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# Micro1 Generalist Project — Looking for an Experienced Freelancer I’m looking for a freelancer who has experience with **AI generalist work, data annotation/data labeling, video analysis, and following detailed guidelines** to help me with a Micro1 Generalist project. ### Skills Required * AI/ML generalist knowledge * Video annotation and video analysis * Data labeling/annotation * Strong attention to detail * Ability to identify errors and inconsistencies in videos * Understanding and following detailed instructions and guidelines * Good written English and communication * Ability to work independently * Comfortable using online annotation/AI tools * Good time management * Ability to maintain consistent quality across multiple tasks ### What You Will Do The work may invo...

I have Arabic Multimodal Dataset (Text + Image) designed for Multimodal Aspect-Based Sentiment Analysis (MABSA). Each data point consists of an Arabic review text paired with one or more relevant images. I am seeking an expert LLM Specialist to build a comprehensive, state-of-the-art experimental framework to validate the dataset's learnability and establish standard benchmarks for the scientific community. The project involves task-specific fine-tuning using modern text and vision-language architectures The dataset must be evaluated across two distinct tasks following standard evaluation benchmarks (e.g., SemEval standards): Task 1: Aspect Category Detection (Identifying specific predefined aspect categories mentioned). Task 2: Aspect-Based Sentiment Polarity Classification (Classify...

I am looking for an experienced AI/ML developer to build and integrate an advanced AI video transformation feature into my existing portal. The system should create realistic face movements and body motions to simulate live yoga sessions. I will provide a source video, my visual references, and voice/audio samples. The aim is to replace the source face with mine while preserving natural expressions, head movement, body gestures during yoga poses, as well as lip synchronization with the provided voice. It should convincingly mimic a live session's feel. Applicants should have expertise in AI video generation, face replacement, body motion tracking, voice cloning, and backend integration techniques. I'm open to suggestions on using suitable AI models, APIs, or a self-hosted soluti...

Developed a healthcare AI application for lung disease classification using chest X-ray images, combined with an interactive medical chatbot and voice assistant. The classification component analyzes chest X-ray images and predicts the corresponding lung disease category. Alongside the classification model, I was responsible for developing the AI Chatbot and RAG system, allowing users to ask questions about lung diseases, symptoms, and medical information. I implemented Retrieval-Augmented Generation (RAG) to connect the chatbot with a medical knowledge base, enabling it to retrieve relevant information and generate more informative and context-aware responses. The system also included a Voice Assistant to provide a more natural voice-based interaction with the user. Key Features: Che...

Looking for an experienced developer to build an AI-based UAV detection and tracking system for a custom ArduPilot quadcopter using Raspberry Pi 5 and AI HAT+ 2. Scope includes real-time drone detection, object tracking, MAVLink integration, safe autonomous follow/stand-off navigation, telemetry logging, GCS interface, failsafes, SITL testing, and complete source-code handover.

I have a single video frame captured in daylight that shows the front of a vehicle. Unfortunately, the plate is extremely blurry and all normal sharpening tricks have failed. I need a forensic imaging specialist who can push well beyond routine filters—think de-blurring algorithms, AI-based super-resolution, frame averaging, or any other proven technique—to extract a legible plate number. Source material • One “very blurry” video frame pulled directly from the original recording. • Daytime lighting; no night-vision artifacts to worry about. What I would like back • A cleaned, high-confidence still image that clearly shows the license plate characters. • A brief note (a paragraph or two is fine) outlining the process and software you use...

Merhaba, Kitantik mağazam için basit bir ürün yükleme otomasyonu yaptırmak istiyorum. İstediğim sistem şu şekilde çalışacak: 1. Telefon veya bilgisayardan ürün/kaset fotoğrafını sisteme yükleyeceğim. 2. Fotoğraftaki renkli sticker algılanacak. 3. Sticker rengine göre daha önceden belirlediğim fiyat otomatik atanacak. Örneğin: - Kırmızı = 250 TL - Mavi = 200 TL - Yeşil = 150 TL - Sarı = 100 TL Bu fiyatları daha sonra panelden kendim değiştirebilmeliyim. 4. Yapay zekâ fotoğrafı analiz ederek mümkün olduğunca: - sanatçı - albüm/kaset adı - marka - ürün türü - fotoğrafta görülebilen diğer bilgiler bilgilerini çıkarsın. 5. Fotoğraf...

AI-Based Football Video Analytics – Research Project I’m looking for an AI/ML + Computer Vision developer/researcher to develop an experimental research prototype + research paper on: “AI-Based Low-Cost Video Analytics System for Assisting Grassroots Football Talent Identification.” Project Scope I already have football video footage. The goal is to build a research-level prototype, not a commercial application. Workflow: Football Video → Preprocessing → Player Detection (YOLO) → Player Tracking (ByteTrack/BoT-SORT) → Movement/Position Analysis → Feature Extraction → Player Performance Metrics → Analytical Player Profile Possible metrics include distance, estimated speed, movement intensity, trajectories, field/zone coverage,...

