YOLOv2 - GitPress.io

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Origin: YOLO9000: Better, Faster, Stronger

Imporvements comparing to YOLOv1

1. Add BN

2. Hight Resolution classifier

a. Train on ImageNet (224 x 224)<br> b. Resize & Finetune on ImageNet (448 x 448)<br> c. Finetune on dataset<br> d. Get 13 x 13 grid finally (7 x 7 grid before)

3. Use Anchors

4. Fine-Grained Features

a. Lower features are concatenated directly to heigher features<br> b. A new layer is added for that purpose: reorg<br>

$$\left[\begin{array}{llll} a & b & c & d \ e & f & g & h \ i & j & k & l \ m & n & o & p \end{array}\right] \quad===>\quad\left[\begin{array}{ll} a & c \ i & k \end{array}\right] \quad\left[\begin{array}{ll} b & d \ j & l \end{array}\right] \quad\left[\begin{array}{ll} f & h \ n & p \end{array}\right] \quad\left[\begin{array}{ll} e & g \ m & n \end{array}\right]$$

5. Multi-Scale Training

Remove FC layers: Can accpet any size of inputs, enhance model robustness.
Size across 320, 352, ..., 608. Change 10 per epochs<br> [border % 32 = 0, decided by down sampling

Anchor in YOLOv2

Box Generation	#	Avg IOU
Cluster SSE	5	58.7
Cluster IOU	5	58.7
Anchor Boxes	9	58.7
Cluster IOU	9	58.7

1. Anchor size and number

a. Faster RCNN: 9 by hands<br> b. YOLOv2: 5 by K-Means [dist: 1 − IOU(bbox, cluster)]

2. Anchors, Truth BBoxes & Predicted bboxes

Anchors: 0.57273, 0.677385, ..., 9.77052, 9.16828<br>

[10 numbers: ($a_{w_{i}}, a_{h_{i}}$) * 5)]<br> anchors[0] = $a_{w_{i}}$ = $\frac{a_{w_{i}}}W$ * 13

Truth Anchor:

oringinal bbox: $\left[x_{o}, y_{o}, w_{o}, h_{o}\right] \in[0, \mathrm{W} \mid \mathrm{H}]$
normalize original bbox: $[x_{r}, y_{r}, w_{r}, h_{r}]\in[0, 1]$
- $[x_{r}, y_{r}, w_{r}, h_{r}] = [x_{o}/W, y_{o}/H, w_{o}/W, h_{o}/H]$
Transfer to 13 x 13 grid and box: $[x_{s}, y_{s}, w_{s}, h_{s}]\in[0, 13]$
- $\left[x_{i}, y_{i}, w_{i}, h_{i}\right]=\left[x_{r}, y_{r}, w_{r}, h_{r}\right] * (13 \mid 13)$
- save this for calculating
- transfer to 0~1 corresponding to each grid cell
final box: $[x_{f}, y_{f}, w_{f}, h_{f}]\in[0, 1]$
- $x_{f} = x_{i} - i$ // i,j = 13 x 13 grid
- $y_{f} = y_{i} - j$
- $W_{f} = log(W/anchors[0])$
- $H_{f} = log(H/anchors[1])$

Predicted Anchor:

$$\begin{aligned} b_{x} &=\sigma\left(t_{x}\right)+c_{x} \ b_{y} &=\sigma\left(t_{y}\right)+c_{y} \ b_{w} &=p_{w} e^{t_{w}} \ b_{h} &=p_{h} e^{t_{h}} \ \operatorname{Pr}(\text { object }) * I O U(b, \text { object }) &=\sigma\left(t_{o}\right) \end{aligned}$$

The Model Darknet-19

Output of YOLOv2: [0: 25]

one grid cell: $S^2$ * B * [x, y, w, h, $C_{0}, ..., C_{N}$]

detection layer
- 3 x 331024 Conv
- add a passthrough layer
- 1*1 avg pooling

Loss Function

$$\begin{array}{c} \sum_{i=0}^{W} \sum_{j=0}^{H} \sum_{k=0}^{A} 1_{\text {Max IOU}<\text {Thresh}} \lambda_{\text {noobj}} * \left(-b_{i j k}^{o}\right)^{2} \ \qquad\qquad\qquad\qquad\qquad\qquad+1_{t<12800} \lambda_{\text {prior}} * \sum_{r \in(x, y, w, h)}\left(\text {prior}{k}^{r}-b{i j k}^{r}\right)^{2} \ \qquad\qquad\qquad\qquad\qquad\quad+1_{k}^{\text {truth}}\left(\lambda_{\text {coord}} * \sum_{r \in(x, y, w, h)}\left(\operatorname{truth}^{r}-b_{i j k}^{r}\right)\right. \ \qquad\qquad\qquad\qquad\qquad\quad+\lambda_{\text {obj}} * \left(\operatorname{IoU}{\text {truth}}^{k}-b{i j k}^{o}\right)^{2} \ \left.\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+\lambda_{\text {class}} * \left(\sum_{c=1}^{C}\left(\operatorname{truth}^{c}-b_{i j k}^{c}\right)^{2}\right)\right) \end{array}$$

No longer use the square root
Confidence: 1 convert to IoU

The path from YOLO to YOLOv2

	YOLO								YOLOv2
batch norm?		√	√	√	√	√	√	√	√
hi-res classifier?			√	√	√	√	√	√	√
convolutional?				√	√	√	√	√	√
anchor boxes?				√	√
new network?					√	√	√	√	√
dimension priors?						√	√	√	√
location prediction?						√	√	√	√
passthrough?							√	√	√
multi-scale?								√	√
hi-res detector?									√
VOC2007 mAP	63.4	65.8	69.5	69.2	69.6	74.4	75.4	76.8	78.6