Write-Up: Res2Net50-v1b-26w-4s Pre-trained Model The file "res2net50-v1b-26w-4s-3cf99910.pth" is a PyTorch checkpoint containing weights for a Res2Net50 architecture, a powerful multi-scale backbone often used for image classification, object detection, and semantic segmentation. 1. Architecture Breakdown The naming convention provides specific details about the model's configuration: Res2Net50 : A variation of the 50-layer ResNet architecture that replaces standard bottleneck blocks with Res2Net modules. v1b : This refers to a common modification of the ResNet "stem" where the initial convolution is replaced by three convolutions, and the downsampling stride is moved to the second layer to preserve more information. 26w (Width) : The base width of each filter group within the module, set to 26 channels. 4s (Scale) : The "scale dimension," indicating that the feature maps are split into 4 groups. These groups are processed in a hierarchical residual-like manner, allowing the model to capture multi-scale features at a granular level. 3cf99910 : A short hash (checksum) typically generated by PyTorch's model_zoo to ensure file integrity during download. 2. Key Technical Innovations The core of this model is the Res2Net module . Unlike standard ResNets that process features at a single scale within a block, Res2Net: Hierarchical Connections : Splits the input into several groups and processes them such that each subsequent group receives the output of the previous one. Increased Receptive Fields : This design increases the range of receptive fields for each layer without adding significant computational overhead. Granular Multi-Scale Representation : It captures both fine-grained details and global context more effectively than traditional ResNet-50 models. 3. Performance & Use Cases This specific variant (26w-4s) is designed to offer a superior balance between accuracy and computational cost. Res2Net: A New Multi-scale Backbone Architecture - arXiv
Technical Specification Sheet: res2net50-v1b-26w-4s-3cf99910.pth 1. Identity
Filename: res2net50-v1b-26w-4s-3cf99910.pth File Type: PyTorch model state dictionary ( .pth ) Hash Identifier: 3cf99910 (likely a Git hash or MD5 suffix for version tracking)
2. Architecture This file contains the pre-trained weights for a Res2Net model, specifically the Res2Net50_v1b_26w_4s variant. | Parameter | Value | |-----------|-------| | Base Architecture | ResNet50 (v1b) | | Scaling Strategy | Res2Net | | Width Factor | 26w (26 channels in residual blocks) | | Scale Factor | 4s (4 hierarchical scale branches per block) | | Depth | 50 layers | 3. Key Characteristics res2net50-v1b-26w-4s-3cf99910.pth
Multi-scale representation: Unlike standard ResNet, Res2Net replaces the 3×3 convolution filters with a set of smaller filter groups, increasing the receptive field diversity without additional computational cost. v1b: Indicates a ResNet v1b stem (7×7 conv, stride 2, followed by max pooling) with slight improvements over original v1. 26w-4s: The model uses 26 channels as the base width and splits feature maps into 4 scale groups.
4. Common Use Cases
Image classification (ImageNet pre-trained) Transfer learning backbone for: v1b : This refers to a common modification
Object detection (Faster R-CNN, YOLO) Semantic segmentation (DeepLab, U-Net) Fine-grained visual recognition
5. Origin This file is typically downloaded from:
Official Res2Net GitHub repository: Res2Net Pretrained Models Alternative sources: MMClassification (OpenMMLab), torchvision model hubs (if custom registered) These groups are processed in a hierarchical residual-like
6. Loading in PyTorch import torch import torchvision.models as models Load the state dict weights_path = "res2net50-v1b-26w-4s-3cf99910.pth" state_dict = torch.load(weights_path, map_location='cpu') Define model architecture (requires Res2Net implementation) Example using a custom Res2Net class or mmcls: from mmcls.models import build_classifier config = dict( type='ImageClassifier', backbone=dict( type='Res2Net', depth=50, base_width=26, scale=4, deep_stem=False, avg_down=False), neck=dict(type='GlobalAveragePooling'), head=dict(type='LinearClsHead', num_classes=1000)) model = build_classifier(config) model.load_state_dict(state_dict, strict=True)
7. Expected Performance (ImageNet Top-1) | Model | Top-1 Accuracy | |-------|----------------| | ResNet50 | 76.15% | | Res2Net50-26w-4s (v1b) | 78.0% - 78.5% | 8. File Integrity