退购1.1定位算法

2023-08-10 12:25:23 +08:00
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+---
+comments: true
+description: 'Ultralytics YOLOv5 Docs: Learn model structure, data augmentation &amp; training strategies. Build targets and the losses of object detection.'
+---
+
+## 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%>