CustomCNN: Tailored Convolutional Architectures

• CustomCNN is a tailored convolutional architecture specifically designed for niche tasks, emphasizing efficient layer composition and parameter reduction.
• The training protocols employ optimizers like Adam, adaptive dropout, and rigorous regularization to achieve competitive performance on specialized datasets.
• CustomCNNs are optimized for resource-constrained environments, often outperforming generic models by leveraging domain-specific design adaptations and efficient deployment strategies.

A Custom Convolutional Neural Network (CustomCNN) denotes any convolutional architecture designed for a particular task, dataset, or resource constraint, as opposed to deep canonical backbones such as VGG, ResNet, or EfficientNet. CustomCNNs range from highly compact models for embedded inference to broad task-specific networks for image, speech, or even rule-constrained applications. The architecture, training protocol, and deployment strategies of CustomCNNs are determined by the target domain, scale, and desired efficiency. Custom design enables practitioners to balance accuracy, computational cost, and domain adaptation, often outperforming generic deep models in resource-constrained or highly specialized environments.

1. Architectural Principles and Layer Composition

CustomCNN architectures generally feature a stack of convolutional blocks followed by pooling, normalization, and fully connected classifiers. The base example from "Comparative Analysis of Custom CNN Architectures versus Pre-trained Models and Transfer Learning" (Tanvir et al., 7 Jan 2026) is a four-block structure:

• Each block consists of two 3×3 convolutions, BatchNorm, ReLU activation, max-pooling, and dropout.
• Channel progression: Block 1 (32), Block 2 (64), Block 3 (128), Block 4 (256).
• An adaptive average pooling layer reduces the final feature map to length 256.
• Classification head: three fully connected layers (256→512→256→num_classes) with dropout and softmax.

This structure comprises ≈3.40 million parameters and occupies ~13.6MB. Variants in the literature include residual connections, squeeze-and-excitation attention (SE), depthwise-separable convolutions, and progressive channel scaling (Avro et al., 4 Jan 2026, Tabassum et al., 8 Jan 2026, Hasan et al., 3 Jan 2026). Input sizes, convolution strides, activation types, and classifier head depth are adjusted per dataset. Mathematical formulations rigorously follow convolution:

$$(\mathbf{X} * \mathbf{W})_{i, j}^{(k)} = \sum_{c=1}^{C_{in}} \sum_{u,v} X_{i+u, j+v}^{(c)} W_{u,v}^{(c,k)} + b^{(k)}$$

and cross-entropy loss:

$\mathcal{L} = -\sum_{m=1}^M y_m \log(\hat y_m)$

Optimizers such as Adam (β₁=0.9, β₂=0.999, ε=1e-8) and Kaiming Normal initialization are standard (Tanvir et al., 7 Jan 2026, Avro et al., 4 Jan 2026).

2. Training Protocols and Hyperparameter Selection

CustomCNNs are typically trained from scratch using stochastic optimizers (Adam or SGD momentum). Common hyperparameters include:

• Batch size: 32–64
• Learning rate: 1e-3 (Adam), 1e-4 to 1e-3 (SGD with/without decay)
• Dropout rates: increasing across layers (0.10 to 0.50), placed between convolutional and fully connected layers
• Number of epochs: 10–50, with convergence often by the 10th epoch in basic tasks (Tanvir et al., 7 Jan 2026)

Regularization leverages dropout and batch normalization; data augmentation is variable but often omitted for simplicity (e.g., no augmentation in (Tanvir et al., 7 Jan 2026), class-dependent augmentation in (Tabassum et al., 8 Jan 2026)). Validation uses accuracy, F1-score, and standard metrics:

$$\mathrm{F_1} = 2 \cdot \frac{\mathrm{Precision} \cdot \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}$$

3. Performance Benchmarks and Dataset Generalization

CustomCNNs exhibit efficient training and competitive results on binary or coarse tasks with moderate training samples. The detailed per-dataset outcomes in (Tanvir et al., 7 Jan 2026) are:

Dataset	Val Acc.	Test Acc.	F1-Score	Training Time (s)
Footpath Vision	81.08%	78.16%	0.785	1,107.6
Auto Rickshaw	67.84%	–	–	1,062.1
Mango Image BD	89.81%	90.04%	–	–
Paddy Variety BD	54.04%	52.89%	–	–
Road Damage BD	92.54%	91.18%	0.888	433.9

CustomCNNs outperform deep models trained from scratch on small, imbalanced datasets—primarily due to less overfitting and efficient regularization (Tabassum et al., 8 Jan 2026). However, they underperform compared to transfer learning with fine-tuning on complex or fine-grained datasets, where ResNet-18 or VGG-16 yield absolute accuracy improvements (e.g., Paddy Variety: 52.9% → 93.1%) (Tanvir et al., 7 Jan 2026, Hasan et al., 3 Jan 2026).

