My solution to HackerEarth's Identify the dance form challenge

Another post starts with you beautiful people!
Today an interesting deep learning challenge is finished in HackerEarth and I got 91.17026 mAP score in the leader board. One drawback I see in HackerEarth is due to small dataset many participants manually prepare the submission files and show 100% score in the leader board. Many aspiring data scientists see this and become nervous. Even with getting score 75+, they become demotivated and leave their experiments in between the challenge. Also the winning approach is not disclosed after the challenge. With this post I will try to motivate my all aspiring data scientists and I will share my solution so that in their next challenge they can easily get 85+ score or even 92+ score :)

Problem statement
An event management company organized an evening of Indian classical dance performances to celebrate the rich, eloquent, and elegant art of dance. After the event, the company plans to create a micro site to promote and raise awareness among people about these dance forms. However, identifying them from images is a difficult task.

You are appointed as a Machine Learning Engineer for this project. Your task is to build a deep learning model that can help the company classify these images into eight categories of Indian classical dance-

1. Manipuri
2. Bharatanatyam
3. Odissi
4. Kathakali
5. Kathak
6. Sattriya
7. Kuchipudi
8. Mohiniyattam

My Solution

Due to small size of the dataset, I decided to frame this challenge as object detection and recognition problem and used AlexeyAB/darknet/YOLOv4 to train and test the images. If you are new to this framework, please follow my below two posts to get started-

• Installing and building Darknet
• Training dance form dataset with YOLOv4

For the dataset preparation, I manually annotated the dataset in required YOLO format. The complete annotation process took 1.5 hours. The complete training of the model took 18 hours in my system. If you don't have GPU enabled system, don't worry! Follow this amazing notebook to install, build and train your custom dataset with YOLOv4 in colab-

YOLOv4 in Colab

Once the training is completed, weights files are generated inside the backup folder of the darknet root directory. The file name is yolo-obj_final.weights and it's size is around 250 mb. Now to make prediction on your all test images run the following command-

./darknet detector test data/obj.data cfg/yolo-obj.cfg backup/yolo-obj_final.weights -ext_output -dont_show -out data/dance_result.json < data/test.txt

The above command will generate a json file. This json file contains the detected object name, it's bounding box coordinates and confidence score. To prepare the final submission file I created a script json_csv_submission2.py. After submitting this submission file I got score 91.17026 in the leaderboard. I have uploaded my solutions files to the github and weight file in the kaggle since we cannot push file larger than 100 mb. You can fork or download the training files from below link-

my github repository And you can download the trained weights from this link.

In this post all required things are mentioned to solve an image related deep learning problem. Only one thing is expected from all of you is to get started. Until you practice yourself, you will not learn. I am quite sure that in your next image related challenge you will definitely going to use this object detection and recognition technique and spot a better rank. So push yourself, read my mentioned links, practice yourselves and in my next blog post I will share you another interesting learning. Till then Go chase your dreams, have an awesome day, make every second count and see you later in my next post.