iamabhayaditya/InceptionResNetV2-UNet-MBRSC

InceptionResNetV2-UNet: Dubai Satellite Semantic Segmentation & Edge Optimization

This repository hosts a production-grade, edge-optimized deep learning model designed for multi-class land-cover semantic segmentation from high-resolution satellite imagery (Dubai Satellite Imagery Dataset).

This work explores the accuracy vs. model footprint trade-offs for deploying aerial and satellite segmentation models to edge and on-orbit compute platforms.

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📌 Architecture & Model Overview

• Architecture: U-Net with an InceptionResNetV2 feature extraction encoder
• Total Parameters: 36,793,414 (~36.79M)
• Trainable Parameters: 36,752,390 (~36.75M)
• Non-trainable Parameters: 41,024 (~160 KB)
• Input Resolution: 512 × 512 × 3 (RGB, normalized to [0, 1])
• Output Resolution: 512 × 512 × 6 (6-class probability distribution)
• Quantization: Dynamic Range Post-Training Quantization (PTQ) via TensorFlow Lite / LiteRT

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⚙️ Training Specifications & Hyperparameters

• Dataset Split: 504 augmented training tiles, 64 validation tiles ($512 \times 512$)
• Batch Size: 4
• Steps per Epoch: 126
• Epochs Trained: 33 (converged via EarlyStopping)
• Optimizer: Adam
• Initial Learning Rate: 0.0001 with Exponential Decay Scheduling
• Loss Function: categorical_crossentropy
• Evaluation Metrics: dice_coef, categorical_accuracy
• Peak Validation Performance:
  • Validation Accuracy: 86.24%
  • Validation Dice Coefficient: 81.72% (Peak: 81.89%)

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📊 Quantization & Deployment Benchmarks

We evaluated the performance, storage, and latency trade-offs between standard floating-point precision (FP32) and dynamic 8-bit integer quantization (INT8):

Model Format	Precision	Disk Size	Size Reduction	Overall mIoU	5-Class Terrain mIoU	CPU Latency	GPU Latency	Throughput
Keras (.keras)	FP32	423.14 MB	Baseline (1.0x)	59.09%	70.34%	4,693.27 ms	638.39 ms	1.57 FPS (GPU)
TFLite (.tflite)	FP32	140.36 MB	~3.0x smaller	59.09%	70.34%	~4,200 ms	—	0.24 FPS (CPU)
TFLite INT8 (.tflite)	Dynamic INT8	35.89 MB	11.8x smaller (-91.5%)	51.08%	60.28%	15,911.85 ms	—	0.06 FPS (CPU)

Key Finding: Dynamic INT8 quantization slashes memory storage by 91.5% (saving 387.25 MB of storage), while retaining 60.28% 5-class terrain mIoU.

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🔍 Per-Class Evaluation Breakdown

Detailed analysis of class preservation between baseline FP32 and dynamic INT8:

Class	RGB Color	Baseline IoU (FP32)	INT8 IoU (Quantized)	Change (Delta IoU)	Status
🌊 Water	(226, 169, 41)	0.9015	0.8849	-0.0166 (-1.7%)	🟢 Preserved almost perfectly
🌿 Vegetation	(254, 221, 58)	0.6163	0.6190	+0.0027 (+0.3%)	🟢 Completely preserved
🏜️ Land	(132, 41, 246)	0.8083	0.7761	-0.0322 (-3.2%)	🟢 Very stable
🛣️ Road	(110, 193, 228)	0.5105	0.3327	-0.1778 (-17.8%)	🟡 Moderate drop
🏢 Building	(60, 16, 152)	0.6806	0.4012	-0.2794 (-27.9%)	🔴 Major drop
❓ Unlabeled	(155, 155, 155)	0.0282	0.0509	+0.0227	—
Overall mIoU	—	59.09%	51.08%	-8.01%
5-Class Terrain mIoU	—	70.34%	60.28%	-10.06%