4. Efficiency, Model Size, and Hardware Adaptation

CustomCNNs are preferred in scenarios where memory, latency, and deployment constraints dominate. Model sizes range from 0.6MB to 13.6MB, with parameter counts as low as 14,862 for highly optimized designs (Isong, 26 Jan 2025), versus 134M+ for deep canonical backbones (Khan et al., 7 Jan 2026, Akhand, 5 Jan 2026). Lightweight designs are explicitly targeted for embedded or IoT deployment, where quantization and further pruning can reduce computational cost with minimal accuracy loss (Tabassum et al., 8 Jan 2026, Jahanshahi, 2019). FPGA deployments leverage resource-aware design, fixed-point arithmetic, and hardware generation backends (Jahanshahi, 2019).

5. Variants and Domain-Specific Customizations

CustomCNN architectures have been adapted beyond image classification, supporting:

• Speech-based keyword detection with 1D multi-width convolutions and separate softmax heads for structured output (Salehinejad et al., 2017)
• Hybrid attention, residual blocks, and channel scaling for domain adaptation in agriculture and smart city vision (Avro et al., 4 Jan 2026)
• Rule-guided or physics-guided layers to inject expert knowledge, enhancing robustness in data-scarce regimes and improving interpretability (Gupta et al., 2024)
• Modular and ensemble-in-convolutional design where micro-CNN kernels replace linear filters for richer patchwise representations (Huang, 2018)

Ablation studies systematically emphasize the importance of layer depth, filter width, dropout configuration, and attention mechanisms for maximum generalization, notably in handwriting recognition tasks (Mamun et al., 2024).

6. Comparative Analysis: Transfer Learning and Canonical Backbones

Empirical studies show that while CustomCNNs are efficient and adequate for straightforward recognition tasks, transfer learning with ResNet-18, VGG-16, MobileNet, or EfficientNet yields superior accuracy, especially on complex and fine-grained datasets. The main trade-offs are:

• CustomCNN: minimal parameters (3.4M), fastest convergence on easy tasks, limited by lack of pre-learned features, competitive only when training data is sufficient for the target domain (Tanvir et al., 7 Jan 2026, Tabassum et al., 8 Jan 2026).
• Pre-trained (feature extraction): lowest trainable weights, intermediate accuracy, ideal for quick deployment, but ~5–15% lower accuracy than fine-tuning.
• Transfer Learning (fine-tuning): large weights (11–134M), best accuracy, slowest convergence, highest resource overhead (Tanvir et al., 7 Jan 2026, Akhand, 5 Jan 2026, Khan et al., 7 Jan 2026).

Design recommendations consistently emphasize matching model depth to task complexity and leveraging transfer learning when benchmarking in accuracy-critical applications (Hasan et al., 3 Jan 2026, Avro et al., 4 Jan 2026).

7. Practical Guidelines for Implementation and Deployment

• Select model depth commensurate with task complexity: e.g., 10–20 layers for binary; 50+ layers and attention blocks for fine-grained multiclass (Hasan et al., 3 Jan 2026, Tabassum et al., 8 Jan 2026).
• Apply data augmentation rigorously in low-data regimes; use batch normalization and dropout for regularization (Tabassum et al., 8 Jan 2026).
• Quantize and prune post-training for embedded deployment; freeze batchnorm for inference-only paths (Tabassum et al., 8 Jan 2026).
• Monitor per-class metrics to detect minority-class underfit; adjust class weights and loss formulations as necessary (Hasan et al., 3 Jan 2026).
• Use domain-specific knowledge through custom layers or external rules if interpretability and real-world constraints must be addressed (Gupta et al., 2024).

By balancing architectural simplicity, parameter efficiency, and regularization, CustomCNNs remain a vital tool for data-driven modeling under resource and domain constraints. Their competitiveness depends critically on the problem's inherent complexity and the available training regime. Transfer learning remains dominant for top-line accuracy when resources permit, but custom designs satisfy deployment and efficiency criteria across a range of applied scientific and engineering fields.