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ENOT-AutoDL pruning benchmark on ImageNet-1k

This repository contains models accelerated with ENOT-AutoDL framework. Models from Torchvision are used as a baseline. Evaluation code is also based on Torchvision references.

ResNet-50

Model Latency (MMACs) Accuracy (%)
ResNet-50 Torchvision 4144.85 76.14
ResNet-50 ENOT (x2) 2057.61 (x2.01) 75.48 (-0.66)
ResNet-50 ENOT (x4) 867.94 (x4.77) 73.58 (-2.57)

ViT-B/32

Model Latency (MMACs) Accuracy (%)
ViT-B/32 Torchvision 4413.99 75.91
ViT-B/32 ENOT (x4.8) 911.80 (x4.84) 75.68 (-0.23)
ViT-B/32 ENOT (x9) 490.78 (x8.99) 73.72 (-2.19)

MobileNetV2

Model Latency (MMACs) Accuracy (%)
MobileNetV2 Torchvision 334.23 71.88
MobileNetV2 ENOT (x1.6) 209.24 (x1.6) 71.38 (-0.5)
MobileNetV2 ENOT (x2.1) 156.80 (x2.13) 69.90 (-1.98)

Validation

To validate results, follow this steps:

  1. Install all required packages:
    pip install -r requrements.txt
    
  2. Calculate model latency:
    python measure_mac.py --model-ckpt path/to/model.pth
    
  3. Measure accuracy of ONNX model:
    python test.py --data-path path/to/imagenet --model-onnx path/to/model.onnx --batch-size 1
    
  4. Measure accuracy of PyTorch (.pth) model:
    python test.py --data-path path/to/imagenet --model-ckpt path/to/model.pth
    

If you want to book a demo, please contact us: enot@enot.ai .

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Dataset used to train ENOT-AutoDL/imagenet-benchmark