| ---
|
| 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 . |
