Updated Version of YOLOv7 with CUDA Fix
Description
- Integrated from yolov7 official repo https://github.com/WongKinYiu/yolov7, fixed the issue on cuda core can't run on yolov7 model. Tested on conda environment with
python3.9, pytorch=1.11.0, cudatoolkit=1.13
Performance
MS COCO
| Model | Test Size | APtest | AP50test | AP75test | batch 1 fps | batch 32 average time |
|---|---|---|---|---|---|---|
| YOLOv7 | 640 | 51.4% | 69.7% | 55.9% | 161 fps | 2.8 ms |
| YOLOv7-X | 640 | 53.1% | 71.2% | 57.8% | 114 fps | 4.3 ms |
| YOLOv7-W6 | 1280 | 54.9% | 72.6% | 60.1% | 84 fps | 7.6 ms |
| YOLOv7-E6 | 1280 | 56.0% | 73.5% | 61.2% | 56 fps | 12.3 ms |
| YOLOv7-D6 | 1280 | 56.6% | 74.0% | 61.8% | 44 fps | 15.0 ms |
| YOLOv7-E6E | 1280 | 56.8% | 74.4% | 62.1% | 36 fps | 18.7 ms |
Installation on Windows
Conda environment -- Anaconda https://www.anaconda.com/ \ Python -- Python 3.9 installed with Anaconda
Install Anaconda
Select Installation Type : 'just me' \
Anaconda Install Location : Anywhere you want, doesn't have to be on C drive \
Advanved Installation Options : \
Clone yolov7 Repo
Run git clone https://github.com/FlyerJB/YOLOv7-RoboMaster.git on your command prompt to some dir under C:/ drive or your OS drive to avoid Enviornment failure \
Make Conda Environment
Open Conda Command Prompt with Admin Right \
Cd into yolov7 dir with cd <where you clone your yolov7> \
And craete Conda Environment
Conda Create -n <The Name You Like> Python3.9
Activate Conda Environment
Run command Conda activate <The Name You put from previous step>
pip install required packages
Option 1 : Install yolov7 for training on CPU \
pip install -r requirements.txt
Option 2 : Install yolov7 for training on RTX GPU \
pip install -r requirement_nv_gpu.txt \
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
Validate Cuda Installation ( required for nv_gpu training )
Run python or python3 or py to run python \
Run import torch \
Run torch.cuda.is_available() \
If it returns True, it means CUDA is successfully install on your device with Pytorch. \
Then run exit() to exit python
Seperate Dataset
Validate set
10% - 20% of the whole dataset \
It goes under data\val foler with train and label folders inside \
Train set
80% to 90% of the whole dataset \
It goes under data\train foler with train and label folders inside \
Download yolov7 weight
Download yolov7 weight using link below, and put it under YOLOv7-Robomaster dir \ YOLOv7
Trained model
Our own model have been up uploaded to cv-yolov7/model files
Models
Training
With GPU training
# train models
python train.py --workers 1 --device 0 --batch-size -1 --epochs 50 --img 640 --data data/coco_custom.yaml --hyp data/hyp.scratch.custom.yaml --cfg cfg/training/yolov7-custom.yaml --name yolov7-tut3 --weights yolov7.pt With CPU training
# train models
python train.py --workers 8 --device CPU --batch-size -1 --epochs 50 --img 640 --data data/coco_custom.yaml --hyp data/hyp.scratch.custom.yaml --cfg cfg/training/yolov7-custom.yaml --name yolov7-tut3 --weights yolov7.pt