mouhanedg56 Goto Github PK
Name: Mouhaned
Type: User
Company: Chatdesk
Bio: ML engineer @ Chatdesk
Location: Lyon
Blog: https://www.linkedin.com/in/mouhaned-chebaane-1b781b127/
Name: Mouhaned
Type: User
Company: Chatdesk
Bio: ML engineer @ Chatdesk
Location: Lyon
Blog: https://www.linkedin.com/in/mouhaned-chebaane-1b781b127/
software tools for problem solving
A Knowledge Base for the FB Group Artificial Intelligence and Deep Learning (AIDL)
Multi-task modelling extensions for huggingface transformers
fast.ai Courses
Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Keras code and weights files for popular deep learning models.
Deep Residual Learning for Image Recognition
NLP French language model implementing ULMFiT
Detecting Road Features: identifying lane and vehicles boundaries in a video.
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
Train a neural network to detect and classify emotion from a photograph of a face.
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
A library for easily evaluating machine learning models and datasets.
Mask RCNN in TensorFlow
Make huge neural nets fit in memory
Multi-task modelling extensions for huggingface transformers
Image augmentation for machine learning experiments.
Keras package for region-based convolutional neural networks
Harness LLMs with Multi-Agent Programming
Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay
The project “Library Management System” was developed in java, which mainly focuses on basic operations in a library like adding new members, new books, and updating new information, searching books and members and facility to borrow and return books, with all sort of validation control.
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
codes for multi keywords speech recognition
Solve Pacman game using Reinforcement Learning
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.