Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
I mede this slide for the beginners of object detection.
Anchor box was really hard to understand for me, so I wrote about it as easy to understand as I can.
Let's overwhelmingly prosper!!
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
I mede this slide for the beginners of object detection.
Anchor box was really hard to understand for me, so I wrote about it as easy to understand as I can.
Let's overwhelmingly prosper!!
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Slides by Albert Jimenez about the following paper:
Gordo, Albert, Jon Almazan, Jerome Revaud, and Diane Larlus. "Deep Image Retrieval: Learning global representations for image search." arXiv preprint arXiv:1604.01325 (2016).
We propose a novel approach for instance-level image retrieval. It produces a global and compact fixed-length representation for each image by aggregating many region-wise descriptors. In contrast to previous works employing pre-trained deep networks as a black box to produce features, our method leverages a deep architecture trained for the specific task of image retrieval. Our contribution is twofold: (i) we introduce a ranking framework to learn convolution and projection weights that are used to build the region features; and (ii) we employ a region proposal network to learn which regions should be pooled to form the final global descriptor. We show that using clean training data is key to the success of our approach. To that aim, we leverage a large scale but noisy landmark dataset and develop an automatic cleaning approach. The proposed architecture produces a global image representation in a single forward pass. Our approach significantly outperforms previous approaches based on global descriptors on standard datasets. It even surpasses most prior works based on costly local descriptor indexing and spatial verification. We intend to release our pre-trained model.
Slides by Miriam Bellver from the Computer Vision Reading Group at the Universitat Politecnica de Catalunya about the paper:
Lu, Yongxi, Tara Javidi, and Svetlana Lazebnik. "Adaptive Object Detection Using Adjacency and Zoom Prediction." CVPR 2016
Abstract:
State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/understanding-selecting-and-optimizing-object-detectors-for-edge-applications-a-presentation-from-walmart-global-tech/
Md Nasir Uddin Laskar, Staff Machine Learning Engineer at Walmart Global Tech, presents the “Understanding, Selecting and Optimizing Object Detectors for Edge Applications” tutorial at the May 2023 Embedded Vision Summit.
Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge.
In this presentation, Laskar explores the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. He delves into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examines their strengths and weaknesses. And he provides guidance on how to evaluate and select an object detector for an edge application.
Recent Progress on Object Detection_20170331Jihong Kang
This slide provides a brief summary of recent progress on object detection using deep learning.
The concept of selected previous works(R-CNN series/YOLO/SSD) and 6 recent papers (uploaded to the Arxiv between Dec/2016 and Mar/2017) are introduced in this slide.
Most papers are focusing on improving the performance of small object detection.
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Panoptic Segmentation was introduces at ECCV 2018 by FAIR (Facebook AI Research). It's gaining the popularity and there were a few papers presented at one of the biggest computer vision conference, CVPR 2019. This slide contains the descriptions of panoptic segmentation networks presented at CVPR 2019, as well as the description of Panoptic Segmentation.
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
This was an intensive meetup at Samsung Next IL covering most interesting papers that were presented in CVPR 2017 last month. It is a good opportunity to have an overview of recent advancements in the field of Deep Learning with applications to Computer-Vision.
The following topics are covered:
• Object detection
• Pose estimation
• Efficient networks
This is an intensive meetup at Samsung Next IL covering most interesting papers that were presented in CVPR 2017 last month. It is a good opportunity to have an overview of recent advancements in the field of Deep Learning with applications to Computer-Vision.
The following topics are covered:
• Object detection
• Pose estimation
• Efficient networks
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
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Object Detection Using R-CNN Deep Learning Framework
1. Object Detection
Using R-CNN Deep Learning
Framework
Nader Karimi Bavandpour (nader.karimi.b@gmail.com)
Summer School of Intelligent Learning
IPM, 2019
2. Table of Content
● Machine Learning Key Point: Inductive Bias
● From Classification to Instance Segmentation
● Region Proposal
● R-CNN Framework
2
4. Definition of Inductive Bias
The kind of necessary assumptions about the nature of the target function are subsumed in the phrase
inductive bias.
