You will get Custom Image Classification with Deep CNNs
Project details
I'll build, train, and fine-tune a custom Convolutional Neural Network (CNN) for your image classification task using PyTorch or TensorFlow. The package covers data preprocessing, augmentation, transfer learning with architectures like ResNet, EfficientNet, or MobileNet, model evaluation (accuracy, F1, confusion matrix), and a clean, reusable codebase. Ideal for product categorization, medical imaging, quality inspection, wildlife/species ID, and similar use cases.
Machine Learning Tools
NumPy, pandas, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$80
|
Standard
$180
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 6 days | 10 days | 21 days |
Number of Revisions | 1 | 3 | Unlimited |
Number of Model Variations | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $80
Additional Model Variation
(+ 2 Days)
+$80About Tabish
AI Engineer
Faisalabad, Pakistan - 3:01 am local time
"Hi, I'm Tabish , a data scientist passionate about uncovering insights and driving decisions with data. I specialize in machine learning, data visualization, and statistical analysis to help organizations make data-driven decisions. Let's connect and explore how data can drive your business
Steps for completing your project
After purchasing the project, send requirements so Tabish can start the project.
Delivery time starts when Tabish receives requirements from you.
Tabish works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Data Collection
I will review your project requirements, analyze your dataset structure, and verify image quality/labeling to ensure we are aligned before starting any development
Core Model Development
I will set up the CNN architecture in PyTorch/TensorFlow, implementing transfer learning with state-of-the-art models (like ResNet or EfficientNet) tailored to your specific task.