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
If you want to book a demo, please contact us: enot@enot.ai .
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no library tag.