You will get Custom Machine Learning Models for Image Processing in Python
Project details
Need help with a machine learning project? I’ll build clean, effective ML/DL models tailored to your dataset and task.
I specialize in vision and prediction systems using Python, PyTorch, and TensorFlow. Whether it’s image classification, object detection, regression, or custom pipelines, I focus on clarity, speed, and performance.
You'll receive:
– Well-documented code
– Trained model with test results
– Easy-to-follow workflow
– Fast, reliable delivery
Tools used: PyTorch, TensorFlow, scikit-learn, OpenCV, Flask/Django (if deployed)
Whether you're a startup validating an idea, a researcher in need of automation, or a team needing API-ready ML logic, I’ve got you covered.
Let’s turn your data into results.
I specialize in vision and prediction systems using Python, PyTorch, and TensorFlow. Whether it’s image classification, object detection, regression, or custom pipelines, I focus on clarity, speed, and performance.
You'll receive:
– Well-documented code
– Trained model with test results
– Easy-to-follow workflow
– Fast, reliable delivery
Tools used: PyTorch, TensorFlow, scikit-learn, OpenCV, Flask/Django (if deployed)
Whether you're a startup validating an idea, a researcher in need of automation, or a team needing API-ready ML logic, I’ve got you covered.
Let’s turn your data into results.
Machine Learning Tools
Azure Machine Learning, BERT, ChatGPT, GitHub Copilot, Google Sheets, GPT-3, Keras, Microsoft Excel, NumPy, NVIDIA AI Platform, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, Tableau, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$180
|
Standard
$300
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 4 days | 14 days | 21 days |
Number of Revisions | 3 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 1 | 3 |
Number of Graphs/Charts | 3 | 5 | 9 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$40 - $50
Additional Revision
+$40
Additional Model Variation
(+ 3 Days)
+$45
Additional Scenario
(+ 3 Days)
+$40
Additional Graph/Chart
(+ 1 Day)
+$15
Model Documentation
(+ 1 Day)
+$30
Data Source Connectivity
(+ 1 Day)
+$35
Cloud Deployment
(+ 2 Days)
+$25
Performance Monitoring
(+ 6 Days)
+$35Frequently asked questions
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SV
Shalin V.
Dec 3, 2025
Implementation of Open Source GitHub Project in Google Colab
MA
Mustafa A.
Oct 29, 2025
Research Assistant Needed for Machine Learning with PyTorch
About Abhijeet
Computer Vision Engineer: Image Processing, Deep Learning & 3D Vision
Kathmandu, Nepal - 11:31 am local time
I'm a Computer Vision Engineer with a deep focus on image processing, object detection, tracking, pose estimation, and 3D vision. Deep Learning is my foundation where PyTorch is my tool. I don't do generalist AI work. Computer Vision is the only thing I do, and I do it end to end: dataset preparation, model fine-tuning, pipeline engineering, and clean documented delivery.
Here is what I have actually built:
- Image Processing: a compilation based project on on all the concepts in Image processing ranging from simple Image Augmentation and Manipulation to Image Classification, Segmentation, and Generation using Neural Networks: Classifiers (LeNet), UNet and GAN along with the implementation of core compression algorithms like JPEG and MPEG
- Open-Vocabulary 3D Scene Editing: a language-aligned Gaussian Splatting framework based 3D Scene Reconstruction where you type a prompt and the system segments, selects, and edits objects inside a 3D scene in real time with multi-view consistency. Built on 3DGS with CLIP and SAM integration primarily focusing on 3D reconstruction and Open-Vocabulary Scene Editing.
- Padelytics: a full racket sport match analysis system built on fine-tuned YOLO11 for player, racket and ball detection, MediaPipe for per-player pose estimation, and automatic rally, shot classification and event detection. Originally built for padel and directly transferable to tennis, pickleball, squash, and badminton. Raw match footage goes in, structured analytics come out within minutes.
- IP102 Pest Classification: 102-class agricultural pest detection using CNN and Vision Transformer architectures. Hit a performance ceiling at 76% and documented exactly why, because understanding failure is more valuable than hiding it.
My core stack: YOLO11, YOLOv8, MediaPipe, OpenPose, OpenCV, PyTorch, Gaussian Splatting, CLIP, SAM, ByteTrack.
What I deliver: I deliver production-ready Computer Vision pipelines which includes detection models, tracking systems, pose extractors, keypoint detection, image processing, instance segmentation, and VLM-integrated solutions with full documentation.
I work well on sports video analytics, real-time inference systems, surveillance and object tracking, retail and warehouse CV pipelines, action recognition, and 3D reconstruction from images or video.
Based in Nepal, available across US and European time zones, 0-4 hour response time.
If you have a video or vision problem and need it solved properly, send me the details and I'll tell you within a few hours exactly how I'd approach it.
Steps for completing your project
After purchasing the project, send requirements so Abhijeet can start the project.
Delivery time starts when Abhijeet receives requirements from you.
Abhijeet works on your project following the steps below.
Revisions may occur after the delivery date.
Milestone 1
Project Overview
Milestone 2
Project Outlines and Workflow