---
license: apache-2.0
datasets:
- coco
pipeline_tag: image-segmentation
tags:
- computer-vision
- image-segmentation
- ENOT-AutoDL
---

# ENOT-AutoDL pruning benchmark on MS-COCO

This repository contains models accelerated with [ENOT-AutoDL](https://pypi.org/project/enot-autodl/) framework.
Models from [Torchvision](https://pytorch.org/vision/stable/models.html) 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:

1. Install all required packages:
   ```bash
   pip install -r requrements.txt
   ```
1. Calculate model latency:
   ```bash
   python measure_mac.py --model-path path/to/model.pth
   ```
1. Measure mean IoU of PyTorch (.pth) model:
   ```bash
   python test.py --data-path path/to/coco --model-path path/to/model.pth
   ```

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