abhisheks008 / ML-Crate

ML-Crate stands as the ultimate hub for a multitude of exciting ML projects, serving as the go-to resource haven for passionate and dedicated ML enthusiasts!πŸŒŸπŸ’« Devfolio URL, https://devfolio.co/projects/mlcrate-98f9
https://quine.sh/repo/abhisheks008-ML-Crate-409463050
MIT License
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Human Dataset Visualization #722

Open SOMNATH0904 opened 1 month ago

SOMNATH0904 commented 1 month ago

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Human Dataset Visualization

:red_circle: Aim : Here I will convert the human3.6M dataset from txt file to image from for 10 time steps to predict the 11th step. Similarly I will convert the human3.6M dataset from txt file to skeleton gif data.

:red_circle: Dataset : N/A

:red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model. Linear Regression, Ada Boost Algorithm, Decision Tree, Random Forest

πŸ”΄ Reference Project Folder: NA


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:red_circle::yellow_circle: Points to Note :


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Happy Contributing πŸš€

All the best. Enjoy your open source journey ahead. 😎

github-actions[bot] commented 1 month ago

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

abhisheks008 commented 1 month ago

Please share the dataset for this problem statement.

SOMNATH0904 commented 1 month ago

Taken the dataset from this website -> http://vision.imar.ro/human3.6m/description.php

abhisheks008 commented 1 month ago

You are planning to predict the steps of the humans using your models right? If that is the problem statement, then what are the models you are planning to implement here? Mention at least 4-5 models for this.

SOMNATH0904 commented 1 month ago

Yes, the plan is to predict human steps using models in this project. I am planning to implement several models, including Convolutional Neural Networks (CNNs) for feature extraction, Long Short-Term Memory (LSTM) networks for handling sequential data, and 3D Pose Estimation models like the Human Pose Estimation Network (HPE) for predicting 3D poses from 2D images. Additionally, Generative Adversarial Networks (GANs) will be used to generate realistic human motion sequences, and Temporal Convolutional Networks (TCNs) will be considered for capturing long-range dependencies in sequential data.

abhisheks008 commented 1 month ago

One issue at a time @SOMNATH0904

SOMNATH0904 commented 1 month ago

@abhisheks008 Can you now please assign this to me. I have the project. I want to contribute, as today is the last date.

abhisheks008 commented 1 month ago

Assigned @SOMNATH0904