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📊 Model Performance: Baseline (FP32) vs. Quantized (INT8)

Class	Baseline IoU (FP32)	INT8 IoU (PTQ)	Baseline Dice / F1	INT8 Dice / F1	Baseline Precision	INT8 Precision	Baseline Recall	INT8 Recall
🌊 Water	0.9015	0.8849	0.9482	0.9390	0.9429	0.9123	0.9536	0.9672
🏜️ Land	0.8083	0.7761	0.8940	0.8740	0.8733	0.8101	0.9157	0.9488
🏢 Building	0.6806	0.4012	0.8099	0.5727	0.7799	0.9023	0.8423	0.4195
🌿 Vegetation	0.6163	0.6190	0.7626	0.7647	0.8441	0.7270	0.6954	0.8065
🛣️ Road	0.5105	0.3327	0.6759	0.4993	0.6999	0.3820	0.6535	0.7203
❓ Unlabeled	0.0282	0.0509	0.0548	0.0968	0.3341	0.3701	0.0298	0.0557
Mean (Overall)	0.5909	0.5108	0.6909	0.6244	0.7457	0.6840	0.6817	0.6530
5-Class Terrain	0.7034	0.6028	0.8181	0.7299	0.8280	0.7467	0.8121	0.7725

Error Maps

📂 Repository File Manifest

• InceptionResNetV2-UNet.keras: Native Keras 3 format containing architecture, weights, and optimizer state.
• InceptionResNetV2-UNet.h5: Keras HDF5 format for backward compatibility with legacy pipelines.
• InceptionResNetV2-UNet.weights.h5: Checkpoint containing only trained layer weights.
• InceptionResNetV2-UNet_fp32.tflite: Standalone TensorFlow Lite FP32 model for mobile and edge runtime.
• InceptionResNetV2-UNet_int8.tflite: Dynamically quantized 8-bit model optimized for ultra-low memory footprints (35.89 MB).

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💻 Quickstart Inference Guide

1. Keras Full Precision Model (FP32)

import tensorflow as tf
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
    repo_id="iamabhayaditya/InceptionResNetV2-UNet-MBRSC",
    filename="InceptionResNetV2-UNet.keras"
)
model = tf.keras.models.load_model(model_path, compile=False)

2. Edge / CPU Inference with Quantized TFLite (INT8)

import numpy as np
import tensorflow as tf
from huggingface_hub import hf_hub_download
tflite_path = hf_hub_download(
    repo_id="iamabhayaditya/InceptionResNetV2-UNet-MBRSC",
    filename="InceptionResNetV2-UNet_int8.tflite"
)
interpreter = tf.lite.Interpreter(model_path=tflite_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Input image shape: (1, 512, 512, 3), float32 normalized to [0, 1]
interpreter.set_tensor(input_details[0]['index'], input_image)
interpreter.invoke()
# Output shape: (1, 512, 512, 6)
predictions = interpreter.get_tensor(output_details[0]['index'])
pred_mask = np.argmax(predictions[0], axis=-1)

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📜 Lineage & Attribution

This project is derived from the open-source implementation by Ayush Dabra licensed under GNU General Public License v3.0 (GPL-3.0). Modifications & Modernization:

• Manually documented and commented every pipeline component, training routine, and utility function to ensure strict reproducibility and clean code standards.
• Engineered diagnostic visualization routines—including error heatmaps, class activation visualizations, and confusion matrices—to isolate edge-case failures (e.g., small building boundaries vs. narrow asphalt roads).
• Achieved superior convergence, lifting validation performance to ~86.24% categorical accuracy and an ~81.72% Dice coefficient (peak: 81.89%).
• Upgraded deprecated dependencies to modern TensorFlow 2.x / Keras 3.
• Engineered dynamic Post-Training Quantization (PTQ) routines for TensorFlow Lite / LiteRT deployment.
• Established edge deployment benchmarking suites (measuring GPU vs. CPU latency, memory footprints, and per-class quantization degradation).