You will get A custom Visual Question Answering VQA model using Deep Learning
Project details
I will build a high-accuracy Visual Question Answering (VQA) model that combines Computer Vision and Natural Language Processing to answer natural language questions about your images.
Whether you need a proof-of-concept prototype or a production-ready system, I use state-of-the-art Deep Learning architectures (such as Transformers, ViT, ResNet, and BERT) to deliver precise results.
My services include:
Custom Model Design: Building architectures that fuse image features (CNNs) with text embeddings (RNNs/Transformers).
Data Handling: Preprocessing, augmentation, and formatting your dataset for optimal training.
Training & Fine-Tuning: optimizing models to achieve high accuracy on your specific domain (e.g., medical, security, or retail).
Evaluation: Providing detailed metrics (Accuracy, BLEU scores) and visualization of attention maps.
Why work with me? I specialize in PyTorch and have hands-on experience deploying VQA systems. I focus on clean, documented code and clear communication throughout the project. Let's turn your visual data into actionable insights!
Whether you need a proof-of-concept prototype or a production-ready system, I use state-of-the-art Deep Learning architectures (such as Transformers, ViT, ResNet, and BERT) to deliver precise results.
My services include:
Custom Model Design: Building architectures that fuse image features (CNNs) with text embeddings (RNNs/Transformers).
Data Handling: Preprocessing, augmentation, and formatting your dataset for optimal training.
Training & Fine-Tuning: optimizing models to achieve high accuracy on your specific domain (e.g., medical, security, or retail).
Evaluation: Providing detailed metrics (Accuracy, BLEU scores) and visualization of attention maps.
Why work with me? I specialize in PyTorch and have hands-on experience deploying VQA systems. I focus on clean, documented code and clear communication throughout the project. Let's turn your visual data into actionable insights!
Machine Learning Tools
Azure Machine Learning, BERT, ChatGPT, Databricks MLflow, GitHub Copilot, Google Sheets, Keras, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Sonnet, SQL, TensorFlow, Vertex AI, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$80
|
Standard
$450
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$40 - $200
Additional Revision
+$50
Model Documentation
(+ 5 Days)
+$100
Source Code
(+ 6 Days)
+$150Frequently asked questions
About Boules
Machine Learning Engineer | Computer Vision & NLP Specialist
Sohag, Egypt - 9:35 pm local time
Whether you need to analyze video data, generate captions for images, or classify complex text patterns, I deliver clean, documented, and high-performance code.
My Core Services:
Computer Vision: Developing models for action recognition, object detection, and image classification (CNNs, ResNet, Transformers).
Natural Language Processing: Building text classification systems (e.g., spam detection, sentiment analysis) and utilizing LSTM/RNN architectures.
Model Optimization: Fine-tuning pre-trained models and creating custom Datasets/DataLoaders in PyTorch.
Featured Projects:
Group Activity Recognition: Built a system to analyze and classify group dynamics in sports (e.g., Volleyball) using spatio-temporal modeling.
Image Captioning: Developed an encoder-decoder model (CNN + Transformer) to automatically generate descriptive captions for images.
Fraud & Spam Detection: Created high-accuracy classification models for detecting anomalies in financial data and text messages.
I am passionate about turning complex data into actionable solutions. If you are looking for a dedicated engineer to bring your AI project to life, let’s connect.
Steps for completing your project
After purchasing the project, send requirements so Boules can start the project.
Delivery time starts when Boules receives requirements from you.
Boules works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis
I review your project goals, use case, and specific questions you want the model to answer. We define the success metrics (e.g., accuracy target).
Data Review & Preparation
I analyze your provided images and Q&A pairs. I perform data cleaning, resizing, and augmentation to ensure the dataset is ready for training.