π Just completed a full End-to-End Image Classification project on Intel's Natural Scenes Dataset!
As a Data Scientist & ML Engineer, I built a complete pipeline that automatically identifies 6 types of natural scenes β Buildings, Forest, Glacier, Mountain, Sea & Street β from raw images.
π What I did:
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Exploratory Data Analysis (EDA) β class distributions, brightness/contrast stats, color histograms
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Data Augmentation β rotation, zoom, flip, brightness tuning to prevent overfitting
β
Built & compared 4 Deep Learning models:
πΉ Simple CNN (Baseline)
πΉ Deep Custom CNN with BatchNorm Dropout
πΉ VGG16 Transfer Learning (freeze fine-tune)
πΉ MobileNetV2 Transfer Learning
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Grad-CAM visualizations β to explain WHAT the model actually sees
β
Confusion Matrix, Classification Report & Per-Class Accuracy
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Final predictions exported as CSV with confidence scores
π Dataset: ~25,000 images | 6 classes | 150Γ150 px
π Best Model Accuracy: 94%
π‘ Key Takeaway:
Transfer Learning is a game-changer. MobileNetV2 & VGG16 significantly outperformed custom CNNs β and Grad-CAM made the model explainable to non-technical stakeholders.
π If your business needs:
β Image classification or object detection solutions
β Computer Vision pipelines for automation
β Explainable AI for stakeholder reporting
Let's connect and talk! π© DM me or drop a comment below.
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