Name: Benyamin Jafari
Type: User
Company: FlayTask (Formerly Yoello)
Bio: M.Sc. in Mechatronic Engineering (HRI) | Python Software Engineer β Backend | AI | LLM | Machine learning and robotics enthusiast.
Location: Istanbul, Turkey
Blog: https://stackoverflow.com/users/3702377
Benyamin Jafari's Projects
Me in a Nutshell
Several Gazebo models in order to person detection using 3D LiDAR, Pioneer P3DX and maze map for rescue robot exploration which is compatible with ROS.
Human detection package through two LaserScans 2D points
Leg detection package through LaserScan 2D points.
A package to detect human torso in outdoor and indoor environments.
A chat based 3D model creator and editor backend powered by GPT on top of FastAPI
visual servo simulator
A Django project to rate a content model through REST APIs
Natural language browser automation
Implemented a ROS package of bug 0, 1, and 2 navigation algorithm in Python and Gazebo sim.
A devkit for the Canadian Adverse Driving Conditions (CADC) dataset.
Python colored print plus storing it into a file.
System Connection Breaker API
Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis".
Deep Learning Specialization by Andrew Ng on Coursera.
State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.
Demo CAS server used by django-cas-ng/example
Goal of project was to detect vehicles and pedestrians using Lidar points in real time. This uses ROS nodes.
Django CAS 1.0/2.0/3.0 client authentication library, support Django 2.0, 2.1, 2.2, 3.0 and Python 3.5+
A Django Central Authentication Service server implementing the CAS Protocol 3.0 Specification
https://pypi.python.org/pypi/django-dynamic-link/0.5.6 fixed
Django application and library for importing and exporting data with admin integration.
Simple CRUD REST API with Python Django Rest Framework
DEPRECATED, this project is no longer maintained, see README for more information.
Data provided by Microsoft
The source code for the TΓΌbingen-Oslo team in SemEval-2018 Multilingual Emoji Prediction shared task.
Text to emoji mapping
Deep-learning system presented in "EmoSence at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual Conversations" at SemEval-2019.
Sentiment Analysis using Emoji ranking and practical POS scoring approach