/PyTorch_YOLO_Tutorial

YOLO Tutorial

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PyTorch_YOLO_Tutorial

YOLO Tutorial

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Introduction

Here is the source code for an introduction to YOLO. We adopted the core concepts of YOLOv1~v4, YOLOX and YOLOv7 for this project and made the necessary adjustments. By learning how to construct the well-known YOLO detector, we hope that newcomers can enter the field of object detection without any difficulty.

Book: The technical books that go along with this project's code is being reviewed, please be patient.

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

My environment:

  • PyTorch = 1.9.1
  • Torchvision = 0.10.1

At least, please make sure your torch is version 1.x.

Training Configuration

Configuration
Per GPU Batch Size 16
Init Lr 0.01
Warmup Scheduler Linear
Lr Scheduler Linear
Optimizer SGD
Multi Scale Train True (320 ~ 640)

Due to my limited computing resources, I can not use a larger multi-scale range, such as 320-960.

Experiments

VOC

  • Download VOC.
cd <PyTorch_YOLO_Tutorial>
cd dataset/scripts/
sh VOC2007.sh
sh VOC2012.sh
  • Check VOC
cd <PyTorch_YOLO_Tutorial>
python dataset/voc.py
  • Train on VOC

For example:

python train.py --cuda -d voc --root path/to/VOCdevkit -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
Model Backbone Scale IP Epoch APval
0.5		 FPS3090
FP32-bs1		 Weight
YOLOv1 ResNet-18 640 √ 150 76.7 ckpt
YOLOv2 DarkNet-19 640 √ 150 79.8 ckpt
YOLOv3 DarkNet-53 640 √ 150 82.0 ckpt
YOLOv4 CSPDarkNet-53 640 √ 150 83.6 ckpt
YOLOX-L CSPDarkNet-L 640 √ 150 84.6 ckpt
YOLOv7-Large ELANNet-Large 640 √ 150 86.0 ckpt

All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on VOC2007 test. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.

COCO

  • Download COCO.
cd <PyTorch_YOLO_Tutorial>
cd dataset/scripts/
sh COCO2017.sh
  • Check COCO
cd <PyTorch_YOLO_Tutorial>
python dataset/coco.py
  • Train on COCO

For example:

python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
  • Redesigned YOLOv1~v2:
Model Backbone Scale Epoch FPS APval
0.5:0.95		 APval
0.5		 FLOPs
(G)		 Params
(M)		 Weight
YOLOv1 ResNet-18 640 150 27.9 47.5 37.8 21.3 ckpt
YOLOv2 DarkNet-19 640 150 32.7 50.9 53.9 30.9 ckpt
  • YOLOv3:
Model Backbone Scale Epoch FPS APval
0.5:0.95		 APval
0.5		 FLOPs
(G)		 Params
(M)		 Weight
YOLOv3-Tiny DarkNet-Tiny 640 250 7.0 2.3
YOLOv3 DarkNet-53 640 250 42.9 63.5 167.4 54.9 ckpt
  • YOLOv4:
Model Backbone Scale Epoch FPS APval
0.5:0.95		 APval
0.5		 FLOPs
(G)		 Params
(M)		 Weight
YOLOv4-Tiny CSPDarkNet-Tiny 640 250
YOLOv4 CSPDarkNet-53 640 250 46.6 65.8 162.7 61.5 ckpt
  • YOLOv5:
Model Backbone Scale Epoch FPS APval
0.5:0.95		 APval
0.5		 FLOPs
(G)		 Params
(M)		 Weight
YOLOv5-N CSPDarkNet-N 640 250 7.7 2.4
YOLOv5-S CSPDarkNet-S 640 250 27.1 9.0
YOLOv5-M CSPDarkNet-M 640 250 74.3 25.4
YOLOv5-L CSPDarkNet-L 640 250 46.7 65.5 155.6 54.2 ckpt
  • YOLOX:
Model Backbone Scale Epoch FPS APval
0.5:0.95		 APval
0.5		 FLOPs
(G)		 Params
(M)		 Weight
YOLOX-L CSPDarkNet-L 640 300 46.6 66.1 155.4 54.2 ckpt
  • YOLOv7:
Model Backbone Scale Epoch FPS APval
0.5:0.95		 APval
0.5		 FLOPs
(G)		 Params
(M)		 Weight
YOLOv7-T ELANNet-Tiny 640 300 38.0 56.8 22.6 7.9 ckpt
YOLOv7-L ELANNet-Large 640 300 144.6 44.0

