Presentation file for lab seminar.

1. SSD:
Single Shot Multibox Detector
NamHyuk Ahn

2. Object Detection
- mean Average Precision (mAP)
• Popular eval metric
• Compute average precision
for single class, and average
them over all classes
• Detections is True-positive
if box is overlap with ground-
truth more than some threshold
(usually use 0.5)

3. Object Detection
- R-CNN Family
• Most popular detection method in deep learning
• Use region proposal method <- make model slow
• Good accuracy (Faster: 73.2% mAP), but very slow
• R-CNN: 50 sec/img, Fast: 2 sec/img, Faster: 0.2 sec/img (7 FPS)
- YOLO (You Only Look Once)
• Real-time (45 FPS), but low accuracy (63.4% mAP)

4. YOLO:
You Only Look Once
- Single shot detector model
• Not separate classification and bbox regression
- Divide image into S x S grid (7x7 in paper)
• Within each grid cell, (4+1)*B + C vector,
• B: # of boxes in each grid (2 in paper)
C: # of classes (20 in paper)
(4+1): 4 box coord + 1 box confidence
- Direct prediction using CNN with regression loss

5. YOLO:
You Only Look Once
- Operate on a single-scale feature map (last pool)
• Bad accuracy with large or small object
- Predict bbox using fc layer
- Hard data augmentation, 448x448 input image
- Use customized CNN architecture

6. SSD:
Single Shot Multibox Detector
- Multi-scale feature maps for detection
• Add conv layer at the end of base network, decrease size progressively
• Concat output of multi-scale feature map at the last layer
- Convolutional predictors for detection
• YOLO use fc layer, but SSD use 3x3 conv kernel

7. SSD:
Single Shot Multibox Detector
- Default boxes and aspect ratios
• Set default boxes at each location, and predict offset relative to
corresponding default box
• output dims: (C+4)K*M*N,
K=# of default box, C=# of classes, MN=feature dims

8. SSD:
Single Shot Multibox Detector
- Default boxes and aspect ratios
• Use 6 default boxes at each feature cell
• { 1, 2, 3, 1/2, 1/3 } aspect ratio boxes + 1 box with 1 aspect ratio
• Set 3 boxes in conv4_3 to reduce computation

9. SSD:
Single Shot Multibox Detector
- Output feature (final layer)
• With given output boxes from multi-scale features, sort them
using class confidence
• Pick top-200 boxes and make each box 7-dim vector
• [ batch_idx, class_confidence, label, box offset…]
• Output feature dim is 7x200
•

10. Model analysis
- Data argumentation is very important
- More feature map is better
• Lower feature map can capture fine-grained details of object
- More default box shape is better
• If you only 4 boxes, performance drop by 0.9%
• Using variety shape of default box makes predicting box easier
- Astrous VGG is better and faster

11. Result
- Accuracy is compare to state-of-the-art, and with
real-time

12. Reference
- Liu, Wei, et al. "SSD: Single Shot MultiBox Detector." arXiv preprint
arXiv:1512.02325 (2015).