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Introduction to object detection

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This is part 1/4 of Brodmann17 A-Z Deep Learning based Object Detection meetup given by Assaf Mushinsky

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Introduction to Object Detection
Computer Vision Tasks Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf
Computer Vision Tasks Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf
Classification + Localization ● Classification: ○ Input: Image ○ Output: Class label ○ Loss: Cross entropy (Softmaxlog) ○ Evaluation metric: Accuracy ● Localization: ○ Input: Image ○ Output: Box in the image (x, y, w, h) ○ Loss: L2 Loss (Euclidean distance) ○ Evaluation metric: Intersection over Union ● Classification + Localization: ○ Input: Image ○ Output: Class label + box in the image ○ Loss: Sum of both losses
Classification + Localization: ImageNet Challenge ● Dataset ○ 1000 Classes. ○ Each image has 1 class with at least one bounding box. ○ ~800 Training images per class. ● Evaluation ○ Algorithm produces 5 (class + bounding box) guesses. ○ Example is correct if at least one of guess has correct class AND bounding box at least 50% intersection over union.
Intersection Over Union (IoU) ● Important measurement for object localization. ● Used in both training and evaluation. Intersection(A,B) Union(A,B) IoU(A,B) =
Classification + Localization: Model Classification Head: ● C Scores for C classes Localization Head: ● Class agnostic: (x,y,w,h) ● Class specific: (x,y,w,h) X C
Computer Vision Tasks Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf
Object Detection 2001-2007 ● Rapid Object Detection using a Boosted Cascade of Simple Features (2001) ○ Viola & Jones ● Histograms of Oriented Gradients for Human Detection (2005) ○ Dalal & Triggs ● Object Detection with Discriminatively Trained Part Based Models (2010) ○ Felzenszwalb, Girshick, Ramanan ● Fast Feature Pyramids for Object Detection (2014) ○ Dollar
Object Detection 2007-2012 Source: Ross Girshick’s CVPR 2017 Tutorial http://deeplearning.csail.mit.edu/instance_ross.pptx
Object Detection Today Source: Ross Girshick’s CVPR 2017 Tutorial http://deeplearning.csail.mit.edu/instance_ross.pptx
Object Detection: Datasets 2007 Pascal VOC ● 20 Classes ● 11K Training images ● 27K Training objects Was de-facto standard, currently used as quick benchmark to evaluate new detection algorithms. 2013 ImageNet ILSVRC ● 200 Classes ● 476K Training images ● 534K Training objects Essentially scaled up version of PASCAL VOC, similar object statistics. 2015 MS COCO ● 80 Classes ● 200K Training images ● 1.5M Training objects More categories and more object instances in every image. Only 10% of images contain a single object category, 60% in Pascal. More small objects than large objects.
Pascal Examples
COCO Examples
Object Detection ● Input: Image ● Output: For each object class c and each image i, an algorithm returns predicted detections: locations with confidence scores .
Object Detection: Evaluation ● True positive: correct class prediction AND IoU > 50%. ● False positive: wrong class or IoU < 50%. ● False negative: missed (not detected) object ● Only one detection can be matched to an object. ●
Object Detection: Evaluation ● Mean Average Precision (mAP) across all classes, based on Average Precision (AP) per class, based on Precision and Recall.
Precision And Recall For a Threshold
Precision-Recall Curve Source: Drawing by Prof. William H. Press, the University of Texas at Austin
Average Precision (AP) ● [In the vision community] AP is the estimated area under the PR curve  
Mean Average Precision (mAP) ● The winner of each object class is the team with the highest average precision ● The winner of the challenge is the team with the highest mean Average Precision (mAP) across all classes.
Object Detection: Evaluation ● Mean Average Precision (mAP) across all classes, based on Average Precision (AP) per class, based on Precision and Recall.
Object Detection: Evaluation ● Today new metrics are emerging ○ Averaging precision over all IoU thresholds: 0.5:0.05:0.95 ○ Averaging precision for different object sizes (small, medium, big) ○ Averaging recall as a metric to measure object proposal quality.
Looking for brilliant researchers cv@brodmann17.com

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Introduction to object detection

