Only ‘pip install yolov5’ away from you…

Simple as that.
  • Are you having a hard time installing latest YOLO object detector in Windows/Linux?
  • Are you getting errors during training/inference with your custom YOLOv5 models?
  • Are you looking for a real-time object tracker with only few lines of code?
  • Do you want to perform large-scale (drone surveillance/satellite imagery/wide-area surveillance) object detection in one click?

YOLOv5 Object Detector


Only 'pip install yolov5' away from you…

Simple as that.
  • Are you having a hard time installing latest YOLO object detector in Windows/Linux?
  • Are you getting errors during training/inference with your custom YOLOv5 models?
  • Are you looking for a real-time object tracker with only few lines of code?
  • Do you want to perform large-scale (drone surveillance/satellite imagery/wide-area surveillance) object detection in one click?

YOLOv5 Object Detector


Visualization of how SAHI inference works.

Github: https://github.com/obss/sahi

Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage.

Here you can see the inference result of the state of the art instance segmentation model Cascade Mask RCNN:


Bu yazı, https://medium.com/codable/face-anti-spoofing-starter-kit-f248ed195a3c Medium makalemin Türkçe çevirisidir.

Vietnamlı güvenlik şirketi Bkav tarafından, Face ID’yi aşması için tasarlanan maske. Kaynak: https://www.youtube.com/watch?v=rhiSBc061JU

Biyometrik tanımlama, en eski kişi doğrulama tekniklerinden biridir. Parolalar ve anahtarlar casusluk yoluyla elde edilebilir, çalınabilir, unutulabilir veya taklit edilebilir. Ancak kişinin kendisinin benzersiz özelliklerini taklit etmek ve kaybetmek çok daha zordur. Bu özellikler; parmak izleri, ses, retinanın damarlarının şekli ve daha fazlası olabilir.

Ancak tahmin ettiğiniz üzere biyometrik sistemleri de kandırmaya çalışanlar var. İşte bu makalenin konusu da: Saldırganlar başka bir kişiyi taklit ederek yüz tanıma sistemlerinden nasıl kaçınmaya çalışıyor ve bu nasıl tespit edilebilir!

Bu makaleyi okuduktan sonra neler hakkında bilgi sahibi olacaksınız:

  • Yüz tanıma (face recognition) ve yüz sahteciliği (face…


Vietnamese security company Bkav made headlines in mid-November after uploading a video featuring Face ID accessed by a mask.
Vietnamese security company Bkav made headlines in mid-November after uploading a video featuring Face ID accessed by a mask.
Vietnamese security company Bkav made headlines in mid-November after uploading a video featuring Face ID accessed by a mask. Source: https://www.youtube.com/watch?v=rhiSBc061JU

Biometric identification is one of the earliest techniques of person verification. Passwords and keys can be spied, stolen, forgotten, or faked. But the unique characteristics of the person himself is much more difficult to fake and lose. This can be fingerprints, voice, shape of the vessels of the retina, and more.

Of course, biometrics systems are also trying to be fooled. That’s the topic of this article: How attackers try to avoid facial recognition systems by impersonating another person, and how this can be detected!

What you will know after reading this article:

  • Relation between face recognition and face (anti)…


(List is under development, will include more than 10 datasets in the future.)

Illustration of 3D car detection, credits: Bc. Libor Novák

You are interested in autonomous driving and want to study 2D/3D object detection & tracking, lane/drivable area segmentation, semantic/instance segmentation, self localization and scene flow estimation for self-driving cars but don’t know where to start?

Then continue reading, and you will find all the necessary information on publicly available self-driving datasets!

1. Nuscenes Dataset from APTIV


In this post, the paper “ “Chitty-Chitty-Chat Bot”: Deep Learning for Conversational AI ” is summarized.

Link to paper: https://www.ijcai.org/proceedings/2018/0778.pdf

Yan, R., 2018, “Key-Value Retrieval Networks for Task-Oriented Dialogue,” In IJCAI, pages 5520–5526

This paper is actually a survey paper. We think that it might be a good idea to review such a paper for the last entry of our blog as a summary of previous papers we have reviewed so far. Although this paper highlights overview of methods as well as problem formulation and data collection, here we only mention approaches to bots. The writers divide methods into 3…


In this post, the paper “Key-Value Retrieval Networks for Task-Oriented Dialogue” is summarized.

Link to paper: http://www.aclweb.org/anthology/W17-5506

Mihail Eric, Lakshmi Krishnan, Francois Charette, Christopher D. Manning, 2017, “Key-Value Retrieval Networks for Task-Oriented Dialogue,” in Proceedings of the SIGDIAL 2017 Conference, pages 37–49

In this paper, researchers from Stanford NLP Group and Ford Research and Innovation Center seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. …


In this post, the paper “Lexicon-Based Methods for Sentiment Analysis” is summarized.

Link to paper: https://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00049

Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, Manfred Stede, 2011, “Lexicon-Based Methods for Sentiment Analysis,” in Computational Linguistics, Volume 37, Issue 2, p.267–307

In this article, Sentiment Analysis and Semantic Orientation Calculation is done with lexicon-based methods. Using a hand-crafted dictionary from a specific corpus and assigning semantic orientation scores to words to calculate semantic orientation values of sentence inputs, via analyzing the sentiments, is conducted in this research. Interestingly this research appeared to be successful than the others in the field, thanks…


In this post, the paper “Key-Value Retrieval Networks for Task-Oriented Dialogue” is summarized.

Link to paper: http://www.aclweb.org/anthology/W17-5506

Mihail Eric, Lakshmi Krishnan, Francois Charette, Christopher D. Manning, 2017, “Key-Value Retrieval Networks for Task-Oriented Dialogue,” in Proceedings of the SIGDIAL 2017 Conference, pages 37–49

In this paper, researchers from Stanford NLP Group and Ford Research and Innovation Center seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. …

Fatih Cagatay Akyon

Senior Machine Learning Engineer twitter.com/fcakyon

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