DCCI

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Content-Aware Adaptive Device-Cloud Collaborative Inference for Object Detection

DCCI 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# DCCI
Content-Aware Adaptive Device-Cloud Collaborative Inference for Object Detection

## Dependencies
There are some dependencies for running this
1. python3
2. pytorch
3. torchvision
4. zeromq
5. fvcore

## How to compress large models
1. Load initialization weights for basic training
```azure
python train.py --cfg cfg/yolov4.cfg --data data/voc.data --weights weights/yolov4.weights --epochs 100 --batch-size 16
```
2. Based on the optimal weights trained in step 1, perform sparse training
```azure
python train.py --cfg cfg/yolov4.cfg --data data/voc.data --weights weights/best.pt --epochs 299 --batch-size 64 -sr --s 0.0001 --prune 1
```
3. Channel-level pruning using sparsely trained models
```azure
python channel_pruning.py --cfg cfg/yolov4.cfg --data data/voc.data --weights weights/best.pt --global_percent 0.9 --layer_keep 0.01
```

4. Layer-level pruning using channel-pruned models
```azure
python layer_prune.py --cfg cfg/light_weight_channel.cfg --data data/voc.data --weights weights/light_weight_channel.weight --rl_epochs 80 --batch-size 32
```
5. Fine-tune recovery accuracy
```azure
python train.py --cfg cfg/light_weight_layer.cfg --data data/voc.data --weights weights/light_weight_layer.weights --epochs 100 --batch-size 32
```

## Run end-to-end
We give an example of an end-to-end test under examples. The end_to_end_test file is used to test the end-to-end accuracy. The partition_datasets file is used to partition datasets into hard and simple cases. detect_client is deployed to IoT devices and detect_server is deployed to the cloud.

## The weight model can be obtained here
link：https://pan.baidu.com/s/19gVlej3ZkRVxn-n6uVMpPQ

password：lar5

DCCI

DCCI 최신버젼 다운로드

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DCCI 최신버젼 다운로드

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