Opencv OR tensorflowproiecte
Am nevoie de o aplicatie in tensorflow pentru comanda vocala, aplicatia trebuie sa fie in romana.
...compare each face against a gallery to decide whether it is an identical match. • Serve three environments without extra rewrites: security-grade CCTV feeds, social-media style mobile uploads, and large photo-management archives. • Deliver low latency on a single modern GPU while still running acceptably on CPU-only hardware for lightweight deployments. I’m comfortable with either PyTorch or TensorFlow/Keras; use the framework you know best. A pre-trained backbone such as ResNet, MobileNet, or Vision Transformer is fine as long as you include the full training pipeline so I can continue to improve the model with fresh data. Deliverables 1. Source code with clear, commented modules for detection, embedding generation, and similarity matching. 2. Pre-tra...
...combine traditional threat-intel techniques with machine-learning pipelines so the system continuously adapts as new data arrives. Here’s what success looks like to me: • A modular data-collection layer that can stream pcap, NetFlow, or similar log formats into a preprocessing engine. • Feature-engineering and model-training code written in Python (feel free to leverage Pandas, scikit-learn, TensorFlow, PyTorch—whatever best suits the task). • A detection component that scores incoming traffic and raises alerts via a simple REST API or CLI output. • Clear documentation covering setup, retraining, and how new data sources—such as endpoint events or social-media threat chatter—could be plugged in later. Because this is time-...
...stability, thermal management, and seamless connectivity transitions. Technical Specifications: 1. Camera Pipeline & Intelligent Routing: Implement CameraX to ensure maximum compatibility across various Android OEMs (Samsung, Pixel, Xiaomi, etc.). Develop a Routing Engine to ingest the camera stream and dispatch frames to two parallel modules: Offline Module: Local processing using ML Kit or TensorFlow Lite for immediate tasks (e.g., fast OCR, object proximity). Cloud Module: Optimized frame streaming (compressed JPEG/WebP) to the Google Gemini Flash API (target: 1-2 frames per second). 2. Smart Connectivity Fallback: Implement a robust network listener using ConnectivityManager / NetworkCapabilities. Automatic Failover: The system must detect low-latency or lost sign...
...every relevant frame straight into a MySQL database. Each stored image must be linked to the corresponding frame number and any detection metadata so I can later query, filter, and analyse the results. Once the data is stored, I want a lightweight viewer that steps through the saved frames in order, overlaying the detection boxes so I can visually confirm accuracy. OpenCV for frame extraction and display is acceptable; YOLO, TensorFlow, or another modern model is fine so long as the code is clean, well-commented, and easy for me to retrain with additional classes. To keep the hand-off smooth, please include: • A self-contained Python 3 script (or module set) that performs detection, inserts frames into MySQL, and plays them back. • The SQL schema and sample d...
...rewrite. On the modelling side, I want a workflow that lets me start a new model from scratch, feed in data, train, save, and then reuse that model inside the video generator. Ideally this is handled behind a clean dashboard rather than command-line steps. Core expectations • Intuitive UI/UX for both the video creator and the model-training console • Scalable backend (Python, PyTorch/TensorFlow, or comparable) with GPU support • Real-time preview for voice and style selections • Secure user accounts and storage for datasets, models, and rendered videos • Source code, build instructions, and a short hand-off call on completion If you have a reusable codebase or experience with generative media tools such as Stable Diffusion, FFmpeg, or J...
Szukam doświadczonego developera, który stworzy dla mnie bot...środki rzeczywiste. Po stronie technicznej liczę na: • Model uczenia maszynowego lub kombinację algorytmów statystycznych reagujących w sekundowym interwale. • Integrację z order book, jeśli pozwala na to przepustowość API. • Dashboard lub prosty panel www/CLI do podglądu metryk, stanu konta i ręcznej interwencji. W zgłoszeniu napisz, proszę: 1. Jakiej dokładnie architektury i bibliotek (TensorFlow, PyTorch, CCXT, itp.) zamierzasz użyć. 2. Szacowany czas realizacji poszczególnych etapów. 3. Koszt wykonania oraz model późniejszego utrzymania i aktualizacji. Zależy mi na długofalowej współpracy i stabilnej rentowności, przy pełnej świadomości ryzyka ...
