Welcoem to posts!!
in the future - u will be able to do some more stuff here,,,!! like pat catgirl- i mean um yeah... for now u can only see others's posts :c
With advancements in Generative AI, interactions with voice assistants are now real-time and feel like talking to a real person.
In our latest article, we explore the Speech-to-Speech pipeline from HuggingFace, covering:
- How speech-to-text transforms spoken language into accurate transcriptions with Whisper.
- The role of LLMs in understanding context and generating meaningful, human like responses
- How text-to-speech models like parler-tts converts these responses back into lifelike audio.
learnopencv.com/speech-to-speech/
#llms #speech2speech #GenAI #huggingface #whisper
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3D U-Net, an efficient paradigm in medical segmentation, excels at analyzing volumetric data, allowing it to capture a holistic view of brain scans.
The Brain Tumor Segmentation (BraTS), is an annual challenge that aims to use advanced state-of-the-art deep learning models and techniques to segment lesions in brain regions.
In this guide, we will explore how to train a 3D U-Net model using the BraTS2023-GLI dataset.
learnopencv.com/3d-u-net-brats/
30 - 0
Unveiling DETR: The Future of Object Detection
Curious about cutting-edge advancements in Computer Vision? Explore DETR (Detection Transformer) and learn how it revolutionizes object detection with transformer architecture! In this comprehensive overview, you'll dive into its working principles, real-world applications, and inference performance. Perfect for anyone looking to stay ahead in the AI and CV space!
Check out the full article here: learnopencv.com/detr-overview-and-inference/
#ComputerVision #AI #DETR #ObjectDetection #MachineLearning #Transformers #DeepLearning
34 - 0
Are you curious about self-supervised learning but not sure where to start? This article makes it easy to understand the core concepts of Self-Supervised Learning and introduces you to Facebook AI's DINO model.
It also shows how to apply DINO to a real-world challenge: road segmentation of Indian roads using the IDD dataset. Follow the step-by-step guide to preprocess data, build and fine-tune DINO on downstream segmentation task.
learnopencv.com/fine-tune-dino-self-supervised-lea…
21 - 0
Sapiens, a powerful new model family from Meta Reality Labs, has been introduced for human-centric vision tasks such as 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction.
Human Vision Models like Sapiens are game changers in Metaverse, facilitating the creation of life-like human avatars.
We will explore the pretraining, task specific fine-tuning details from the Sapiens paper and perform inference with the Sapiens-1B model across multiple tasks.
Individuals working at the intersection of Computer Vision and Mixed Reality will find this read very interesting
learnopencv.com/sapiens-human-vision-models/
#sapiens #humanvision #humanvisionmodel #computervision
37 - 1
ColPali, is a novel approach for Efficient Document Retrieval using Vision Language Models developed by the team at Illuin Tech. It outperforms standard retrievers by a huge margin with less latency and complexity.
We will explore and test this through a much demanding industrial use case by building a Multimodal RAG application with Colpali and Gemini on finance reports.
learnopencv.com/multimodal-rag-with-colpali/
Individuals and companies seeking to enhance their document analysis capabilities with RAG will find this read more useful.
#ColPali #DocumentRetrieval #VisionLanguageModels #IlluinTech #MultimodalRAG #AIinFinance #Gemini #DocumentAnalysis #RAGModel #FinanceTech
24 - 1
In this chapter of our robotics blog series, we’ll embark on the exciting challenge of building an Autonomous Path-Following Vehicle using ROS2 and CARLA in Python!
With a special focus on the control aspect—one of the four key pillars of robotics: sensing, perception, planning, and control—we’ll walk you through the process of achieving a smooth waypoint following using a PID controller.
learnopencv.com/pid-controller-ros-2-carla/
Get ready to explore the innovative techniques driving the control systems of autonomous vehicles!
#robotics #autonomousvehicles #pidcontroller #ros2
25 - 0
In this article, we address the complex problem of recognizing handwritten text using OCR. We will train the TrOCR model for handwritten note recognition on the Goodnotes dataset.
learnopencv.com/handwritten-text-recognition-using…
The dataset, which has been curated from several users, is versatile and complex. Pretrained OCR models fail to recognize the text in these documents, but fine-tuning the model with proper hyperparameter tuning makes it extremely performant on the dataset.
#OCR #HandwrittenTextRecognition #TrOCR #AI #MachineLearning #DeepLearning #Dataset #DataScience #AIResearch
34 - 0
Learn the essentials to build a CLIP-like model from scratch for a fashion apparel search app with image retrieval.
CLIP was trained on a massive dataset of image-text pairs, which allows it to excel as a zero-shot classification model. It can take a given text prompt or labels and efficiently retrieve matching images from a database, identifying those that share similar features with the query.
learnopencv.com/clip-model/
The article primarily dicusses:
How to implement the Vision and Text Encoder of CLIP from Scratch using PyTorch
Techniques involved in training a CLIP on a Fashion Images Dataset
Finally, how to build an apparel search app with Gradio
#CLIPModel, #ImageRetrieval, #MachineLearning, #PyTorch, #DeepLearning, #ImageSearch, #AI, #ComputerVision, #FashionTech, #ModelTraining
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LiDAR Odometry and Mapping are among the most reliable methods for SLAM, yet few resources cover the fundamentals. In this article, we explore two of the most influential papers: LOAM and LeGO-LOAM.
learnopencv.com/lidar-slam-with-ros2/
We break down the mathematics behind LiDAR positioning and mapping and dive into the C++ code to understand their implementation. We also provide a step-by-step guide to running LeGO-LOAM in ROS 2.
#lidar #slam #loam #ros2 #robotics
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Welcome to LearnOpenCV, a comprehensive YouTube channel dedicated to Computer Vision, Machine Learning, and Artificial Intelligence. Our mission is to provide high-quality educational content for everyone to succeed in these rapidly growing fields.
Our channel covers a wide range of topics, including deep learning, image processing, object detection, and face recognition, using state-of-the-art tools like OpenCV, PyTorch, and TensorFlow.
In addition to computer vision tutorials, we also offer valuable courses & career advice to help you achieve your professional goals
We encourage our viewers to engage with us by commenting, asking questions, and sharing their ideas. We want to create a collaborative learning environment where everyone can contribute and benefit.
Our channel is perfect for students, professionals, & hobbyists who are passionate about computer vision, ML & AI. Join us and start your journey towards becoming an expert in these exciting fields.