- Wikipedia
Every machine learning algorithm with any ability to generalize beyond the training data that it sees has
some type of inductive bias.
- StackOverflow
4
5. Examples of Inductive Bias
● Maximum Margin: Maximize the width of the boundary between two classes
● Nearest Neighbors: Most of the cases in a small neighborhood in feature space belong to the same
class
● Minimum Cross-Validation Error: Select the hypothesis with the lowest cross-validation error
5
○ Although cross-validation may seem to be free of bias,
the "no free lunch" theorems show that cross-validation must be biased.
● Locality of Receptive Field: Use convolutional layers instead of fc layers
11. Intersection Over Union (IoU)
Important measurement for object localization
Used in both training and evaluation
11
12. Datasets: ImageNet Challenge
● 1000 Classes
● Each image has 1 class with at least one bounding box
● About 800 Training images per class
● Algorithm produces 5 (class + bounding box) guesses
● Correct if at least one of guess has correct class and bounding box
at least 50% intersection over union.
12
15. Selective Search for Region Proposal
● A region proposal algorithm used in object detection
● Designed to be fast with a very high recall
● Based on computing hierarchical grouping of similar regions based on
color, texture, size and shape compatibility
15
18. Selective Search for Region Proposal
● Combines the similar regions to form a larger region
○ based on color similarity, texture similarity, size
similarity, and shape compatibility
● Finally, these regions produce the Regions of
Interest (RoI)
18
27. Problems with R-CNN
● Extracting 2,000 regions for each image based on selective search
● Extracting features using CNN for every image region. Suppose we have N images, then the number of
CNN features will be N*2,000
● The entire process of object detection using R-CNN has three models:
○ CNN for feature extraction
○ Linear SVM classifier for identifying objects
○ Regression model for tightening the bounding boxes
27
28. R-CNN Family
● R-CNN: Selective search → Cropped Image → CNN
● Fast R-CNN: Selective search → Crop feature map of CNN
● Faster R-CNN: CNN → Region-Proposal Network → Crop feature map of CNN
● Mask-CNN: Mask-CNN: Adds Object Boundary Prediction to R-CNN
28
29. Fast RCNN
● Selective search as a proposal method
to find the Regions of Interest is slow
● Takes around 2 seconds per image to
detect objects, which is much better
compared to RCNN
29
30. R-CNN Family
● R-CNN: Selective search → Cropped Image → CNN
● Fast R-CNN: Selective search → Crop feature map of CNN
● Faster R-CNN: CNN → Region-Proposal Network → Crop feature map of CNN
● Mask-CNN: Mask-CNN: Adds Object Boundary Prediction to R-CNN
30
31. Faster RCNN
● Region Proposal Network (RPN) for region proposal
○ Input: Image of any size
○ Output: A set of rectangular object proposals and objectness
scores
○ Related to attention mechanisms
31
32. Faster RCNN
● Feature maps from CNN are passed to the
Region Proposal Network (RPN)
● k Anchor boxes of different shapes are
generated using a sliding window in the RPN
● Anchor boxes are fixed sized boundary boxes
that are placed throughout the image and
have different shapes and size
32
33. Faster RCNN
● For each anchor, RPN predicts two things:
○ The first is the probability that an anchor is an object (it does not consider which
class the object belongs to)
○ Second is the bounding box regressor for adjusting the anchors to better fit the
object
33
34. R-CNN Family
● R-CNN: Selective search → Cropped Image → CNN
● Fast R-CNN: Selective search → Crop feature map of CNN
● Faster R-CNN: CNN → Region-Proposal Network → Crop feature map of CNN
● Mask-CNN: Mask-CNN: Adds Object Boundary Prediction to R-CNN
34
35. Mask R-CNN
● Extends Faster R-CNN by adding a
branch for predicting an object mask in
parallel with the existing branch for
bounding box recognition
35
36. Mask R-CNN
● Defines a multi-task loss on each sampled RoI
as:
L = L_cls + L_box + L_mask
36