Bader Dammak's starred repositories
machine_learning_complete
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
py-motmetrics
:bar_chart: Benchmark multiple object trackers (MOT) in Python
Transformers-Tutorials
This repository contains demos I made with the Transformers library by HuggingFace.
Comprehensive_reID_Baseline
This is a repository for organizing codes related to re-identification (especially state-of-the-art reid methods).
Person-reID-Evaluation
GOM:New Metric for Re-identification. 👉GOM explicitly balances the effect of performing retrieval and verification into a single unified metric.
awesome-mlops
A curated list of references for MLOps
PaddleSpeech
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
AI-Product-Index
A curated index to track AI-powered products.
From-0-to-Research-Scientist-resources-guide
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
albumentations
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
nn-zero-to-hero
Neural Networks: Zero to Hero
supervision
We write your reusable computer vision tools. 💜
ultralytics
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
Machine-Learning-Collection
A resource for learning about Machine learning & Deep Learning
tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
ML-Papers-Explained
Explanation to key concepts in ML
DMLS_cheat_sheets
Cheat sheets based on each chapter of Chip Huyen's "Designing Machine Learning Systems"
ColossalAI
Making large AI models cheaper, faster and more accessible
lm-evaluation-harness
A framework for few-shot evaluation of language models.