Modified ResNet-101 Architecture

Updated 19 November 2025

The paper demonstrates advanced residual block redesigns that improve gradient flow, enhance multi-scale representation, and boost computational efficiency.
It details modifications such as dynamic sub-block execution, dilated convolutions, and width-depth trade-offs that yield measurable performance gains on benchmarks.
The architecture variations underscore diverse strategies including privacy-preserving methods and normalization-free training to ensure robust and scalable deep learning.

A modified ResNet-101 architecture refers to any ResNet-101 variant in which key components—such as the residual block structure, normalization, receptive field, shortcut topology, head design, or dynamic execution—are re-engineered to address limitations of the standard design. These modifications target objectives such as improved accuracy, computational efficiency, deeper gradient flow, multi-scale representation, privacy-preserving training, and adaptation to varying memory or data constraints. Because ResNet-101 serves as a backbone in numerous tasks, modifications are common in both fundamental network research and diverse application domains.

1. Residual Block Alterations and Advanced Information Flow

Several works propose architectural innovations within the core residual block or its inter-block connectivity. "Improved Residual Networks for Image and Video Recognition" introduces a refined stage-wise structure for ResNet-101, where each main stage (conv2_x, conv3_x, conv4_x, conv5_x) is segmented into Start, Middle, and End blocks. These blocks optimize normalization and activation placement to preserve the scale and variance of feature representations. The Start block includes full normalization and non-linearity, Middle blocks reduce redundant normalization, and End blocks conclude with an additional normalization step followed by ReLU. Downsampling and channel increase use an enhanced projection shortcut: a spatial max-pooling precedes a 1×1 convolution, and the shortcut branch applies batch normalization to maintain alignment with the main residual branch. This organization improves gradient flow and stability at extreme depths; networks exceeding 1000 layers are trainable with these refinements (Duta et al., 2020).

Other variants, such as ResNetX-101, introduce "fold-depth" as a new structural hyperparameter. Rather than the canonical skip from $x_{l-1}$ , the shortcut connects $x_{l-i}$ , where $i$ cycles with the user-defined fold depth $t$ , effectively increasing the network's degree of shortcut disorder and boosting the proportion of short paths in the ensemble. This approach enhances the diversity of receptive field combinations and gradient propagation paths, measurable via path-length distributions and trophic incoherence metrics (Feng et al., 2019). The resulting graph is topologically more complex, yielding empirically better accuracy at constant parameter count and computational footprint on small benchmarks.

2. Multi-Scale, Dilated, and Dynamic Convolutional Enhancements

Expanding the receptive field and capturing granular multi-scale information are central to several modified ResNet-101 designs. In "Explainable AI: Comparative Analysis of Normal and Dilated ResNet Models for Fundus Disease Classification," only the bottleneck blocks in conv5_x are altered: their 1×1, 3×3, and 1×1 convolutions are replaced with dilated versions at an explicit rate $d=3$ . Dilation increases the effective receptive field of the key 3×3 layer from 3 to 7 pixels per block, with three blocks in total raising the conv5_x RF contribution from 6 to 18 pixels. All parameter counts and FLOPs remain identical, but spatial context in final feature maps is substantially increased. Such modifications yield a marked increase in mean F1 score and accuracy on challenging, multi-class medical image classification tasks (Karthikayan et al., 7 Jul 2024).

"Res2Net: A New Multi-scale Backbone Architecture" replaces every bottleneck block in ResNet-101 with the Res2Net block. Each bottleneck receives an additional 'scale' dimension: feature maps are split into $s$ smaller groups, which undergo hierarchical 3×3 convolutions that propagate information through progressively more layers per group. The result is that individual splits within a block span receptive fields of size {1×1, 3×3, 5×5, ..., (2s-1)×(2s-1)} in a single pass, substantially improving per-block feature diversity. This translation yields Res2Net-101, maintaining comparable complexity (≈8.0 GFLOPs, 50.0M parameters) to the standard backbone but reducing ImageNet top-1 error by 1.8 percentage points and further improving COCO object detection and salient object segmentation (Gao et al., 2019).

Dynamic inference is addressed in the "Dynamic Multi-path Neural Network" (DMNN-101). Here, each residual block is subdivided into $N$ sub-blocks (typically $N=2$ ), each with its own bottleneck structure. At inference, a lightweight controller determines for each sample whether to execute or bypass each sub-block based on the image features and category embedding. The gating mechanism is optimized via Gumbel-Softmax, and the expected execution rate and FLOPs are directly regularized in the loss. DMNN-101 achieves 45.1% FLOPs reduction relative to standard ResNet-101 with matching or superior accuracy on ImageNet (Su et al., 2019).

3. Width-Depth Trade-offs and Network Scaling

"Wider or Deeper: Revisiting the ResNet Model for Visual Recognition" demonstrates that wider, shallower variants of ResNet-101 can surpass much deeper models in both accuracy and efficiency. This formulation increases the bottleneck width by a multiplier ( $w=1.5$ in the canonical example), while reducing the number of residual blocks in the deepest stage (from 23 to 9). The architecture thus preserves the advantage of wide channels and avoids diminishing gradient signals inherent in excessive depth. Parameter and FLOP counts are matched or reduced, while top-1 ImageNet accuracy improves by nearly 3 percentage points compared to the standard backbone (Wu et al., 2016). This analysis is grounded in the observed "effective depth" beyond which gradients vanish and layers do not contribute to global optimization.