...researcher who can build a production-ready model that listens to a baby’s cry, watches the paired video, and decides—reliably—whether the cause is hunger, discomfort, or simple attention seeking. Audio and video must be fused inside one architecture; running them in parallel but independently will not satisfy our accuracy goals. You may use the deep-learning stack you trust most (PyTorch, TensorFlow, Keras, OpenCV, torchaudio, etc.) provided the final network can run in real time on an edge device and be exported to ONNX or TFLite. I will share product constraints and a small proprietary data set; you will expand it through public sources or augmentation, perform rigorous cross-validation, and refine the model until we consistently exceed 90 % precision...
...meters, then reports everything wirelessly to the Pi for processing. Here’s what I need from you: • Python (Raspberry Pi) and MicroPython/C++ (ESP32) code that ingests the raw sensor streams, pushes them through an on-device model, and decides—within seconds—whether to start or stop the main pump and which solenoid valves to open. • An ML pipeline: training notebooks, a lightweight model (TensorFlow Lite or similar) and the inference wrapper that runs locally. The model must act on current soil-moisture readings and short-term weather data, while also generating three forward-looking insights: predicted soil moisture over the next 6–24 h, likely weather changes in that window, and the water volume the system will probably consume. • Cont...
...dataset available. But definately need a NEW DNN training Dataset. simple UI . Total Project is 250 USD STAGE 1 - 100 USD STAGE 2 100 USD Deplyment, Testing and Document 50 USD STAGE 1 handles intelligence + cloud STAGE 2 handles device + communication + app STAGE- I Backend Engineer (Cloud + Data + API) Deep Neural Networks for classification (imbalanced datasets, SMOTE preferred) Python (TensorFlow/PyTorch) and model deployment via REST API Handling weather/time-series datasets Cloud hosting (Azure/AWS) and database management SMS gateway integration and push notification backend Secure API development and logging system Deliverable: Deployed ML model with working API endpoint and backend system. STAGE 2 — Embedded & Mobile Systems Engineer (LoRa + App + Hardware...
I have an ongoing structural-optimization study that will be powered by a large language model...Deliverables • Python scripts and MATLAB functions that load data, train the LLM, and call the structural optimization routine. • Clear documentation (inline comments and a short README) so I can rerun the experiments on my workstation. • A brief report summarizing training results and the improvement achieved in the optimized designs. All work must execute on standard Python 3.x with PyTorch (or TensorFlow, if preferred) and MATLAB R2021a+. Provide any additional open-source libraries in a requirements list. Once the code reproduces the baseline accuracy and demonstrates a measurable structural-performance gain, the project is considered complete and ready for the n...
...build from scratch: data prep, training, packaging, deployment, and a quick monitoring stub, with code hosted in a clean Git repo I can reference later. • Short homework tasks between calls so I cement what we covered and come prepared with questions. If you have experience turning Jupyter prototypes into scalable production services on GCP using Python frameworks such as FastAPI, Flask, or TensorFlow Serving, I’d love to hear how you can guide me. Clear explanations, screen-sharing while you code, and the ability to leave me with reusable scripts or templates are essential....
...concept, but I now need the scene brought to life with responsive touch/gesture input through AR Foundation and polished 3D assets. Unity 3D (URP) will drive the core app, while MediaPipe or OpenCV will handle hand-tracking and gesture recognition. On the hardware side the code must remain portable to Raspberry Pi OS and Arduino-based prototypes, so a clean Python or Node.js bridge is essential. Here’s what I’d like you to deliver: • A Unity scene that compiles on Android and iOS, wired for AR Foundation with robust user interaction logic (tap, pinch, swipe, custom gestures). • Integration of MediaPipe/OpenCV scripts so gestures trigger in-app events with minimal latency. • A small library of optimized 3D models (Blender or Maya) that match...
...rescuers can be dispatched quickly. I’m flexible about the imagery source—NASA, ESA, Google Earth, or any other free feed is fine as long as it delivers cloud-free, high-resolution scenes. You can use the tool to capture screenshots by moving in circles around the selected location. The detector has to work at desert scale, so please build it with an established computer-vision framework (e.g., TensorFlow, PyTorch, YOLO, or a similarly robust model) and output the findings in both human-readable (an image with bounding boxes or a simple web map) and machine-readable form (CSV/GeoJSON with lat/long, time stamp, confidence score). Once I apply the tool to a new location and receive a list of car and truck pictures and coordinates automatically reflected on the map, n...