  • 2. Computer Vision Tasks Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf
  • 3. Computer Vision Tasks Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf
  • 4. Classification + Localization ● Classification: ○ Input: Image ○ Output: Class label ○ Loss: Cross entropy (Softmaxlog) ○ Evaluation metric: Accuracy ● Localization: ○ Input: Image ○ Output: Box in the image (x, y, w, h) ○ Loss: L2 Loss (Euclidean distance) ○ Evaluation metric: Intersection over Union ● Classification + Localization: ○ Input: Image ○ Output: Class label + box in the image ○ Loss: Sum of both losses
  • 5. Classification + Localization: ImageNet Challenge ● Dataset ○ 1000 Classes. ○ Each image has 1 class with at least one bounding box. ○ ~800 Training images per class. ● Evaluation ○ Algorithm produces 5 (class + bounding box) guesses. ○ Example is correct if at least one of guess has correct class AND bounding box at least 50% intersection over union.
  • 6. Intersection Over Union (IoU) ● Important measurement for object localization. ● Used in both training and evaluation. Intersection(A,B) Union(A,B) IoU(A,B) =
  • 7. Classification + Localization: Model Classification Head: ● C Scores for C classes Localization Head: ● Class agnostic: (x,y,w,h) ● Class specific: (x,y,w,h) X C
  • 8. Computer Vision Tasks Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf
  • 9. Object Detection 2001-2007 ● Rapid Object Detection using a Boosted Cascade of Simple Features (2001) ○ Viola & Jones ● Histograms of Oriented Gradients for Human Detection (2005) ○ Dalal & Triggs ● Object Detection with Discriminatively Trained Part Based Models (2010) ○ Felzenszwalb, Girshick, Ramanan ● Fast Feature Pyramids for Object Detection (2014) ○ Dollar
  • 10. Object Detection 2007-2012 Source: Ross Girshick’s CVPR 2017 Tutorial http://deeplearning.csail.mit.edu/instance_ross.pptx
  • 11. Object Detection Today Source: Ross Girshick’s CVPR 2017 Tutorial http://deeplearning.csail.mit.edu/instance_ross.pptx
  • 12. Object Detection: Datasets 2007 Pascal VOC ● 20 Classes ● 11K Training images ● 27K Training objects Was de-facto standard, currently used as quick benchmark to evaluate new detection algorithms. 2013 ImageNet ILSVRC ● 200 Classes ● 476K Training images ● 534K Training objects Essentially scaled up version of PASCAL VOC, similar object statistics. 2015 MS COCO ● 80 Classes ● 200K Training images ● 1.5M Training objects More categories and more object instances in every image. Only 10% of images contain a single object category, 60% in Pascal. More small objects than large objects.
  • 15. Object Detection ● Input: Image ● Output: For each object class c and each image i, an algorithm returns predicted detections: locations with confidence scores .
  • 16. Object Detection: Evaluation ● True positive: correct class prediction AND IoU > 50%. ● False positive: wrong class or IoU < 50%. ● False negative: missed (not detected) object ● Only one detection can be matched to an object. ●
  • 17. Object Detection: Evaluation ● Mean Average Precision (mAP) across all classes, based on Average Precision (AP) per class, based on Precision and Recall.
  • 18. Precision And Recall For a Threshold
  • 19. Precision-Recall Curve Source: Drawing by Prof. William H. Press, the University of Texas at Austin
  • 20. Average Precision (AP) ● [In the vision community] AP is the estimated area under the PR curve  
  • 21. Mean Average Precision (mAP) ● The winner of each object class is the team with the highest average precision ● The winner of the challenge is the team with the highest mean Average Precision (mAP) across all classes.
  • 22. Object Detection: Evaluation ● Mean Average Precision (mAP) across all classes, based on Average Precision (AP) per class, based on Precision and Recall.
  • 23. Object Detection: Evaluation ● Today new metrics are emerging ○ Averaging precision over all IoU thresholds: 0.5:0.05:0.95 ○ Averaging precision for different object sizes (small, medium, big) ○ Averaging recall as a metric to measure object proposal quality.
  • 24. Looking for brilliant researchers cv@brodmann17.com