Branded Accessories Recognition AI

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@ABDOKAMA

Hi, I have experience in computer vision, including: Detecting animals in wildlife. Developing a project that segments hair and skin on faces. Working on agricultural projects that detect crops and identify diseases in plants. I am eager to apply these skills to new challenges and contribute to impactful projects.

@dushyant317

I am a seasoned software developer with 13 years of experience, holding a degree from IIT Delhi. My expertise aligns perfectly with the required skills for your project. I have successfully delivered complex solutions across diverse domains with a focus on quality and scalability. I bring strong problem-solving ability, hands-on technical depth, and client-centric delivery. I am confident I can add value to your project and deliver results within timelines.

@AashieqJoseph

Guaranteed results ✅, or zero cost to you ?. I understand the importance of brand-level accuracy and efficient inference times in your image-recognition project. Leveraging my expertise in deep learning and computer vision, I will curate a high-quality dataset, implement a custom PyTorch pipeline for precision, and ensure seamless integration for a user-friendly experience. While new to Freelancer, I come with a proven track record and offer discounted rates to showcase top-notch quality. Let's connect and discuss how I can exceed your expectations! Regards, Aashieq Joseph

@jinufeb14

Being ranked in the top 0.03% of freelancers, you can trust that I bring a unique combination of expertise and efficiency to your project. My deep understanding of Machine Learning (ML) will empower us to build the ideal dataset strategy for this project. Whether it involves sourcing, labelling, or augmentation, we'll ensure each step is executed meticulously for optimal performance. Based on the complexities of your task, I propose exploring frameworks like YOLOv5/8 or EfficientDet to meet your requirements without compromising on precision. Understanding the value of brand-level recognition for you, my focus would be on developing an architecture that delivers that precision. Given your specific objectives, a custom PyTorch or TensorFlow pipeline seems fitting but I'm always open to discussing alternative approaches if you'd like. From my end, you can expect to receive not just the model weights and code but also detailed installation notes and a demo REST endpoint (or notebook) to ensure smooth hand-off. Ultimately, my goal is to build an AI solution that meets your acceptance criteria; achieving at least 90 % accuracy on a validation set with inference running in under 200 ms on a modern GPU. By choosing me, you're guaranteed dedication and proficiency—critical factors in building an end-to-end image recognition system as complex as this. Let's transform your vision into reality!

@FrankPO

I am a perfect fit for your project, aiming to deliver a high-precision image recognition system for handbags and watches, focusing on brand-level accuracy and speed. Utilizing PyTorch or TensorFlow, I apply custom datasets, efficiently labeled and augmented, to ensure 90% or higher accuracy. While new here, my experience off-site ensures top-notch model delivery, including code, weights, and a demo. I would love to chat more about your project! Regards, Frank Osler

@niveditaj1

Hello, I have done a SIMILAR project in the past & if you want, I can share its screenshots For your requirement, I can build the end-to-end image recognition solution for identifying handbag and watch brands. My approach will be to use a two-stage process for high accuracy. First, I will use a popular object detection model like YOLO to locate the items in an image. Once an item is located, a second, specialized classification model will be used to identify its specific brand. For the dataset, I will start with publicly available images and apply extensive data augmentation to create a robust training set. The final deliverable will include the trained model weights, a lightweight inference script, and a demo notebook, all optimized to meet your performance targets. 1) Can you provide the initial list of handbag and watch brands to be identified? 2) Could you share a few sample images that are representative of your use case? 3) What specific GPU will be used for inference so I can benchmark against your speed requirement? 4) What will be product images or Brands? Thanks, Nivedita

@abdulwassay72

I can deliver a complete end-to-end image recognition solution that not only detects handbags and watches but also identifies the exact brand with high accuracy. My approach will use YOLOv8/YOLOv9, fine-tuned on a carefully prepared dataset where each bounding box is labeled with both product type and brand. Dataset strategy: I will combine your provided data with curated sources, ensuring balanced representation across brands. Rare classes will be strengthened with targeted augmentations (mosaic, copy-paste, lighting changes) while preserving key brand cues such as logos and textures. Validation will be split by brand to guarantee fair evaluation. Model training: Starting with pretrained YOLOv8/YOLOv9 weights, I’ll fine-tune for brand-level classification. The model will be optimized for both accuracy and speed and benchmarked to meet the requirement of ≤200 ms inference per image on a modern GPU. Deliverables: Trained YOLOv8/YOLOv9 weights Inference script (CLI) for predictions REST API (FastAPI) with `/predict` endpoint Demo notebook and documentation for setup and usage Acceptance criteria: ≥90% brand-level accuracy on your validation set, with inference times under 200 ms. Would you prefer a single YOLO model with all brands as classes, or keep it flexible for future expansion? Best regards, Wassay

@webskittersIT

100% Job Success | 200+ Projects | 7+ Years of Experience | 2000+ projects Please check my profile:https://www.freelancer.com/u/webskittersIT Hi there, I understand your need for an end-to-end image recognition system that not only detects handbags and watches but also delivers brand-level accuracy with speed. This is exactly where my expertise fits. My approach: • Build a robust dataset pipeline with sourcing, cleaning, labeling, and heavy augmentation to capture real-world conditions. • Use a scalable deep learning architecture such as YOLOv8 or EfficientDet, fine-tuned for your brand list. • Optimize for inference speed under 200 ms while ensuring over 90 percent accuracy. • Deliver production-ready assets, including model weights, inference script or REST API, setup notes, and a demo notebook. Let’s connect to align on dataset strategy and framework choice so I can deliver a high-performing solution tailored to your needs. Best regards, Atanu.S (SM)