...dashboards (heatmaps, multi-lottery support). 3. Detailed Implementation 3.1. Data Structure: DB schemas for lotteries (rules/ranges/draw days), full history (indexed), user settings (no credentials). 3.2. Draw Day Changes: internal API with official validation & calendar sync. 3.3. Cost Calculation: dynamic Python functions + jackpot scraping & EV. 3.4. Prediction: train RNN/LSTM (PyTorch/TensorFlow), combinatorial generation (itertools), Genetic optimization. 3.5. Backtesting: parallel scripts (multiprocessing), no bet limits, model cross-validation. 3.6. Automation: as in 2.5, with execution logs and real-time UI feedback. 4. Conclusion System developed for exclusive personal use, integrating data collection, multi-model AI optimization, supervised secure au...
...those classes, it must immediately push an alert to my back-end (REST webhook is fine) and simultaneously initiate recording on the camera stream. Speed is critical: I’m targeting sub-100 ms inference per frame on an Nvidia Jetson Xavier, yet I still need accuracy good enough to avoid nuisance alerts in busy scenes. You’re free to choose the framework you prefer—YOLOv8, Faster R-CNN, or a custom TensorFlow / PyTorch implementation—as long as the final package runs headless in Linux and can be containerised (Docker) for deployment. Please include: • A fully trained model with reproducible training pipeline • Real-time inference script that ingests RTSP feeds and exposes JSON alerts • Simple unit test clips proving correct detection and...
...subscription plans Signal history & performance tracking Alerts via Telegram, email, webhook, or mobile push Admin System Signal monitoring dashboard Strategy performance analytics User & subscription management Technical Stack (Flexible) We are open to: Frontend: React / / Vue Backend: Node.js, Python, or C# (.NET) Data: WebSocket market feeds + historical OHLCV storage AI/ML: TensorFlow, PyTorch, or equivalent Infra: Scalable cloud architecture, API-first design Budget High budget — We prioritize quality, performance, and long-term scalability over cost. TRADE LOGIC sample • [TREND] Channel trend: Downtrend • [MSB] 3 out of the last 5 MSBs are Bearish pattern breakouts • [MSB] Price near the last MSB level (0.17%) - Resistance &bu...
...An example water module plus at least one non-water module (finance, social media, or marketing—your pick) • Concise user documentation and a short video or GIF that walks through the hand-off / execution / result flow The simpler the tech stack the better, but I’m fine with Python, Node.js, or a small Rust service if you feel performance demands it. Lean on well-supported libraries such as TensorFlow, PyTorch, scikit-learn, or OpenAI’s function-calling API—whichever speeds you toward a clean, repeatable release. I’ll sign off once I can: 1. follow your README to spin up the app on my own machine, 2. assign a sample water job plus an unrelated business task, and 3. see both completed autonomously with logs confirming each step. If tha...
I have a curated dataset of abdominal X-ray images that needs a robust deep-learning model capable of classifying key clinical findings. The end goal is a production-ready Python solution that can consistently score above 90 % accuracy on an unseen validation set. You’ll start with any mainstream framework you prefer—TensorFlow, Keras, or PyTorch—and handle the full pipeline: data preparation and augmentation, model architecture selection, training, hyper-parameter tuning, and evaluation. Please keep the code modular and well-commented so I can retrain or fine-tune later as new data comes in. A concise report that explains your decisions, metrics, and suggestions for future improvements will also be appreciated. To help me choose quickly, focus your proposal on y...
...images—specifically plain-film X-rays—and tell me whether each study is of the chest, abdomen, or an extremity. All input files will be standard hospital exports (mostly DICOM, occasionally PNG/JPEG), so the model must handle typical variations in resolution and contrast. What I’m after is a reproducible, well-documented solution: data preparation, augmentation, model architecture (a CNN in TensorFlow, Keras, or PyTorch is fine), training, and evaluation. Please include class-balanced splits, explain any preprocessing you apply, and show the metrics you achieve on an unseen validation set. Deliverables • Python code with clear comments for preprocessing, training, and inference • Trained model weights ready for deployment • A short report ...
