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Impact of Hyperparameter Optimization on Lightweight Models for Real-Time Image Classification

“Why change the architecture when you can just tweak the knobs?” — Someone smart (probably you after reading this)

models-optimized accuracy-boom

Official repository for the paper:

📄 “Analysis of Hyperparameter Optimization Effects on Lightweight Deep Models for Real-Time Image Classification”
🧪 Accepted in Scientific Reports (Nature Portfolio) arXiv:2507.23315
🔬 By: Vineet Kumar Rakesh, Soumya Mazumdar, Tapas Samanta, Hemendra Kumar Pandey, and Amitabha Das
Corresponding Author — Variable Energy Cyclotron Centre (VECC), DAE, Govt. of India

🚀 What’s Inside

This repository contains all code and configuration files used in our Scientific Reports study:

  • ✅ Training & evaluation scripts for 7 lightweight CNN and transformer-based models
  • ✅ Comprehensive ablation studies on learning rate, batch size, optimizers, and augmentation
  • ✅ Real-time deployment metrics: latency, FPS, model size, GFLOPs
  • ✅ Subset-based reproducible benchmark (90k ImageNet-1K images)
  • ✅ PyTorch 2.5.1 + CUDA 12.6 with Automatic Mixed Precision (AMP)

🧠 Summary of Findings

Model Top-1 (%) Top-5 (%) Latency (ms) ↓ FPS ↑ Params (M) FLOPs (G)
ConvNeXt-Tiny 83.85 95.09 0.51 (B=32) 1964.99 28.57 4.46
EfficientNetV2-S 88.50 97.15 0.31 (B=32) 3226.66 21.31 2.85
MobileNetV3-Large 86.99 96.93 0.10 (B=32) 10034.10 4.18 0.21
MobileViT v2 (S) 87.82 97.19 0.40 (B=32) 2516.01 4.88 1.41
MobileViT v2 (XS) 87.36 96.80 0.33 (B=32) 3007.27 1.36 0.36
RepVGG-A2 88.45 97.16 0.26 (B=16) 3862.14 28.21 5.69
TinyViT-21M 90.94 97.74 0.59 (B=16) 1687.04 33.21 4.09

🧩 Hyperparameter tuning alone improved accuracy by 1.5–3.5% without modifying architectures.
🧠 Models like MobileNetV3-L and RepVGG-A2 achieved sub-5 ms latency and 9,000+ FPS on NVIDIA L40s GPUs.

📊 Key Insights

  • Learning rate is the single most sensitive hyperparameter — optimal range ≈ 0.01–0.1.
  • Composite augmentations (RandAugment + Mixup + CutMix + Label Smoothing) yielded the largest performance gains.
  • Cosine LR scheduling with 5-epoch warm-up ensured stable convergence.
  • Mixed precision training provided up to 40% faster training with negligible accuracy drop.
  • Subset-based ImageNet training (90k balanced samples) gave reproducible and scalable results.

🎨 Cumulative Augmentation Effects

Table: Top-1 Validation Accuracy (%) of Representative Models with Cumulative Augmentation Strategies
(Trained for 300 Epochs on ImageNet–1K Subset)

Model Baseline + RandAug + Mixup + CutMix + Label Smooth
ConvNeXt-Tiny 83.85 86.24 86.90 88.50 88.00
EfficientNetV2-S 88.50 91.34 92.72 92.63 92.56
MobileNetV3-Large 86.99 89.15 90.97 90.45 90.20
MobileViT v2 (S) 87.83 89.91 91.47 92.63 91.28
MobileViT v2 (XS) 87.36 88.88 90.56 90.18 90.31
RepVGG–A2 88.45 89.61 91.54 91.48 91.43
TinyViT–21M 90.94 92.11 93.30 93.35 93.84

📈 Composite augmentation consistently enhanced validation accuracy across all architectures,
with CutMix and Label Smoothing yielding the strongest late-epoch gains.

🧰 Installation

git clone https://github.com/VineetKumarRakesh/lcnn-opt.git
cd lcnn-opt
conda env create -f env.yml
conda activate lcnn-opt

🧪 Tested with PyTorch 2.5.1 + CUDA 12.6 on NVIDIA L40s (48 GB), Python 3.10.18.

🏋️ Train a Model

python train.py --model repvgg_a2 --config configs/repvgg.yaml --amp


* Use --amp for mixed precision training
* All training logs are automatically saved to /logs/

---

## 🧪 Evaluate a Model

```bash
python eval.py --checkpoint outputs/repvgg_best.pt --data-path /path/to/imagenet-val

Want to recreate the full ablation madness?

python scripts/ablation_study.py --config configs/convnext.yaml

📁 Project Structure (because we’re organized)

configs/     # YAML configurations for all models
data/        # Dataset loaders & preprocessing
models/      # Model wrappers
scripts/     # Ablation, profiling, and visualization tools
logs/        # Training logs
plots/       # Curves and figures
outputs/     # Saved checkpoints

📸 Results & Visualizations

📈 All results are saved in the outputs folder.

  • Plots: /plots
  • Training logs: /logs

💡 All graphs were created without harming any matplotlib instances.

🔐 Checkpoints Access Policy

We don’t ship checkpoints with the repo because, well, storage is expensive and email is free.

🧬 To request checkpoints, kindly:

Please include:

  • Your full name
  • Institutional affiliation
  • Your favorite optimizer (optional but highly encouraged)

🙏 Acknowledgements

  • 💻 NVIDIA L40s — the silent workhorse
  • 🔥 PyTorch, timm, and decord
  • ☕ Coffee — the original batch size booster

📜 Citation

If this repository saved you time, compute, or reviewer wrath, please consider citing:

@misc{rakesh2025impacthyperparameteroptimizationaccuracy,
      title={Impact of Hyperparameter Optimization on the Accuracy of Lightweight Deep Learning Models for Real-Time Image Classification}, 
      author={Vineet Kumar Rakesh and Soumya Mazumdar and Tapas Samanta and Sarbajit Pal and Amitabha Das},
      year={2025},
      eprint={2507.23315},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.23315}, 
}

🌐 External Links

🤓 Final Words

“Real-time is not just fast. It’s fast with purpose.”

Train smart, tune wisely, and may your Top-1 be ever rising. 🚀