CNN-Based Object Classification
Overview
Convolutional Neural Network models that classify objects and digits into predefined categories and sub-categories. The trained models were further fine-tuned for additional object classes, producing strong cross-domain performance. Outcome: Reusable CNN classifiers that can be fine-tuned to new object classes with minimal data and effort.
Architecture & Pipeline
flowchart LR
n0["Image DatasetLabeled samples"]
n1["PreprocessingAugmentation · normalize"]
n2["CNN + DNNTensorFlow"]
n3["Fine-tune for New ClassesTransfer learning"]
n4["Predicted ClassHierarchical output"]
n0 --> n1
n1 --> n2
n2 --> n3
n3 --> n4
classDef step0 fill:#f1f5f9,stroke:#64748b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step1 fill:#ecfeff,stroke:#06b6d4,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step2 fill:#f0fdfa,stroke:#0d9488,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step3 fill:#ecfdf5,stroke:#10b981,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step4 fill:#fffbeb,stroke:#f59e0b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
class n0 step0;
class n1 step1;
class n2 step2;
class n3 step3;
class n4 step4;
End-to-end flow derived from this project's scope and tech stack. Tap View Fullscreen for a larger view, or scroll horizontally on small screens.
Key Features
- CNN and DNN architectures for hierarchical classification
- Fine-tuning workflow for new object categories
- Validated accuracy across predefined classes
- Tech Stack:** Python, TensorFlow