...belongs to (e.g., chest PA vs. chest lateral, cervical spine, hand, etc.). The job is strictly about classifying the type of X-ray, not diagnosing any pathology. Here is what I already have and what I expect from you: • A curated folder structure with several thousand labelled PNG and DICOM files that you can download from my secure server. • A preference for Python with either PyTorch or TensorFlow/Keras—use whichever framework you feel will achieve the best accuracy and fastest inference on a modern GPU. • Clean, reproducible code (Jupyter notebook or script) plus a short README that explains environment setup, training commands, and how to run inference on a single file or a batch. • A trained model file and a simple inference function/CLI th...
...(MediaPipe / TensorFlow Lite / OpenCV / YOLO, etc.) • Demonstrate: 1. Live head tracking 2. Auto zoom in real time 3. Stable performance (≥15 FPS) Deliverables • Full source code • Build instructions • Short demo video showing real-time performance • Explanation of how this will later integrate with a custom camera SDK Once validated, this prototype will be integrated into our production system. Target Platform (Future Integration) Our production system will be: • Android-based smart mirror • Wi-Fi camera input (custom SDK) • C/C++ (NDK) + Java/Kotlin • Video resolution: up to 4K • P2P local streaming (no cloud) So experience with embedded/mobile video pipelines is important. Preferred Skills • Computer Vis...
...meters, then reports everything wirelessly to the Pi for processing. Here’s what I need from you: • Python (Raspberry Pi) and MicroPython/C++ (ESP32) code that ingests the raw sensor streams, pushes them through an on-device model, and decides—within seconds—whether to start or stop the main pump and which solenoid valves to open. • An ML pipeline: training notebooks, a lightweight model (TensorFlow Lite or similar) and the inference wrapper that runs locally. The model must act on current soil-moisture readings and short-term weather data, while also generating three forward-looking insights: predicted soil moisture over the next 6–24 h, likely weather changes in that window, and the water volume the system will probably consume. • Cont...
...After auto capture: * Show a preview screen of the captured image * Provide two actions: * Save → confirm and store the captured image * Retake → discard the image and return to live camera view * Ensure retake resets detection and overlay state Technical Requirements * Technology Stack: React Native with Expo * Use Expo Camera * Corner / black box detection using: * OpenCV (WebAssembly or Expo-compatible approach), or * Any optimized computer vision technique suitable for Expo * Must work on Android and iOS * Performance optimized for real-time detection Deliverables * Fully functional camera module with: * Live 4 black box detection * Green overlay confirmation on successful detection * Automatic high-resolution image capt...
...back-office system. • Flag high-risk clients to underwriters with an explanation score so they can override or request extra documentation. • Present all of this via a REST/JSON API that my current React front end can call, and expose a lightweight Python-based admin dashboard where underwriting managers can adjust thresholds and retrain models on fresh data. I work mainly with a Python solution (TensorFlow, PyTorch, or scikit-learn—whichever you feel is best) will plug in cleanly. The customer-facing chatbot or form assistant can be built with a service such as Rasa or a fine-tuned OpenAI model as long as it routes seamlessly into the same risk engine. Acceptance criteria 1. A reproducible training pipeline with clear data-schema docs. 2. Dockerised API...
...continually scouting, testing, and refining state-of-the-art models in three core areas: text generation, sentiment analysis, and machine translation. Scope of work — Track current research and emerging repositories (Hugging Face, arXiv, GitHub) to spot promising architectures and training techniques. - klaud8 / hrm ai / chat gpt / claude — Spin up controlled experiments in Python using PyTorch/TensorFlow, comparing baseline performance with fine-tuned variants on representative datasets. — Optimise inference speed, memory footprint, and prompt-engineering workflows so models transition smoothly from notebook to production API. — Document findings in concise experiment reports and integrate successful models into our existing CI/CD pipeline. De...
I have a cleaned dataset containing donor health information and I want a lightweight web app that predicts the likelihood of a person making a future donation. When a visitor submits their details, the model should outp...only the prediction score. 3. Implement email automation (SMTP or a trusted API such as SendGrid) that fires immediately after each prediction. Deliverables: • Trained model file and reproducible training script • Source code for the web app with clear setup instructions • Brief README explaining how to retrain and change email credentials If you have prior work with scikit-learn, TensorFlow, Flask, Django, or similar tools, please mention it. I look forward to seeing a working demo deployed on a free tier (Heroku, Render, or comparabl...
