/Backbones

Clash of Backbones

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Clash of Backbones

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Which Pytorch Backbone to Use for Low Data Fine-tuning?

Resource-efficient Image Classification

Criteria for choosing backbones for our experiments?

  • ImageNet-1k pre-trained weights available in torchvision or github (WaveMix)
  • Number of parameters less than 30 M
  • Only model with highest ImageNet performance from one architecture family

Backbones Compared

Architecture # Params (M) ImageNet-1k Top-1 Accuracy (%)
ResNet-50 25.6 76.13
WaveMix 27.9 75.32
ConvNeXt-Tiny 28.6 82.52
Swin-Tiny 28.3 81.47
SwinV2-Tiny 28.4 82.07
EfficientNetV2-S 21.5 84.23
DenseNet-161 28.7 77.14
MobileNetV3-Large 5.5 75.27
RegNetY-3.2GF 19.4 81.98
ResNeXt-50 32×4d 25.0 81.20
ShuffleNetV2 2.0× 7.4 76.23

Datasets used for benchmarking

Dataset Domain # Training Images # Testing Images # Classes
CIFAR-10 🖼️ Natural Images 50,000 10,000 10
CIFAR-100 🖼️ Natural Images 50,000 10,000 100
TinyImageNet 🖼️ Natural Images (ImageNet subset) 100,000 10,000 200
Stanford Dogs 🖼️ Natural Images (Dog breeds) 12,000 8,580 120
Flowers-102 🖼️ Natural Images (Flower species) 2,040 6,149 102
CUB-200-2011 🖼️ Natural Images (Bird species) 5,994 5,794 200
Stanford Cars 🖼️ Natural Images (Car models) 8,144 8,041 196
Food-101 🖼️ Natural Images (Food categories) 75,750 25,250 101
DTD 🎨 Texture Images 1,880 1,880 47
UCMerced Land Use 🛰️ Remote Sensing Images 1,680 420 21
EuroSAT 🛰️ Remote Sensing Images 18,900 8,100 10
PlantVillage 🌿 Plant Images 44,343 11,105 39
PlantCLEF 🌿 Plant Images 10,455 1,135 20
Galaxy10 DECals 🌌 Astronomy Images (Galaxy Morphology) 15,962 1,774 10
BreakHis 40× 🏥 Medical Images (Histopathology) 1,398 606 2
BreakHis 100× 🏥 Medical Images (Histopathology) 1,458 632 2
BreakHis 200× 🏥 Medical Images (Histopathology) 1,411 611 2
BreakHis 400× 🏥 Medical Images (Histopathology) 1,276 553 2
RSNA Pneumonia Detection 🏥 Medical Images (Radiology) 24,181 6046 2

Code to run benchmarking for each dataset

python <dataset.py> -model <backbone> -bs <batch-size>

If you want to create a train test split

python split.py --input_dir <input folder path> --output_dir <output folder path> --test_size <fraction to be split>

Cite this paper

@misc{jeevan2024backbone,
      title={Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision}, 
      author={Pranav Jeevan and Amit Sethi},
      year={2024},
      eprint={2406.05612},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}