4. Head Modifications and Global Pooling Alternatives

While most ResNet-101 modifications preserve the global average pooling (GAP) before the final classification layer, the Wise-SrNet architecture replaces the GAP with a spatially-aware, learnable head. Following the final conv5_x output ( $7 \times 7 \times 2048$ ), a $2\times2$ average pooling is followed by a per-channel depthwise convolution with non-negative constraint (kernel $3\times3$ per channel), producing a $1\times1\times 2048$ feature. After flattening and an optional dropout, the standard dense classification layer is applied. This minor increase in parameters and FLOPs (e.g., $+$ 20K params vs 2M in the dense head) yields a reported Top-1 accuracy gain of $+5$ to $8$ percentage points on several datasets, particularly at higher resolutions and when the number of classes is large (Rahimzadeh et al., 2021).

5. Normalization, Initialization, and Training Stability

Batch normalization is fundamental to deep ResNet-101 optimization, but recent work targets normalization-free variants. In "A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization," the canonical residual sum $x_\ell = x_{\ell-1} + F(x_{\ell-1})$ is modified to $x_\ell = c(h(x_{\ell-1}) + f_\ell(x_{\ell-1}))$ , with $c = \sqrt{1/2}$ and $h$ taken as identity, scalar, or 1×1-conv. He-backward initialization is used for all convolutions. This scheme preserves both forward and backward activation/gradient variance and eliminates the need for per-batch statistics; empirical ImageNet accuracy for ResNet-101 drops < $0.3\%$ compared to batch-normed baselines (Civitelli et al., 2021). The method scales robustly to 200+ layers.

6. Privacy-Preserving and Lightweight Variants

For domains such as medical imaging, modifications focus on hardware and privacy constraints. In "Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture," a lightweight, 9-layer ResNet-9 ("DPResNet") replaces batch normalization with group normalization and removes max-pooling, using only convolutional striding for downsampling. The model is explicitly designed for efficient federated learning with DP-SGD (gradient clipping norm 7, $\epsilon=6.0$ , $\delta=1.9\times10^{-4}$ ) and secure aggregation via SMPC. This architecture is 10–20× lighter than ResNet-101, enables privacy guarantees, and achieves accuracy near non-private models on BloodMNIST (Fares et al., 1 Dec 2024).

7. Comparative Table of Representative Modified ResNet-101 Variants

Name/Reference	Key Modification	Parameters (M)	FLOPs (G)	ImageNet Top-1 (%)
Standard ResNet-101	Vanilla bottleneck stack	44.5	7.67	77.4 (Civitelli et al., 2021)
iResNet-101 (Duta et al., 2020)	Block redesign, advanced shortcut	≈44.5	≈7.8	+2% (vs baseline)
DMNN-101 (Su et al., 2019)	Dynamic sub-block execution	43.12	4.21	≥77.4
Res2Net-101 (Gao et al., 2019)	Per-block multi-scale splits	50.0	8.0	79.2
Wider ResNet-101 (Wu et al., 2016)	1.5× width, fewer deep blocks	29.0	≈7.8	80.8
Dilated ResNet-101 (Karthikayan et al., 7 Jul 2024)	Dilated conv5_x only	44.5	7.6	+14pp acc., F1=0.67
Wise-SrNet-101 (Rahimzadeh et al., 2021)	Learnable spatial head	44.62	7.8+0.04	+5–8pp Top-1 (R50)
Norm-Free ResNet-101 (Civitelli et al., 2021)	No BN, robust scaling	44.5	7.8	77.1

All parameter and FLOPs counts as reported in the sources for 224×224 input.

8. Implications and Cross-Variant Analysis

Modified ResNet-101 architectures are purpose-driven, with objectives ranging from fine-grained multi-scale processing (Res2Net), dynamic execution (DMNN-101), improved gradient propagation (iResNet-101, ResNetX), or domain-specific requirements (DPResNet, Wise-SrNet, dilated ResNet-101). There is no single superior variant; rather, design selection is dictated by task, hardware, and learning constraints. Tradeoffs abound: e.g., DMNN-101 reduces compute by nearly half at equal accuracy, Wise-SrNet enhances performance on small and high-resolution datasets with almost no computational penalty, and robust initialization enables deep training without normalization. Effective depth, receptive field, and the topology of skip connections are recurrent axes of improvement.

Collectively, these variants highlight the adaptability of the ResNet-101 backbone, the importance of architectural diversity, and the need for domain-consistent evaluation. The convergence of multi-scale, dynamic, and normalization-free approaches points to increasingly modular, context-aware architectures for future vision systems (Su et al., 2019, Duta et al., 2020, Wu et al., 2016, Gao et al., 2019, Civitelli et al., 2021, Rahimzadeh et al., 2021, Karthikayan et al., 7 Jul 2024, Fares et al., 1 Dec 2024).