  • All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on COCO val2017. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.

  • The reproduced YOLOv5's head is the Decoupled Head, which is why the FLOPs and Params are higher than the official YOLOv5. Due to my limited computing resources, I can not align the training configuration with the official YOLOv5, so I cannot fully replicate the official performance. The YOLOv5 I reproduce is for learning purposes only.

  • Due to my limited computing resources, I had to abandon training on other YOLO detectors, including YOLOv7-Huge and YOLOv8-Nano~Large. If you are interested in these models and have trained them using the code from this project, I would greatly appreciate it if you could share the trained weight files with me.

Train

Single GPU

sh train.sh

You can change the configurations of train.sh, according to your own situation.

You also can add --vis_tgt to check the images and targets during the training stage. For example:

python train.py --cuda -d coco --root path/to/coco -v yolov1 --vis_tgt

Multi GPUs

sh train_ddp.sh

You can change the configurations of train_ddp.sh, according to your own situation.

In the event of a training interruption, you can pass --resume the latest training weight path (None by default) to resume training. For example:

python train.py \
        --cuda \
        -d coco \
        -v yolov1 \
        -bs 16 \
        --max_epoch 300 \
        --wp_epoch 3 \
        --eval_epoch 10 \
        --ema \
        --fp16 \
        --resume weights/coco/yolov1/yolov1_epoch_151_39.24.pth

Then, training will continue from 151 epoch.

Test

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

For YOLOv7, since it uses the RepConv in PaFPN, you can add --fuse_repconv to fuse the RepConv block.

python test.py -d coco \
               --cuda \
               -v yolov7_large \
               --fuse_repconv \
               --img_size 640 \
               --weight path/to/weight \
               --root path/to/dataset/ \
               --show

Evaluation

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

Demo

I have provide some images in data/demo/images/, so you can run following command to run a demo:

python demo.py --mode image \
               --path_to_img data/demo/images/ \
               -v yolov1 \
               --img_size 640 \
               --cuda \
               --weight path/to/weight

If you want run a demo of streaming video detection, you need to set --mode to video, and give the path to video --path_to_vid。

python demo.py --mode video \
               --path_to_img data/demo/videos/your_video \
               -v yolov1 \
               --img_size 640 \
               --cuda \
               --weight path/to/weight

If you want run video detection with your camera, you need to set --mode to camera。

python demo.py --mode camera \
               -v yolov1 \
               --img_size 640 \
               --cuda \
               --weight path/to/weight

Tracking

Our project also supports multi-object tracking tasks. We use the YOLO of this project as the detector, following the "tracking-by-detection" framework, and use the simple and efficient ByteTrack as the tracker.

  • images tracking
python track.py --mode image \
                --path_to_img path/to/images/ \
                -dt yolov2 \
                -tk byte_tracker \
                --weight path/to/coco_pretrained/ \
                -size 640 \
                --cuda \
                --show
  • video tracking
python track.py --mode video \
                --path_to_img path/to/video/ \
                -dt yolov2 \
                -tk byte_tracker \
                --weight path/to/coco_pretrained/ \
                -size 640 \
                --cuda \
                --show
  • camera tracking
python track.py --mode camera \
                -dt yolov2 \
                -tk byte_tracker \
                --weight path/to/coco_pretrained/ \
                -size 640 \
                --cuda \
                --show