Custom Computer Vision Model Development

@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.

@apexd2

I am a perfect fit for your project specializing in developing bespoke computer vision solutions. I acknowledge the need for clean, professional, and seamless implementation of a custom image recognition system. While I am new to freelancer, rest assured of my expertise in Python, TensorFlow, and PyTorch with a proven track record off-site. I would love to chat more about your project! Regards, Johan De Villiers

@kaylinr1

Dear Nikhil Singh, This project aligns well with my background and expertise. You’re looking for a clean, professional, and user-friendly platform with seamless integration and automation—exactly the kind of solution I deliver with precision. With skills in PHP, JavaScript, Python, HTML, MySQL, WordPress, and Software Architecture, I offer full digital solutions from design to backend systems, Android apps, and AI tools. While new to Freelancer, I have extensive off-site experience. I look forward to the opportunity to discuss your project requirements further. Regards, Kaylin Ross

@mihirg63

Hi, Your project requires a bespoke computer-vision model beyond standard detection/segmentation, which is an exciting challenge. I have hands-on experience in TensorFlow, PyTorch, and ONNX export, building models that learn meaningful visual patterns and deploy efficiently. ✅ Proposed Approach: Data Understanding & Preprocessing – Review provided samples under NDA, design augmentation pipeline to boost generalization. Model Selection & Training – Experiment with CNNs/transformer-based backbones depending on data size/complexity. Train & fine-tune with metrics aligned to your business goals. Evaluation & Optimization – Validate accuracy, latency, and robustness; prune/quantize if needed for faster inference. Deployment Packaging – Export trained weights (PyTorch/TensorFlow → ONNX if required), and expose through a lightweight REST/gRPC API. Documentation & Handoff – Clear README, retraining scripts, reproducible notebooks, plus a brief demo video confirming targets. ⚡ Deliverables: Preprocessing & training scripts Trained model weights + inference API Setup & retraining guide ? Timeline (draft): Week 1: Data review & preprocessing pipeline Week 2: Baseline model training & evaluation Week 3: Optimization & API deployment Week 4: Documentation, demo, and handoff Would you like me to also suggest two technical milestone options (baseline model vs optimized deployment) so you can decide based on accuracy vs speed trade-offs? Best regards, Mihir

@Bourahlamayas

Hello Regarding your computer vision project, I would be delighted to assist. As an AI engineer, I've worked with deep learning (using PyTorch and TensorFlow) and putting models into production. I'm at ease creating custom pipelines from scratch because I've worked on projects that go beyond standard classification/detection, such as OCR, document understanding, and image-based anomaly detection. My strategy would be: Create a robust dataset setup and augmentation. Choose and train a model architecture (with ONNX export option) that meets your performance and latency requirements. Create training and inference scripts that are repeatable (well-documented notebooks/code) Use a lightweight API to server the trained model Provide a README along with a brief demonstration (screenshots or video) of the model operating as agreed. My favorite tools are FastAPI/Flask for serving, ONNX for portability, and PyTorch for flexibility. Proposed milestones: Data review & preprocessing pipeline Baseline model training & evaluation Optimized model + inference module API packaging & documentation Final demo + delivery A couple clarifying questions before kickoff: Do you already have labeled data, or will we need to design the annotation pipeline? Any target latency constraints (e.g., inference under X ms)? Any hardware preferences (CPU/GPU for deployment)?

@asadr93

Hello I specialize in Computer Vision & AI model development with Python, TensorFlow, and PyTorch. I can build your bespoke vision model end-to-end: data preprocessing, training, tuning, and deployment (REST/gRPC API with ONNX export if needed). You’ll get clean, reproducible code, trained weights, and documentation with a quick demo to validate accuracy & speed. Let’s connect after the NDA to align on dataset and targets. Best regards, Asad