You will get a Custom Object Detection & Segmentation Pipeline
Project details
You will get a custom object detection and segmentation model based on state-of-the-art deep learning architectures, designed specifically for your application. This pipeline can accurately identify, localize, and segment objects of interest in challenging environments. The model is trained on your annotated datasets and can handle variations in shape, size, lighting, and occlusions.
Applications include precision agricultural analysis, such as rice grain counting and classification, wildlife monitoring, such as focal fish tracking in underwater environments, and medical imaging tasks, like detecting tooth cavities. The solution comes with a ready-to-deploy pipeline for inference, along with options for model fine-tuning, custom metrics, and integration into your workflow for automated detection and analysis.
Applications include precision agricultural analysis, such as rice grain counting and classification, wildlife monitoring, such as focal fish tracking in underwater environments, and medical imaging tasks, like detecting tooth cavities. The solution comes with a ready-to-deploy pipeline for inference, along with options for model fine-tuning, custom metrics, and integration into your workflow for automated detection and analysis.
Machine Learning Tools
Apache Spark, Caffe, ChatGPT, deeplearn.js, Deeplearning4j, GitHub Copilot, Google Data Studio, Keras, Kubeflow, MATLAB, MLflow, NumPy, NVIDIA AI Platform, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPy, TensorFlow, Tesseract OCR, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$450
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 4 | 6 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 2 | 4 | 8 |
Model Validation/Testing | |||
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $200
Additional Revision
+$100
Additional Model Variation
+$200
Additional Scenario
+$100
Additional Graph/Chart
+$50
Model Documentation
+$150
Data Source Connectivity
+$100
Source Code
+$100About Nabeel
AI Engineer
Rawalpindi, Pakistan - 6:03 pm local time
I specialize in:
Image Classification, Segmentation, and Object Detection using YOLOv9, Vision Transformers, and SAM
Physics-Informed Deep Learning and Hybrid CNN–Transformer Architectures
GPU-Accelerated Model Optimization using CUDA, OpenACC, TensorRT, and ONNX
Parallel & Distributed Training on multi-GPU clusters with PyTorch Distributed, Horovod, and MPI
Anomaly and Fault Detection Systems with signal, time-series, and multi-sensor data fusion
AI Application Deployment via Streamlit, FastAPI, and Dockerized MLOps pipelines
Recent Project Highlights
🚀 Vision Mamba U-Net for Composite Defect Segmentation — achieved 99% mIoU using physics-guided thermal imaging principles.
🌾 Hybrid Rice Grain Classification Pipeline — CUDA-accelerated model delivering 92% accuracy with 40% faster inference.
🐠 Underwater Fish Tracking using SAM2 + Kalman Filtering — reached 97% tracking accuracy on complex marine datasets.
🧠 Parallel Vision Transformer for Real-Time Detection — implemented PyTorch + MPI + CUDA, improving throughput by 25%.
☕ Biotic Stress Classification in Crops — developed a Transformer-based classifier achieving 88% accuracy for agricultural health monitoring.
I combine research-grade innovation with production-level engineering to deliver optimized, interpretable, and deployable AI solutions. My workflow ensures every model is efficient, explainable, and ready for real-world deployment.
Steps for completing your project
After purchasing the project, send requirements so Nabeel can start the project.
Delivery time starts when Nabeel receives requirements from you.
Nabeel works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering
Client purchases the project and provides detailed requirements, including dataset, target objects, preferred output format, and any specific constraints or use cases.
Data Preparation & Analysis
Perform data cleaning, augmentation, and preprocessing to ensure high-quality inputs for model training.