Yolo is an end-to-end, real-time object detection system that uses a single convolutional neural network to predict bounding boxes and class probabilities directly from full images. It uses a deeper Darknet-53 backbone network and multi-scale predictions to achieve state-of-the-art accuracy while running faster than other algorithms. Yolo is trained on a merged ImageNet and COCO dataset and predicts bounding boxes using predefined anchor boxes and associated class probabilities at three different scales to localize and classify objects in images with just one pass through the network.

1. TECSAR
Transformative Computer Systems
and Architecture Research
YOLO
Wednesday, October 10, 2018
Shrey Mohan
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2. Points to be discussed
• Overview
• Architecture
• Training
• Predictions
• Performance
• Current work
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3. Overview
• Yolo is an end to end convolutional
network for object localization and
classification.
•It only looks at the image once and to
make predictions, hence the name
becomes You Only Look Once(YOLO).
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4. Overview
• Yolo is one of the most accurate detection
algorithms and it is the fastest at this point
in time.
• Hence, it can be effectively used
for real time computations.
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5. Architecture
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6. Architecture
• Yolo uses Darknet-53 which has
53 Convolution layers.
• This deeper backbone network
thus gives a better mAP.
• Furthering detections to 3
scales also improves the mAP.
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7. Training Yolo
• Various techniques were used :
1. Different resolutions were used, 320, 352,….,608.
2. Batch normalization was used after convolving.
3. Unified datasets were used for training (next slide).
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8. Training Yolo
• ImageNet and COCO datasets are
merged.
• This helps the model to detect
more specialized objects.
• The model may also detect
objects it has never seen before.
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9. Predictions
• The model predicts 3 matrices at different scales of the
following dimensions :
1. 13x13x(N*(80+5))
2. 26x26x(N*(80+5))
3. 52x52x(N*(80+5))
Where 80 are the class probabilities, 5 are the Bounding box
attributes and N are the number of anchors for COCO
dataset (next slide)
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10. Predictions
• We decide pre-defined boxes for
Yolo called anchor boxes which help
predict bounding boxes
• The dimensions are decided after
running k-means clustering on the
training set bounding boxes.
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11. Predictions
• 3 anchor boxes are defined for
Yolo (v3) per grid cell.
• For each anchor box, model
gives tx, ty, tw, th, po and 80 class
probabilities.
• These parameters help predict
locations of bounding boxes
(next slide).
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12. Predictions
• As seen in the equations tx, ty,
tw and th are the offsets to the
anchor boxes.
• This is done for each of the
anchor boxes defined (3 here).
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13. Predictions
• Every grid has 3 anchor boxes
associated.
• So how do we map anchor
boxes to ground truths?
• We calculate the IoU of each
anchor with each ground truth.
• Anchors with highest IoUs
represent that particular
ground truth.
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14. Predictions
• Lambda-coord is set to 5 and
lambda-noobj is set to 0.5.
• Square root of widths and heights
are taken to treat small and large
boxes equally.
•In this function, the last three terms
are changed to cross-entropy
functions instead.
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15. Performance
• There is always a trade-off
between accuracy and speed.
• While yolov2 ran on 45 fps on a
titan X, yolov3 runs about 30
fps.
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16. Results
• This video
shows
detections by
Yolo on the
MOT-16
benchmark.
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