# multi_prec_nas_object_detection

**Repository Path**: wheatrabbit/multi_prec_nas_object_detection

## Basic Information

- **Project Name**: multi_prec_nas_object_detection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-20
- **Last Updated**: 2025-04-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

**NEW RELEASE: we released our new, engineered and user-friendly DNAS library named [PLiNIO](https://github.com/eml-eda/plinio) which includes channel-wise precision assignement among the different implemented methods. We highly suggest to try this new release for your experiments!**


Copyright (C) 2022 Politecnico di Torino, Italy. SPDX-License-Identifier: Apache-2.0. See LICENSE file for details.

Authors: Matteo Risso, Alessio Burrello, Luca Benini, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari

# multi-prec-nas

## Reference
If you use our code in your experiments, please make sure to cite our paper:
```
@INPROCEEDINGS{9969373,
  author={Risso, Matteo and Burrello, Alessio and Benini, Luca and Macii, Enrico and Poncino, Massimo and Pagliari, Daniele Jahier},
  booktitle={2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC)}, 
  title={Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes}, 
  year={2022},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/IGSC55832.2022.9969373}}
```
## Datasets
The current version support the following datasets and tasks taken from the benchmark suite MLPerf Tiny:
- CIFAR10 - Image Classification.
- MSCOCO - Visual Wake Words.
- Google Speech Commands v2 - Keyword Spotting.
- ToyADMOS - Anomaly Detection

## How to run
### Image Classification
1. Visit the folder: `cd image_classification`.
2. Run the provided shell script `run_ic.sh`: 
```
source run_ic.sh <regularization_strenght> 0 resnet8_w248a248_multiprec search ft
```

### Visual Wake Words
1. Run the provided `Makefile` to download the desired dataset: `make vww-init`.
2. Visit the folder: `cd visual_wake_words`.
3. Run the provided shell script `run_vww.sh`: 
```
source run_vww.sh <regularization_strenght> 0 mobilenetv1_w248a248_multiprec search ft
```

### Keyword Spotting
1. Visit the folder: `cd keyword_spotting`.
2. Run the provided shell script `run_kws.sh`: 
```
source run_kws.sh <regularization_strenght> 0 dscnn_w248a248_multiprec search ft
```

### Anomaly Detection
1. Run the provided `Makefile` to download the desired dataset: `make andet-init`.
2. Visit the folder: `cd anomaly_detection`.
3. Run the provided shell script `run_andet.sh`: 
```
source run_andet.sh <regularization_strenght> 0 denseae_w248a248_multiprec search ft
```

### Object Detection
1. Visit the folder: `cd object_detection`.
2. Run the provided shell script `run_od.sh`:
```
source run_od.sh <regularization_strenght> 0
yolov5_w248a248_multiprec search ft

source run_od.sh 0.01 0 yolov5_w248a248_multiprec search ft
后台运行
nohup ./run_od.sh 0.01 0 yolov5_w248a248_multiprec search ft > output.log 2>&1 &
怀疑压缩是不是太狠了，调低一手cd
nohup ./run_od.sh 0.01 0 yolov5_w248a248_multiprec search ft > output.log 2>&1 &

nohup ./run_od.sh 0.1 0 yolov5_w248a248_multiprec search ft > output.log 2>&1 &
nohup ./run_od.sh 1.5 0 yolov5_w248a248_multiprec search ft > output.log 2>&1 &
nohup ./run_od.sh 2.0 0 yolov5_w248a248_multiprec search ft > output.log 2>&1 &
source run_od.sh 0.01 0 yolov5_w248a248_multiprec search ft
```

## License
This code is released under Apache 2.0, see the LICENSE file in the root of this repository for details.