## 1. Model Structure

YOLOv5 (v6.0/6.1) consists of:
- **Backbone**: `New CSP-Darknet53`
- **Neck**: `SPPF`, `New CSP-PAN`
- **Head**: `YOLOv3 Head`

Model structure (`yolov5l.yaml`):

![yolov5](https://user-images.githubusercontent.com/31005897/172404576-c260dcf9-76bb-4bc8-b6a9-f2d987792583.png)


Some minor changes compared to previous versions:

1. Replace the `Focus` structure with `6x6 Conv2d`(more efficient, refer #4825)  
2. Replace the `SPP` structure with `SPPF`(more than double the speed)

<details markdown>
<summary>test code</summary>

```python
import time
import torch
import torch.nn as nn


class SPP(nn.Module):
    def __init__(self):
        super().__init__()
        self.maxpool1 = nn.MaxPool2d(5, 1, padding=2)
        self.maxpool2 = nn.MaxPool2d(9, 1, padding=4)
        self.maxpool3 = nn.MaxPool2d(13, 1, padding=6)

    def forward(self, x):
        o1 = self.maxpool1(x)
        o2 = self.maxpool2(x)
        o3 = self.maxpool3(x)
        return torch.cat([x, o1, o2, o3], dim=1)


class SPPF(nn.Module):
    def __init__(self):
        super().__init__()
        self.maxpool = nn.MaxPool2d(5, 1, padding=2)

    def forward(self, x):
        o1 = self.maxpool(x)
        o2 = self.maxpool(o1)
        o3 = self.maxpool(o2)
        return torch.cat([x, o1, o2, o3], dim=1)


def main():
    input_tensor = torch.rand(8, 32, 16, 16)
    spp = SPP()
    sppf = SPPF()
    output1 = spp(input_tensor)
    output2 = sppf(input_tensor)

    print(torch.equal(output1, output2))

    t_start = time.time()
    for _ in range(100):
        spp(input_tensor)
    print(f"spp time: {time.time() - t_start}")

    t_start = time.time()
    for _ in range(100):
        sppf(input_tensor)
    print(f"sppf time: {time.time() - t_start}")


if __name__ == '__main__':
    main()
```

result:
```
True
spp time: 0.5373051166534424
sppf time: 0.20780706405639648
```

</details>



## 2. Data Augmentation

- Mosaic
<img src="https://user-images.githubusercontent.com/31005897/159109235-c7aad8f2-1d4f-41f9-8d5f-b2fde6f2885e.png#pic_center" width=80%>

- Copy paste
<img src="https://user-images.githubusercontent.com/31005897/159116277-91b45033-6bec-4f82-afc4-41138866628e.png#pic_center" width=80%>

- Random affine(Rotation, Scale, Translation and Shear)
<img src="https://user-images.githubusercontent.com/31005897/159109326-45cd5acb-14fa-43e7-9235-0f21b0021c7d.png#pic_center" width=80%>

- MixUp
<img src="https://user-images.githubusercontent.com/31005897/159109361-3b24333b-f481-478b-ae00-df7838f0b5cd.png#pic_center" width=80%>

- Albumentations
- Augment HSV(Hue, Saturation, Value)
<img src="https://user-images.githubusercontent.com/31005897/159109407-83d100ba-1aba-4f4b-aa03-4f048f815981.png#pic_center" width=80%>

- Random horizontal flip
<img src="https://user-images.githubusercontent.com/31005897/159109429-0d44619a-a76a-49eb-bfc0-6709860c043e.png#pic_center" width=80%>



## 3. Training Strategies

- Multi-scale training(0.5~1.5x)
- AutoAnchor(For training custom data)
- Warmup and Cosine LR scheduler
- EMA(Exponential Moving Average)
- Mixed precision
- Evolve hyper-parameters



## 4. Others

### 4.1 Compute Losses

The YOLOv5 loss consists of three parts: 

- Classes loss(BCE loss)
- Objectness loss(BCE loss)
- Location loss(CIoU loss)

![loss](https://latex.codecogs.com/svg.image?Loss=\lambda_1L_{cls}+\lambda_2L_{obj}+\lambda_3L_{loc})

### 4.2 Balance Losses
The objectness losses of the three prediction layers(`P3`, `P4`, `P5`) are weighted differently. The balance weights are `[4.0, 1.0, 0.4]` respectively.