@datahub0

Thank you for the opportunity to work on your project. I've reviewed your requirements and am confident in delivering high-quality results within your timeline and budget. I also bring creative solutions and techniques that can enhance your project's performance and value. As a gesture of goodwill, I'm happy to add ons new feature of your choice at no extra cost. Let's build something exceptional together. Best regards, Muhammad Abdullah Baig

@pedroh498

Hello, I have just read your job description carefully about Branded Accessories Recognition AI. I can help you achieve high brand-level accuracy by first building a curated and augmented dataset of handbags and watches using reliable sources and preprocessing to ensure balanced brand representation. Then, I will design a detection+classification pipeline—either a two-stage approach (YOLOv8/EfficientDet for object detection + brand classifier) or a fine-tuned end-to-end PyTorch/TensorFlow model optimized for speed and precision. Finally, I will deliver production-ready outputs: trained weights, clean inference code (REST API or script), setup notes, and a demo notebook. My focus will be to meet your 90%+ accuracy target and ensure inference stays below 200ms on a modern GPU. Let’s collaborate to make this AI both fast and brand-accurate. Best regards, Pedro

@quangduyst392

Hi, I’ve reviewed your requirements and believe I’m an excellent fit. I have strong experience building end-to-end computer vision pipelines for product recognition, covering both object detection and fine-grained brand classification. I’ve delivered projects in fashion, accessories, and consumer goods with high accuracy in production. Approach: Dataset: Use your data + public brand images, apply consistent labeling (CVAT/Label Studio), and advanced augmentation (color jitter, cutmix, background replacement) to simulate real-world scenarios. Model: YOLOv8/10 for fast handbag & watch detection. For brand recognition, a two-stage pipeline: Detection (YOLO). Classification (EfficientNet or Vision Transformer fine-tuned on brand set). Optionally, an end-to-end multi-class detector if dataset size allows. Optimization: Export to ONNX/TensorRT for <200ms inference latency on a single GPU. Deliverables: trained weights, inference API (REST or Python script), demo notebook, and documentation. Target: ≥90% brand-level accuracy on your validation set while keeping inference speed within your requirement. I’d be happy to refine the solution further based on your brand list and dataset. Looking forward to collaborating with you.

@ducna17

Hi, I’ve reviewed your requirements and believe I’m a strong fit. I have hands-on experience delivering end-to-end computer vision pipelines for product recognition (object detection + brand classification) in production with high accuracy. Approach for handbags & watches: Dataset: Combine your data with public brand imagery, ensure consistent labeling (Label Studio/CVAT), and apply advanced augmentations (color, cutmix, background replacement) to simulate real-world conditions. Model: Use YOLOv8 for fast, accurate detection. For brands, apply a two-stage pipeline (YOLO + EfficientNet/ViT) or end-to-end multi-class detection if data allows. Inference optimized with TensorRT/ONNX for <200ms latency. Deliverables: Trained weights (detection + classification), inference API (REST/Python), demo notebook, and documentation. Performance: ≥90% brand accuracy on your validation set; <200ms inference per image on GPU. Why me? Proven track record in brand/product recognition, strong expertise in PyTorch/TensorFlow/MLOps, and ability to deliver both research-level accuracy and production-ready solutions. I’d be happy to discuss your brand list and dataset to refine the plan. Looking forward to collaborating.

@pvsysgroupinc

★★★ TOP 1%★★★ I’m excited to bring my experience as our CTO as AI Leader (IIT Roorkee INDIA, 2017) with 10+ years of building AI-powered products from idea to market. I lead a development team that helps early-stage founders launch MVPs faster, smarter, and cost effectively. We specialize in software architecture, deep learning, full-stack development, AWS, and CMMI 3.5 project management—covering covering the full spectrum from strategy to execution. Our portfolio spans startups and enterprises: Startup Products * AI-powered pre-sales analytics using CCTV. * AI-driven physiotherapy diagnosis & safer facial X-rays. * Smart doorbells with AI + IoT for enhanced security. * NLP-powered policy access for enterprise employees. Corporate Consulting * Adani Power – AI-driven boiler efficiency optimization. * Smart City Vadodara – preventing slum formation via early detection. * Deloitte, Genpact & Cyient – enabling AI readiness and advanced analytics. These projects show our ability to solve real-world problems across energy, healthcare, urban planning, and enterprise consulting with scalable AI solutions. I’d love to apply this blend of technical depth and execution-focused leadership to deliver your product quickly and effectively. Let’s connect to explore how we can accelerate your roadmap. Regards, PVSYS GROUP " IF YOU THINK THEN WE CAN"

@deepanshud24

I propose building an end-to-end handbag and watch recognition system that goes beyond generic detection to deliver brand-level classification with ≥90% accuracy on your validation set, while keeping inference under 200 ms on a modern GPU. The dataset will be assembled from brand-owned media, e-commerce listings, and licensed image sources, combined with expert labeling of bounding boxes and brand tags, and enhanced using logo-preserving augmentations, background randomization, and active learning to balance long-tail classes. For the model, I recommend a two-stage pipeline: a YOLOv8-based detector for locating handbags and watches, followed by a lightweight ConvNeXt/ViT brand classifier on the cropped regions, ensuring both speed and precision. I bring strong experience in Convolutional Neural Networks (CNNs) and have successfully applied them in medical imaging, including a project on pulpitis detection that achieved high diagnostic accuracy—demonstrating my ability to design, train, and deploy advanced vision models for real-world applications. At hand-off, you will receive the trained model weights, complete training and inference code (PyTorch/TensorFlow), installation and setup notes, plus a demo in the form of a Jupyter notebook and a lightweight REST API for easy integration

@Raghav969696

hello sir , How many brands do we need to support in v1, and will new ones be added over time? Do you already have labeled images, or should I handle sourcing + labeling entirely?