You Only Look Once: Unified, Real-Time Object Detection

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YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.

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You only look once:
Unified, Real-time Object Detection
by Joseph Redmon, Santosh Divvala,
Ross Girshick, Ali Farhadi (CVPR 2016)

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from deepsystems.io
Pascal VOC2007 test sample results.

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Main Concept
* Object Detection
* Regression problem
* YOLO
* Only One Feedforward
* Global context
* Unified (Real-time detection)
* YOLO: 45 FPS
* Fast YOLO: 155 FPS
* General representation
* Robust on various background
* Other domain

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Previous Works: Repurpose classifier to perform detectio
Deformable Parts Models (DPM)
• Sliding window
R-CNN based methods
1) generate potential bounding boxes.
2) run classifiers on these proposed
boxes
3) post-processing (refinement,
elimination, rescore)

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Object detection as Regression Problem
YOLO: Single Regression Problem
Image → bounding box coordinate and class probability.
* Extremely Fast
* Global reasoning
* Generalizable representation

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Unified Detection
• All BBox, All classes
1) Image → S x S grids
2) Grid cell
→ B: BBoxes and Confidence score
x, y, w, h, confidence
→ C: class probabilities w.r.t #classes

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Unified Detection
• Predict one set of class
probabilities per grid cell,
regardless of the number of
boxes B.
• At test time,
individual box confidence
prediction

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Network Design
• Modified GoogLeNet
• 1x1 reduction layer (“Network in Network”)

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How it works?
from deepsystems.io

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from deepsystems.io
How it works?

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How it works?
from deepsystems.io
Total :
7*7*2 = 98 boxes

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Look at detection procedure
from deepsystems.io

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Limitation of YOLO
from deepsystems.io
• Group of small objects
• Unusual aspect ratios
• Coarse feature
• Localization error of bounding box

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Comparison to other Real-Time Systems
from deepsystems.io

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Combining Fast R-CNN and YOLO
from deepsystems.io

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VOC 2012 Leaderboard
from deepsystems.io

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Generalizability : Person Detection in Artwork
from deepsystems.io

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Key Points
from deepsystems.io
1.Fast: YOLO - 45 fps, YOLO-tiny - 155 fps.
2.End-to-end training.
3.Makes more localization errors but is less likely to
predict false positives on background
4.Performance is lower than the current state of the art.
5.Combined Fast R-CNN + YOLO model is one of the
highest performing detection
6.methods.
7.Learns very general representations of objects: it
outperforms other detection methods,
8.including DPM and R-CNN, when generalizing from
natural images to other domains

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Appendix : Loss Function (sum-squared error)
from deepsystems.io

K U L A
from deepsystems.io
Appendix : Loss Function (sum-squared error)

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from deepsystems.io
Appendix : Intersection over Union (IoU)
• IoU(pred, truth)=[0, 1]

K U L A
from deepsystems.io
Appendix : Sum-Squared Error (SSE)
sum of squared errors of prediction (SSE), is the sum of the squares of
residuals (deviations predicted from actual empirical values of data). It is a
measure of the discrepancy between the data and an estimation model. A
small RSS indicates a tight fit of the model to the data. It is used as an
optimality criterion in parameter selection and model selection.

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You Only Look Once: Unified, Real-Time Object Detection

1. K U L A You only look once: Unified, Real-time Object Detection by Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi (CVPR 2016)

2. K U L A from deepsystems.io Pascal VOC2007 test sample results.

3. K U L A Main Concept * Object Detection * Regression problem * YOLO * Only One Feedforward * Global context * Unified (Real-time detection) * YOLO: 45 FPS * Fast YOLO: 155 FPS * General representation * Robust on various background * Other domain

4. K U L A Previous Works: Repurpose classifier to perform detectio Deformable Parts Models (DPM) • Sliding window R-CNN based methods 1) generate potential bounding boxes. 2) run classifiers on these proposed boxes 3) post-processing (refinement, elimination, rescore)

5. K U L A Object detection as Regression Problem YOLO: Single Regression Problem Image → bounding box coordinate and class probability. * Extremely Fast * Global reasoning * Generalizable representation

