ENOT-AutoDL pruning benchmark on MS-COCO

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.

DeeplabV3_MobileNetV3_Large

Model	Latency (MMACs)	mean IoU (%)
DeeplabV3_MobileNetV3_Large Torchvision	8872.87	47.0
DeeplabV3_MobileNetV3_Large ENOT (x2)	4436.41 (x2.0)	47.6 (+0.6)
DeeplabV3_MobileNetV3_Large ENOT (x4)	2217.53 (x4.0)	46.4 (-0.6)

Validation

To validate results, follow this steps:

Install all required packages:
```
pip install -r requrements.txt
```

Calculate model latency:

python measure_mac.py --model-path path/to/model.pth

Measure mean IoU of PyTorch (.pth) model:

python test.py --data-path path/to/coco --model-path path/to/model.pth

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