![obj_loss](https://latex.codecogs.com/svg.image?L_{obj}=4.0\cdot&space;L_{obj}^{small}+1.0\cdot&space;L_{obj}^{medium}+0.4\cdot&space;L_{obj}^{large})

### 4.3 Eliminate Grid Sensitivity
In YOLOv2 and YOLOv3, the formula for calculating the predicted target information is:  

![b_x](https://latex.codecogs.com/svg.image?b_x=\sigma(t_x)+c_x)  
![b_y](https://latex.codecogs.com/svg.image?b_y=\sigma(t_y)+c_y)  
![b_w](https://latex.codecogs.com/svg.image?b_w=p_w\cdot&space;e^{t_w})  
![b_h](https://latex.codecogs.com/svg.image?b_h=p_h\cdot&space;e^{t_h})

<img src="https://user-images.githubusercontent.com/31005897/158508027-8bf63c28-8290-467b-8a3e-4ad09235001a.png#pic_center" width=40%>



In YOLOv5, the formula is:  

![bx](https://latex.codecogs.com/svg.image?b_x=(2\cdot\sigma(t_x)-0.5)+c_x)  
![by](https://latex.codecogs.com/svg.image?b_y=(2\cdot\sigma(t_y)-0.5)+c_y)  
![bw](https://latex.codecogs.com/svg.image?b_w=p_w\cdot(2\cdot\sigma(t_w))^2)    
![bh](https://latex.codecogs.com/svg.image?b_h=p_h\cdot(2\cdot\sigma(t_h))^2)  

Compare the center point offset before and after scaling. The center point offset range is adjusted from (0, 1) to (-0.5, 1.5).
Therefore, offset can easily get 0 or 1.

<img src="https://user-images.githubusercontent.com/31005897/158508052-c24bc5e8-05c1-4154-ac97-2e1ec71f582e.png#pic_center" width=40%>

Compare the height and width scaling ratio(relative to anchor) before and after adjustment. The original yolo/darknet box equations have a serious flaw. Width and Height are completely unbounded as they are simply out=exp(in), which is dangerous, as it can lead to runaway gradients, instabilities, NaN losses and ultimately a complete loss of training. [refer this issue](https://github.com/ultralytics/yolov5/issues/471#issuecomment-662009779)

<img src="https://user-images.githubusercontent.com/31005897/158508089-5ac0c7a3-6358-44b7-863e-a6e45babb842.png#pic_center" width=40%>


### 4.4 Build Targets
Match positive samples:

- Calculate the aspect ratio of GT and Anchor Templates

![rw](https://latex.codecogs.com/svg.image?r_w=w_{gt}/w_{at})

![rh](https://latex.codecogs.com/svg.image?r_h=h_{gt}/h_{at})

![rwmax](https://latex.codecogs.com/svg.image?r_w^{max}=max(r_w,1/r_w))

![rhmax](https://latex.codecogs.com/svg.image?r_h^{max}=max(r_h,1/r_h))

![rmax](https://latex.codecogs.com/svg.image?r^{max}=max(r_w^{max},r_h^{max}))

![match](https://latex.codecogs.com/svg.image?r^{max}<{\rm&space;anchor_t})

<img src="https://user-images.githubusercontent.com/31005897/158508119-fbb2e483-7b8c-4975-8e1f-f510d367f8ff.png#pic_center" width=70%>

- Assign the successfully matched Anchor Templates to the corresponding cells

<img src="https://user-images.githubusercontent.com/31005897/158508771-b6e7cab4-8de6-47f9-9abf-cdf14c275dfe.png#pic_center" width=70%>

- Because the center point offset range is adjusted from (0, 1) to (-0.5, 1.5). GT Box can be assigned to more anchors.

<img src="https://user-images.githubusercontent.com/31005897/158508139-9db4e8c2-cf96-47e0-bc80-35d11512f296.png#pic_center" width=70%>

## Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):

- **Notebooks** with free GPU: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>


## Status

<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>

If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.