6. K U L A Unified Detection • All BBox, All classes 1) Image → S x S grids 2) Grid cell → B: BBoxes and Confidence score x, y, w, h, confidence → C: class probabilities w.r.t #classes

7. K U L A Unified Detection • Predict one set of class probabilities per grid cell, regardless of the number of boxes B. • At test time, individual box confidence prediction

8. K U L A Network Design • Modified GoogLeNet • 1x1 reduction layer (“Network in Network”)

9. K U L A How it works? from deepsystems.io

10. K U L A from deepsystems.io How it works?

11. K U L A from deepsystems.io How it works?

12. K U L A from deepsystems.io How it works?

13. K U L A from deepsystems.io How it works?

14. K U L A from deepsystems.io How it works?

15. K U L A from deepsystems.io How it works?

16. K U L A from deepsystems.io How it works?

17. K U L A from deepsystems.io How it works?

18. K U L A from deepsystems.io How it works?

19. K U L A How it works? from deepsystems.io Total : 7*7*2 = 98 boxes

20. K U L A Look at detection procedure from deepsystems.io

21. K U L A Look at detection procedure from deepsystems.io

22. K U L A Look at detection procedure from deepsystems.io

23. K U L A Look at detection procedure from deepsystems.io

24. K U L A Look at detection procedure from deepsystems.io

25. K U L A Look at detection procedure from deepsystems.io

26. K U L A Look at detection procedure from deepsystems.io

27. K U L A Look at detection procedure from deepsystems.io

28. K U L A Look at detection procedure from deepsystems.io

29. K U L A Look at detection procedure from deepsystems.io

30. K U L A Look at detection procedure from deepsystems.io

31. K U L A Look at detection procedure from deepsystems.io

32. K U L A Look at detection procedure from deepsystems.io

33. K U L A Look at detection procedure from deepsystems.io

34. K U L A Look at detection procedure from deepsystems.io

35. K U L A Look at detection procedure from deepsystems.io

36. K U L A Look at detection procedure from deepsystems.io

37. K U L A Look at detection procedure from deepsystems.io

38. K U L A Look at detection procedure from deepsystems.io

39. K U L A Limitation of YOLO from deepsystems.io • Group of small objects • Unusual aspect ratios • Coarse feature • Localization error of bounding box

40. K U L A Comparison to other Real-Time Systems from deepsystems.io

41. K U L A VOC Error from deepsystems.io

42. K U L A Combining Fast R-CNN and YOLO from deepsystems.io

43. K U L A VOC 2012 Leaderboard from deepsystems.io

44. K U L A Generalizability : Person Detection in Artwork from deepsystems.io

45. K U L A Generalizability : Person Detection in Artwork from deepsystems.io

46. K U L A Key Points from deepsystems.io 1.Fast: YOLO - 45 fps, YOLO-tiny - 155 fps. 2.End-to-end training. 3.Makes more localization errors but is less likely to predict false positives on background 4.Performance is lower than the current state of the art. 5.Combined Fast R-CNN + YOLO model is one of the highest performing detection 6.methods. 7.Learns very general representations of objects: it outperforms other detection methods, 8.including DPM and R-CNN, when generalizing from natural images to other domains

47. K U L A Appendix : Loss Function (sum-squared error) from deepsystems.io

48. K U L A from deepsystems.io Appendix : Loss Function (sum-squared error)

49. K U L A from deepsystems.io Appendix : Loss Function (sum-squared error)

50. K U L A from deepsystems.io Appendix : Intersection over Union (IoU) • IoU(pred, truth)=[0, 1]

51. K U L A from deepsystems.io Appendix : Sum-Squared Error (SSE) sum of squared errors of prediction (SSE), is the sum of the squares of residuals (deviations predicted from actual empirical values of data). It is a measure of the discrepancy between the data and an estimation model. A small RSS indicates a tight fit of the model to the data. It is used as an optimality criterion in parameter selection and model selection.

You Only Look Once: Unified, Real-Time Object Detection

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