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---
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: [email protected] . |