The goal is to build a small but neatly-structured video dataset that will feed a computer-vision model focused on everyday domestic activities. Each entry in the dataset must show a single household task—cleaning, cooking, or organizing—from two synchronized viewpoints: • First-person: a head-mounted (egocentric) camera capturing what the performer sees. • Third-person: a fixed, tripod-mounted camera giving a clear side or frontal view of the same action. For every recorded task, both clips should start and end together, be labelled with the task name, and be delivered in lossless or high-quality H.264 MP4 at 1080p or better. Natural indoor lighting is fine as long as the objects, hands, and completed result are clearly visible. Acceptance criteria 1. One pair...

I have a collection of 5,000 images that must be annotated with clear, tightly-fitted bounding boxes around the single object of interest in each frame. These labels will feed directly into a new machine-learning pipeline, so consistency and pixel-accurate placement are essential. You are free to work in any mainstream tool such as LabelImg, CVAT, Supervisely or an equivalent—that choice is yours as long as the final export is delivered in a widely-used format (YOLO, COCO JSON or Pascal VOC). I will supply the class list, detailed annotation guidelines and a small set of fully-labeled examples to make expectations crystal-clear before you begin. Deliverables • Complete set of 5,000 bounding-box annotation files in the agreed-upon format • A brief progress log (image c...

I’m building a computer-vision pipeline focused on reliable detection and recognition of human faces and full-body figures. The core of the job is a clean, well-documented Python implementation that can take still images or short video clips and return bounding boxes, class labels, and confidence scores for each detected person. I already have test media and the computing environment; what’s missing is the detection logic itself—ideally leveraging familiar libraries such as OpenCV, TensorFlow, PyTorch, or a proven YOLO/SSD variant. Accuracy on varied lighting and crowded scenes is more important to me than sheer speed, but the code should still run in real time on a modern GPU. Deliverables • Python source code with clear inline comments • Pre-trained weig...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

Urgent: Smartphone Video Data Collection Project (Multiple Slots Open) PROJECT OVERVIEW: We are urgently looking for remote participants to complete a quick, one-time face motion video collection project to train computer vision models. Multiple slots are open immediately. WHO CAN PARTICIPATE: • Male Candidates: Aged 36–50 • Female Candidates: Any age (18+) • Must have a valid smartphone (with front and back camera capability). • Must submit a basic identity verification document (School ID, Aadhaar, PAN, or DL) where private ID numbers are crossed out/hidden, but Name, DOB, Photo, and Country are clearly visible. PROJECT TASKS & GUIDELINES: This project includes different video tasks. Complete guidelines and a step-by-step training video will be shared vi...

I’m building out a series of AI-driven features and need an engineer who can step in wherever the development cycle demands— from early-stage prototyping to production deployment. The exact focus is still open, so you’ll help me evaluate whether classic machine learning, natural language processing, or computer vision best fits each feature we prioritise. Here’s how I see the collaboration: • Work with me to clarify the first use-case, audit the data I already have, and outline any additional collection or labelling steps. • Design a model architecture suited to that problem, train and validate it, then document performance clearly enough for non-technical stakeholders. • Package the solution into clean, modular code (Python preferred) with stra...

Project Description: Developed an end-to-end AI-powered medical intelligence platform for analyzing chest X-ray images and assisting with disease prediction. The system uses Deep Learning with DenseNet121 to classify X-ray images into five categories: Normal, Pneumonia, Atelectasis, Cardiomegaly, and Effusion. Integrated Grad-CAM Explainable AI (XAI) to generate visual heatmaps highlighting regions of the X-ray that contributed to the model's prediction. Built REST APIs using FastAPI for image upload, prediction, model information, and health monitoring. The platform also supports prediction history and is designed for integration with AI-assisted medical report generation using LLMs. Key Features 1. Medical Image Analysis – Processes chest X-ray images using deep learning. 2. ...

I’m looking for a Python-based workflow that takes my equirectangular photo collection and, for any two images I select, confirms whether they were shot from the same angle. Beyond the yes/no decision, the script must also: - check whether they are connected • calculate the scale ratio between the pair, - the angle yaw and pitch they connected • assign a reliability/confidence score to its assessment. sample dataset : i run the progam using cli, json output is fine All results should be written to a concise text report that I can easily parse or forward—feel free to suggest the most convenient plain-text structure. You’re free to use OpenCV, scikit-image, NumPy, or any other well-supported libraries so long as installation remains straightforward (p...

Remote Sensing + AI/ML Model for Identification of 10 Major NTFP Species in Jharkhand, India We are looking for an experienced "Remote Sensing / Computer Vision / Geospatial AI developer" to develop or fine-tune an AI/ML model capable of identifying and mapping "10 major Non-Timber Forest Product (NTFP) tree species in Jharkhand, India" using remote sensing imagery. Target Species 1. Sal 2. Mahua 3. Kusum 4. Tamarind 5. Kendu 6. Palash 7. Chironji 8. Amla 9. Harra 10. Bahera Objective The objective is to develop a reliable workflow that can identify these species from remote sensing data and ultimately generate a **species-wise tree inventory/map**, including tree locations and counts. Scope of Work We are open to either: Developing a new model from scratch or F...