I'm seeking an experienced AI developer to create a computer vision model focused on detecting people. The model will need to function effectively in both indoor and outdoor environments. Key Requirements: - Primary function: Object detection with a focus on people - Adaptable to both indoor and outdoor settings ...focus on people - Adaptable to both indoor and outdoor settings - High accuracy and reliability Ideal Skills and Experience: - Expertise in AI and machine learning - Strong background in computer vision, particularly in object detection - Experience with datasets and training models for varied environments - Proficiency in programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch Please provide examples of similar projects yo...
I’m looking for a data scientist based anywhere in Latin America to help me create reliable predictive models for a finance-focused project. You’ll start with large historical datasets stored in SQL and deliver models that accurately forecast key financial indicators. I work mainly with Python, so you’ll find Pandas, NumPy, Scikit-learn and, when deep learning is justified, TensorFlow already in place. If you prefer R for certain tasks, that’s perfectly fine as long as the final workflow remains reproducible. The end-user needs to consume insights through Power BI, so once the model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding ...
...junior AI engineers and contribute to technical leadership • Conduct research and implement state-of-the-art AI techniques • Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience with REST APIs and Microservices • Excellent ana...
...code in Python myself, so please keep the architecture transparent: well-named modules, docstrings, and a that pins every dependency. Back-tests on at least two years of 5-minute data, a walk-forward validation segment, and a short README outlining how to reproduce the results will be my acceptance criteria. If you already have experience with pandas, NumPy, scikit-learn or TensorFlow, and you know how to talk to Indian broker APIs via REST or websockets, this should feel familiar. Let me know the models or reinforcement-learning frameworks you think best suit intraday equity trading and how you will protect against over-fitting....
...confidence scores, and report generation—all with strict patient privacy, no storage of originals, and human oversight required. Key Requirements: • Clean React/ frontend with drag-and-drop upload, DICOM viewer (e.g., ), annotation overlays & heatmaps. • Python backend (FastAPI preferred) + secure auth, encrypted file handling, and cloud storage (AWS S3/GCP). • PyTorch/TensorFlow ML models (fine-tune YOLO/U-Net/MONAI on open dental datasets) for multi-label detection/segmentation. • Mandatory: Full anonymization on upload (pydicom/deid), end-to-end encryption, audit logs, compliance-ready (HIPAA/GDPR/APP principles), ethical transparency (e.g., explainability features). • Cloud deployment (AWS/GCP/Azure, serverless ideal). NDA required. ...
...something tangible, and a closing block on “Advanced Machine Learning Techniques” that shows them what’s possible beyond the basics. Because the colleges have explicitly asked for hands-on labs rather than slide-only lectures, your material needs to revolve around live coding, interactive notebooks, and short build-and-test cycles. Required expertise • Solid command of Python, scikit-learn, TensorFlow or PyTorch, plus NLP libraries such as spaCy or NLTK. • An educator’s mindset: you can explain core concepts clearly, scaffold complexity, and troubleshoot student code in real time. • Proven history of running workshops—either academic or corporate—within tight timelines. Deliverables 1. Detailed session plan (3 tracks, 8...
...moments, trim away the rest, merge the highlights, and export a seamless final cut. While doing so it should apply modern, clean-looking filters and subtle visual effects—think gentle color grading, light motion graphics, and smooth transitions that feel current rather than retro or flashy. I’m open to the underlying tech stack as long as the result is reproducible. If you prefer Python with OpenCV and FFmpeg, or would rather leverage an environment like Adobe’s Sensei APIs, that’s fine; just outline your approach. What matters is that the pipeline can be triggered from a simple command or button press and deliver a ready-to-publish MP4 or MOV in vertical 9:16. Deliverables • A working script, model, or plug-in that completes the trim-merge-fi...
...straightforward: the program captures frames, an AI model spots the person, and a virtual stick signal (XInput, vJoy or a comparable driver) nudges the aim every frame so it stays centred. Smoothness and speed are critical. On a 1080p feed I’m aiming for roughly 60 fps with no more than 40–50 ms end-to-end latency, so techniques such as YOLOv8, TensorRT or a lightweight custom network combined with OpenCV screen capture should fit. You’re free to choose Python, C++, or another language as long as the final build runs reliably on Windows 10/11 without needing exotic dependencies. Deliverables • Windows executable with a small GUI (start/stop, confidence slider, aim-speed slider, hotkey toggle) • Source code and the pretrained model that recognises p...
