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---
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license: apache-2.0
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datasets:
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- coco
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pipeline_tag: image-segmentation
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tags:
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- computer-vision
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- image-segmentation
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- ENOT-AutoDL
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---
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# ENOT-AutoDL pruning benchmark on MS-COCO
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This repository contains models accelerated with [ENOT-AutoDL](https://pypi.org/project/enot-autodl/) framework.
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Models from [Torchvision](https://pytorch.org/vision/stable/models.html) are used as a baseline.
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Evaluation code is also based on Torchvision references.
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## DeeplabV3_MobileNetV3_Large
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| Model | Latency (MMACs) | mean IoU (%) |
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|---------------------------------------------|:---------------:|:------------:|
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| **DeeplabV3_MobileNetV3_Large Torchvision** | 8872.87 | 47.0 |
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| **DeeplabV3_MobileNetV3_Large ENOT (x2)** | 4436.41 (x2.0) | 47.6 (+0.6) |
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| **DeeplabV3_MobileNetV3_Large ENOT (x4)** | 2217.53 (x4.0) | 46.4 (-0.6) |
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# Validation
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To validate results, follow this steps:
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1. Install all required packages:
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```bash
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pip install -r requrements.txt
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```
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1. Calculate model latency:
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```bash
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python measure_mac.py --model-path path/to/model.pth
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```
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1. Measure mean IoU of PyTorch (.pth) model:
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```bash
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python test.py --data-path path/to/coco --model-path path/to/model.pth
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```
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If you want to book a demo, please contact us: [email protected] . |
