	63.02M
YOLOv3-SPP-640	48	38.2	60.1	40.4	21.6	41.1	50.5	78.72	63.02M

YOLOv3 with Dilated Encoder

The DilatedEncoder is proposed by YOLOF.

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLOv3-DE-320	109	31.1	51.1	31.7	10.2	32.6	51.2	19.10	57.25M
YOLOv3-DE-416	88	35.0	56.1	36.3	14.6	37.4	53.7	32.28	57.25M
YOLOv3-DE-512	74	37.7	59.3	39.6	17.9	40.4	54.4	48.90	57.25M
YOLOv3-DE-608	50	38.7	60.5	40.8	20.6	41.7	53.1	68.96	57.25M
YOLOv3-DE-640	48	38.7	60.2	40.7	21.3	41.7	51.7	76.41	57.25M

YOLOv4

I'm still trying to make it better.

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLOv4-320	89	39.2	58.6	40.9	16.9	44.1	59.2	16.38	58.14M
YOLOv4-416	84	41.7	61.6	44.2	22.0	46.6	57.7	27.69	58.14M
YOLOv4-512	70	42.9	63.1	46.1	24.5	48.3	56.5	41.94	58.14M
YOLOv4-608	51	43.0	63.4	46.1	26.7	48.6	53.9	59.14	58.14M

YOLO-Tiny

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLO-Tiny-320	143	26.4	44.5	26.8	8.8	28.2	42.4	2.17	7.66M
YOLO-Tiny-416	130	28.2	47.6	28.8	11.6	31.5	41.4	3.67	7.82M
YOLO-Tiny-512	118	28.8	48.6	29.4	13.3	33.4	38.3	5.57	7.82M

YOLO-Nano

The FPS is measured on i5-1135G& CPU. Any accelerated deployments that would help speed up detection are not done.

	FPS	AP	AP50	AP75	APs	APm	APl	GFLOPs	Params
YOLO-Nano-320	25	18.4	33.7	17.8	3.9	17.5	33.1	0.64	1.86M
YOLO-Nano-416	15	21.4	38.5	20.9	6.5	21.4	34.8	0.99	1.86M
YOLO-Nano-512	10	22.4	40.7	22.1	8.0	24.0	33.2	1.65	1.86M

Dataset

VOC Dataset

My BaiduYunDisk

BaiduYunDisk: https://pan.baidu.com/s/1tYPGCYGyC0wjpC97H-zzMQ Password：4la9

Download VOC2007 trainval & test

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>

Download VOC2012 trainval

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

My BaiduYunDisk

BaiduYunDisk: https://pan.baidu.com/s/1xAPk8fnaWMMov1VEjr8-zA Password：6vhp

On Ubuntu system, you might use the command jar xvf xxx.zip to unzip the train2017.zip and test2017.zip files since they are larger than 2G (As far as I know, unzip operation can't process the zip file which is larger than 2G.).

MSCOCO Dataset

Download MSCOCO 2017 dataset

Just run sh data/scripts/COCO2017.sh. You will get COCO train2017, val2017, test2017.

Train

For example:

python train.py --cuda \
                -d coco \
                -m yolov1 \
                -ms \
                --ema \
                --batch_size 16 \
                --root path/to/dataset/

You can run python train.py -h to check all optional argument. Or you can just run the shell file, for example:

sh train_yolov1.sh

If you have multi gpus like 8, and you put 4 images on each gpu:

python -m torch.distributed.launch --nproc_per_node=8 train.py -d coco \
                                                               --cuda \
                                                               -m yolov1 \
                                                               -ms \
                                                               --ema \
                                                               -dist \
                                                               --sybn \
                                                               --num_gpu 8 \
                                                               --batch_size 4 \
                                                               --root path/to/dataset/

Attention, --batch_size is the number of batchsize on per GPU, not all GPUs.

I have upload all training log files. For example, 1-v1.txt contains all the output information during the training YOLOv1.

It is strongly recommended that you open the training shell file to check how I train each YOLO detector.

Test

For example:

python test.py -d coco \
               --cuda \
               -m yolov4 \
               --weight path/to/weight \
               --img_size 640 \
               --root path/to/dataset/ \
               --show

Evaluation

For example

python eval.py -d coco-val \
               --cuda \
               -m yolov1 \
               --weight path/to/weight \
               --img_size 640 \
               --root path/to/dataset/

Evaluation on COCO-test-dev

To run on COCO_test-dev(You must be sure that you have downloaded test2017):

python eval.py -d coco-test \
               --cuda \
               -m yolov1 \
               --weight path/to/weight \
               --img_size 640 \
                --root path/to/dataset/

You will get a coco_test-dev.json file. Then you should follow the official requirements to compress it into zip format and upload it the official evaluation server.

yjh0410/PyTorch_YOLO-Family

yjh0410

Reviews

Repository Details

Update: 2022-05-31

A new and strong YOLO family

Requirements

Visualize positive samples

Come soon

This project

Future work

Weights

Backbone

YOLO

Experiments

Tricks

Experiments

YOLOv1

YOLOv2

YOLOv3

YOLOv3 with SPP

YOLOv3 with Dilated Encoder

YOLOv4

YOLO-Tiny

YOLO-Nano

Dataset

VOC Dataset

My BaiduYunDisk

Download VOC2007 trainval & test

Download VOC2012 trainval

My BaiduYunDisk

MSCOCO Dataset

Download MSCOCO 2017 dataset

Train

Test

Evaluation

Evaluation on COCO-test-dev

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