I need an expert to improve the accuracy of a histopathologic cancer detection model. The current model needs enhancement, and I prefer using algorithm enhancement for this task. Key Requirements: - Improve the m...enhancement for this task. Key Requirements: - Improve the model's accuracy in detecting cancerous tissues. - Use advanced techniques and methodologies for algorithm enhancement. Ideal Skills and Experience: - Expertise in machine learning and deep learning - Strong background in medical image analysis - Experience with histopathological images - Proficiency in Python and relevant libraries (TensorFlow, Keras, PyTorch) - Familiarity with model evaluation and performance metrics Please provide examples of similar work and a detailed approach to how you would tackl...
...- Timing data can be mocked for MVP Highlights: - Automatically record each run - Generate short highlight clip after run - Play highlight before next competitor Tech Stack: - Python, OpenCV, YOLOv8 (or similar), FFmpeg / RTMP, Docker (optional) Deliverables: - Working MVP prototype - Source code (modular, documented) - Demo video or test livestream - Basic setup instructions Why This Project is Interesting: - Real-world AI application - Clear commercial potential - No vague AI promises - Opportunity to shape a real product from the start REQUIREMENTS: Tech stack (preferred, flexible) - Python - OpenCV - YOLOv8 (or similar) - FFmpeg / RTMP - Optional: Docker - Frontend UI can be minimal or skipped for MVP Deliverables - Working MVP prototype - Source code (modular, ...
...conversion/refactor: exact same model, exact same results, but with modern syntax, clear separation of concerns, and thorough inline comments. You’ll start from my original scripts and checkpoints, preserve every bit of accuracy, and hand back a fully functioning module (including a simple demo script) that can be installed with pip-installable requirements. Feel free to streamline library calls—TensorFlow, PyTorch, OpenCV, or whatever is currently in place—so long as the final inference output matches the reference I provide. Deliverables • Refactored Python package replicating the current predictions on a supplied test set • README covering setup, dependencies, and usage • Quick comparison report showing identical mAP or better ag...
... Generate a ready-to-print shipping label (standard 4x6 thermal format). 2. Push the full XML payload to our in-house system through a REST-style API. I expect the software to run unattended on Windows, include simple UI panels for device status and manual override, and log every transaction for traceability. Code can be in C#, Python or another language suited to serial/USB device control, OpenCV and Tesseract OCR; just keep external dependencies to well-supported, licence-friendly libraries. Deliverables • Executable application and source code • Sample XML output reflecting the required structure • API endpoint documentation showing request/response examples • Deployment guide for connecting the scale, volumetric camera and scanner Accuracy, sp...
...extend recognition beyond the basic horizontal/vertical stripes to oblique, curved, or irregular banding the current code ignores. • Optimize performance: refactor the pipeline for faster image loading, GPU-aware inference, and leaner memory use so it remains responsive on large datasets. Everything runs in Python, so please stay within that ecosystem. You are free to introduce OpenCV, scikit-image, PyTorch, TensorFlow, or other libraries, provided the final solution installs cleanly with a and runs from a single entry-point script or Jupyter notebook. Input will be folders of images; no video or live feed integration is required at this stage, but laying groundwork for future expansion is a plus. I will supply a labeled image set for benchmarking and expect a sh...
...effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality • Custom model implementation with commented rationale for design decisions • Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines • Final technical report summarizing methodology, results, and potential publication avenues ...
...and push actionable alerts through a cloud-hosted pipeline. Later phases will weave in IoT sensors, GPS data and public-transport schedules, but for now cameras take centre stage and all processing happens in the cloud. Here’s what I need from you: • A complete stream-to-insight workflow: camera feed ingestion → cloud message bus → analytics micro-service. • Computer-vision models (OpenCV, YOLO, TensorFlow—your call) that flag incidents with >90 % precision/recall on my test clips. • A REST API that surfaces live traffic state and returns diversion routes in real time. • Extension hooks so I can sync bus, train and metro timetables and forward delay alerts to commuters. • Containerised or serverless deployment scr...
...effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality • Custom model implementation with commented rationale for design decisions • Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines • Final technical report summarizing methodology, results, and potential publication avenues ...
