বাংলা.এআইতে “শিক্ষার্থী কাগলার জন্য শীর্ষ মার্কস | লিংশো কাকুর সাথে এক বিজয়ীর সাক্ষাৎকার | কাগল দলের লেখা | কাগল ব্লগ”

বাংলা.এআইতে “শিক্ষার্থী কাগলার জন্য শীর্ষ মার্কস | লিংশো কাকুর সাথে এক বিজয়ীর সাক্ষাৎকার | কাগল দলের লেখা | কাগল ব্লগ”

Introduction:

Join us in congratulating Linsho Kaku on his solo first-place win in our Bengali.AI Handwritten Grapheme Classification challenge! Linsho is a student at the Tokyo Institute of Technology and an intern at Future Inc. where he works on an OCR task. With his prior experience in OCR tasks and his knowledge of the paper shared in his lab, Linsho was able to create a winning solution without any preprocessing. His most important insight was to focus on creating a model that could recognize classes that were not given. Linsho used Pytorch as a deep learning framework and had a hardware setup with 4 Tesla V100s. He gained new skills through this competition and advises beginners in data science to think beyond common sense solutions. If he could run a Kaggle competition, he would propose a practical OCR task that evaluates handwriting detection and recognition. Linsho’s research interests include deep learning, image processing, and optical character recognition.

Full Article: বাংলা.এআইতে “শিক্ষার্থী কাগলার জন্য শীর্ষ মার্কস | লিংশো কাকুর সাথে এক বিজয়ীর সাক্ষাৎকার | কাগল দলের লেখা | কাগল ব্লগ”

Congratulations to Linsho Kaku, also known as deoxy, for his impressive solo first-place win in the Bengali.AI Handwritten Grapheme Classification challenge. Linsho is currently a student at the Rio Yokota Laboratory at the Tokyo Institute of Technology, where he focuses on high-performance computing using advanced architectures such as GPUs. Linsho is also an intern at Future Inc., where he is working on an OCR (Optical Character Recognition) task.

Prior Experience and Success Factors
Linsho’s experience as an intern working on OCR tasks played a significant role in his success. His familiarity with preprocessing data and creating models allowed him to excel in the competition. While he did not specialize in Few-Shot Learning, which was a key factor for other top teams’ scores, his knowledge of relevant research papers and discussions within his laboratory contributed to his triumph.

Approach and Research
Linsho’s approach was informed by previous research and discussions on Kaggle. He also consulted resources like Science Direct to understand and implement Few-Shot Learning techniques effectively.

Preprocessing
Linsho did not perform any preprocessing on the images, such as cropping or noise reduction. He found that these processes did not improve recognition accuracy and often resulted in a loss of information. Instead, he focused on developing a model capable of recognizing classes that were not provided, which was the primary task of the competition.

Insights into the Data
Linsho discovered that the classification of three types of components in the handwritten graphemes was just a hint, and the real challenge was to create a model that could recognize unknown classes. Abstracting the structures that can appear in a character was more likely to improve the accuracy of classification. To achieve this, he used a generative model based on CycleGAN to generate font image characters from handwritten characters. By considering the generated font image as a feature of the middle layer in a series of handwriting classification models, he was able to improve the accuracy of his approach.

Tools and Hardware
Linsho utilized Pytorch as a deep learning framework and Jupyter Notebook as an IDE for his work. He had access to servers equipped with 4 Tesla V100 GPUs.

Runtime of the Solution
The training of the CycleGAN model took approximately 2.5 days on Linsho’s hardware setup. The prediction time for his winning solution was around 40 minutes for each of the two ensemble models.

Key Takeaways and Advice
Linsho gained valuable skills through this competition that will help him tackle future challenges in data science. He advises aspiring data scientists not to rely solely on common sense or obvious approaches, as they may not always be the best strategy for winning.

Future Kaggle Competition Proposals
If given the opportunity to run a Kaggle competition, Linsho would propose a more practical OCR task that evaluates the end-to-end process of handwriting detection and recognition. He believes there is room for improvement in developing a general-purpose method of detection, and he hopes that such a competition would spur active discussion and development within the field.

In conclusion, Linsho Kaku is a Master’s student at the Tokyo Institute of Technology, supervised by Rio Yokota. His research interests include deep learning, image processing, and optical character recognition. His impressive win in the Bengali.AI Handwritten Grapheme Classification challenge showcases his expertise and dedication to advancing the field of data science.

Summary: বাংলা.এআইতে “শিক্ষার্থী কাগলার জন্য শীর্ষ মার্কস | লিংশো কাকুর সাথে এক বিজয়ীর সাক্ষাৎকার | কাগল দলের লেখা | কাগল ব্লগ”

Join us in congratulating Linsho Kaku (aka deoxy) on his first-place win in the Bengali.AI Handwritten Grapheme Classification challenge! Linsho, a student at the Tokyo Institute of Technology, used his experience in OCR tasks to preprocess data and create models. His approach involved generating font images from handwritten characters using a style transformation model called CycleGAN. This allowed him to classify unknown classes more accurately. Linsho used Pytorch and Jupyter Notebook for his solution and had a hardware setup with 4 Tesla V100 servers. His winning solution took 2.5 days for training and 40 minutes for prediction. Linsho gained new skills and advises aspiring data scientists to think outside the box. He also suggests a practical OCR problem for future Kaggle competitions.

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