# CondenseNetV2

**Repository Path**: andyjjz/CondenseNetV2

## Basic Information

- **Project Name**: CondenseNetV2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-04-13
- **Last Updated**: 2021-11-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CondenseNetV2

This repository is the official Pytorch implementation for "[CondenseNet V2: Sparse Feature Reactivation for Deep Networks](https://arxiv.org/abs/1711.09224)" paper by Le Yang\*, Haojun Jiang\*, Ruojin Cai, Yulin Wang, Shiji Song, [Gao Huang](http://www.gaohuang.net/) and Qi Tian (* Authors contributed equally).

## Contents

1. [Introduction](#introduction)
2. [Usage](#usage)
3. [Results](#results)
4. [Contacts](#contacts)

## Introduction

Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet has shown that this mechanism can be further improved if redundant features are removed. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. In the proposed network, named **CondenseNetV2**, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers; and 2) actively update a set of preceding features to increase their utility for later layers. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.

## Usage

### Dependencies

- [Python3](https://www.python.org/downloads/)
- [PyTorch >= 1.4.0](http://pytorch.org)
- [ImageNet](https://www.image-net.org/challenges/LSVRC/2012/)
- [COCO2017](https://cocodataset.org/#detection-2017)
- [Cifar10/100](https://www.cs.toronto.edu/~kriz/cifar.html)

### Training
As an example, use the following command to train a CondenseNetV2-A/B/C on ImageNet

```
python -m torch.distributed.launch --nproc_per_node=8 train.py --model cdnv2_a/b/c 
  --batch-size 1024 --lr 0.4 --warmup-lr 0.1 --warmup-epochs 5 --opt sgd --sched cosine \
  --epochs 350 --weight-decay 4e-5 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 \
  --data_url /PATH/TO/IMAGENET --train_url /PATH/TO/LOG_DIR
```


### Evaluation
We take the ImageNet model trained above as an example.

To evaluate the non-converted trained model, use `test.py` to evaluate from a given checkpoint path:

```
python test.py --model cdnv2_a/b/c \
  --data_url /PATH/TO/IMAGENET -b 32 -j 8 \
  --train_url /PATH/TO/LOG_DIR \
  --evaluate_from /PATH/TO/MODEL_WEIGHT
```

To evaluate the converted trained model, use `--model converted_cdnv2_a/b/c`:

```
python test.py --model converted_cdnv2_a/b/c \
  --data_url /PATH/TO/IMAGENET -b 32 -j 8 \
  --train_url /PATH/TO/LOG_DIR \
  --evaluate_from /PATH/TO/MODEL_WEIGHT
```

Note that these models are still the large models after training. To convert the model to standard group-convolution version as described in the paper, use the `convert_and_eval.py`:

```
python convert_and_eval.py --model cdnv2_a/b/c \
  --data_url /PATH/TO/IMAGENET -b 64 -j 8 \
  --train_url /PATH/TO/LOG_DIR \
  --convert_from /PATH/TO/MODEL_WEIGHT
```

## Results

### Results on ImageNet

| Model | FLOPs | Params | Top-1 Error | Tsinghua Cloud | Google Drive |
|---|---|---|---|---|---|
| CondenseNetV2-A | 46M | 2.0M | 35.6 | [Download](https://cloud.tsinghua.edu.cn/smart-link/34933e0e-565b-4633-b1ea-a5266d3d3fcc/) | [Download](https://drive.google.com/file/d/1fhHeAGkdZnOEgv9f-IUCy_uNfc-QHcZ_/view?usp=sharing) |
| CondenseNetV2-B | 146M | 3.6M | 28.1 | [Download](https://cloud.tsinghua.edu.cn/smart-link/444627eb-a296-458e-9a44-db38aca8a761/) | [Download](https://drive.google.com/file/d/1xFR3GcV1tsGq4tHhPS50XCW7AMnfWs6E/view?usp=sharing) |
| CondenseNetV2-C | 309M | 6.1M | 24.1 | [Download](https://cloud.tsinghua.edu.cn/smart-link/4625ac39-54b2-48c1-bcbd-c6d21a6b42fa/) | [Download](https://drive.google.com/file/d/1QaK-5KtVeK-d6ip8RMJhJ87dVmPAnWEA/view?usp=sharing) |

### Results on COCO2017 Detection

| Detection Framework | Backbone | Backbone FLOPs | mAP |
|---|---|---|---|
| FasterRCNN | ShuffleNetV2 0.5x | 41M | 22.1 |
| FasterRCNN | CondenseNetV2-A | 46M | 23.5 |
| FasterRCNN | ShuffleNetV2 1.0x | 146M | 27.4 |
| FasterRCNN | CondenseNetV2-B | 146M | 27.9 |
| FasterRCNN | MobileNet 1.0x | 300M | 30.6 |
| FasterRCNN | ShuffleNetV2 1.5x | 299M | 30.2 |
| FasterRCNN | CondenseNetV2-C | 309M | 31.4 |
| RetinaNet  | MobileNet 1.0x | 300M | 29.7 |
| RetinaNet  | ShuffleNetV2 1.5x | 299M | 29.1 |
| RetinaNet  | CondenseNetV2-C | 309M | 31.7 |

### Results on CIFAR

| Model | FLOPs | Params | CIFAR-10 | CIFAR-100 |
|---|---|---|---|---|
| CondenseNet-50 | 28.6M | 0.22M | 6.22 | - |
| CondenseNet-74 | 51.9M | 0.41M | 5.28 | - |
| CondenseNet-86 | 65.8M | 0.52M | 5.06 | 23.64 |
| CondenseNet-98 | 81.3M | 0.65M | 4.83 | - |
| CondenseNet-110 | 98.2M | 0.79M | 4.63 | - |
| CondenseNet-122 | 116.7M | 0.95M | 4.48 | - |
| CondenseNetV2-110 | 41M | 0.48M | 4.65 | 23.94 |
| CondenseNetV2-146 | 62M | 0.78M | **4.35** | **22.52** |

## Contacts
yangle15@mails.tsinghua.edu.cn
jhj20@mails.tsinghua.edu.cn

Any discussions or concerns are welcomed!

### Citation

If you find our project useful in your research, please consider citing:

```
@inproceedings{yang2021condensenetv2,
  title={CondenseNet V2: Sparse Feature Reactivation for Deep Networks},
  author={Yang, Le and Jiang, Haojun and Cai, Ruojin and Wang, Yulin and Song, Shiji and Huang, Gao and Tian, Qi},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4321--4330},
  year={2021}
}
```