...needs to handle: - Banking interactions (finding banker, opening interface) - Withdrawing logs - Walking to firemaking spots - Lighting fires - Managing inventory I have plenty of screenshots and reference images to work from, so you won't be starting from scratch! What I'm looking for: I need someone who's: - Really comfortable with Python on macOS (not just Windows/Linux) - Has experience with OpenCV or similar computer vision libraries - Has built game bots/automation scripts before - Understands how to make mouse movements and clicks look human (not robotic!) - Knows their way around Git Bonus points if you: - Have a background in ML or computer vision - Have experience with OSRS or similar games - Know macOS UI automation inside and out - Have thought about...
...object detection model built, trained, and packaged so it runs smoothly on iOS and Android devices, in a modern web browser, and as a lightweight desktop application. The same model should power every platform to keep accuracy and behaviour consistent. You are free to choose the framework you are most comfortable with—TensorFlow, PyTorch, YOLOv8, Detectron2, or another proven library—as long as the final artefacts meet these requirements: • Mobile: optimised builds (e.g. TensorFlow Lite, Core ML, or ONNX) that hit realtime speeds on mid-range phones. • Web: WebAssembly/WebGL or implementation that loads in under three seconds on a standard connection. • Desktop: a small executable or Python app with GPU support when available and CPU fallb...
... Secondary expectations include light cooking, everyday chores, and fluid, human-like movement so the machine blends safely into family life. I am looking for an end-to-end solution covering: • Hardware: anthropomorphic frame, compliant actuators, force-torque sensors, depth & RGB cameras, and medical-grade vitals sensors. • Software: ROS-based control stack, SLAM for navigation, TensorFlow/PyTorch models for vision and speech, and a secure mobile dashboard. * Ability to learn and adapt. * Chatgpt integration. * Human-like skin and features. * Ability to ship to usa. Ability for owner to pack in a box and ship to Philippines easily. • Safety & compliance: redundant fail-safes, IEC/ASTM child-safety standards, and a hygiene-certifiable ...
The project centres on building a production-ready text-classification pipeline that leverages modern deep-learning techniques. I have a labelled dataset and need end-to-end code that ingests the text, handles cleaning and tokenisation, and trains an accurate classifier. Python is the preferred language; using PyTorch, TensorFlow or another mainstream framework is fine as long as the solution is reproducible and easy to extend. Key deliverables: • Well-commented source code (data loading, model, training loop, evaluation) • Clear instructions to run training on a fresh machine (README or notebook) • Metrics report showing accuracy, precision, recall and F1 on a held-out set • Exported model weights and a small inference script or API endpoint for batch pre...
...a MEAN stack/Python developer. You will be helping us create and implement the following: • An adoption roadmap that ties specific AI capabilities to each stage of our workflow and project milestones, showing where automation, prediction, or generative content delivers the most value. • A reasoned “why this, not that” selection of tools—think Hugging Face transformers versus OpenAI GPT-4, TensorFlow or PyTorch for model training, spaCy for NLP, Vision APIs for image tasks—plus rapid prototypes that prove the choice. • Drop-in reference implementations or micro-services that slot straight into our existing Node/Express back end. • Plain-language docs and two-minute screen-share videos so community groups, national NGOs, and global...
...provides the mathematical baseline — the system blends both outputs Implement smart caching and pre-computation so API latency doesn't slow down decision-making (pre-fetch likely scenarios, cache common spots) Fallback logic: if the API is slow or unavailable, the bot defaults to the GTO baseline so it never stalls mid-hand 2. Screen Reading / Game State Extraction (The Eyes) Use computer vision (OpenCV + Tesseract OCR or similar) to read the poker table from screen captures or browser window Must accurately extract: hole cards, community cards, pot size, stack sizes, player positions, betting actions, blinds/antes, tournament stage Card recognition model (CNN or template matching) with high accuracy Must work reliably across at least one major free poker platform ...
...object detection model built, trained, and packaged so it runs smoothly on iOS and Android devices, in a modern web browser, and as a lightweight desktop application. The same model should power every platform to keep accuracy and behaviour consistent. You are free to choose the framework you are most comfortable with—TensorFlow, PyTorch, YOLOv8, Detectron2, or another proven library—as long as the final artefacts meet these requirements: • Mobile: optimised builds (e.g. TensorFlow Lite, Core ML, or ONNX) that hit realtime speeds on mid-range phones. • Web: WebAssembly/WebGL or implementation that loads in under three seconds on a standard connection. • Desktop: a small executable or Python app with GPU support when available and CPU fallb...
