https://github.com/naver-ai/rdnet

[ECCV2024] Official implementation of paper, "DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs".
https://github.com/naver-ai/rdnet

backbone classification convolutional-neural-networks dense-connections densenet eccv2024 rdnet revisit

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[ECCV2024] Official implementation of paper, "DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs".

Host: GitHub
URL: https://github.com/naver-ai/rdnet
Owner: naver-ai
License: apache-2.0
Created: 2024-03-28T16:22:32.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2024-08-08T05:21:14.000Z (almost 2 years ago)
Last Synced: 2025-02-07T10:05:00.790Z (over 1 year ago)
Topics: backbone, classification, convolutional-neural-networks, dense-connections, densenet, eccv2024, rdnet, revisit
Language: Python
Homepage: https://arxiv.org/abs/2403.19588
Size: 14.7 MB
Stars: 139
Watchers: 5
Forks: 5
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

          


 🎉🎉 Our paper has been accepted at ECCV 2024! Stay tuned for more updates !! 🎉🎉 


DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs


[Donghyun Kim](https://scholar.google.co.kr/citations?hl=en&user=EBC8BMAAAAAJ)^1*, [Byeongho Heo](https://sites.google.com/view/byeongho-heo/home)², [Dongyoon Han](https://dongyoonhan.github.io/)^2*

¹NAVER Cloud AI, ²NAVER AI Lab







    



    



    



    



  

  



We revitalize **Densely Connected Convolutional Networks (DenseNets)** and reveal their untapped potential to challenge the prevalent dominance of ResNet-style architectures. Our research indicates that DenseNets were previously underestimated, primarily due to conventional design choices and training methods that underexploited their full capabilities.




![tradeoff with SOTA models](resources/images/tradeoff_sota.png)

*
Tradeoff with RDNet (ours) and SOTA models
*




![tradeoff with mainstream models](resources/images/tradeoff.png)

*
Tradeoff with RDNet (ours) and mainstream models
*

### Key Highlights:

- Pilot study (**§5.1**) reveals *concatenations' effectiveness*.

- We have meticulously *upgraded various aspects of DenseNets* (**§3.2**) through architectural tweaks and block redesigns.

- Our revitalized DenseNets (**RDNets**) *outperform mainstream architectures* such as Swin Transformer, ConvNeXt, and DeiT-III (**§4.1**).

Our work aims to reignite interest in DenseNets by demonstrating their renewed relevance and superiority in the current architectural landscape. We encourage the community to explore and build upon our findings, paving the way for further innovative contributions in deep learning architectures.

*We believe that various architectural designs that have been popular recently would be combined with dense connections successfully.*

## Easy to use

RDNet is available on `timm`. You can easily use RDNet by installing the `timm` package.

```python

import timm

model = timm.create_model('rdnet_large', pretrained=True)

```

For detailed usage, please refer to the [huggingface model card](https://huggingface.co/naver-ai/rdnet_tiny.nv_in1k).

## Updates

- **(2024.07.24)**: Pip installable pacakge added.

- **(2024.04.19)**: Initial release of the repository.

- **(2024.03.28)**: Paper is available on [arXiv](https://arxiv.org/abs/2403.19588).

## Coming Soon

- [ ] More ImageNet-22k Pretrained Models.

- [ ] More ImageNet-1k fine-tuned models.

- [x] Cascade Mask R-CNN with RDNet.

- [ ] Transfer Learning with RDNet (with cifar10, cifar100, stanford cars, ...).

## RDNet for Image Classification

For details on object detection and instance segmentation, please refer to [detection/README.md](detection/README.md).

For details on semantic segmentation, please refer to [segmentation/README.md](segmentation/README.md).

### Model Zoo

We provide the pretrained models for RDNet. You can download the pretrained models from the links below.

#### ImageNet-1K (pre-)trained models

|  Model  | IMG Size | Params | FLOPs | Top-1 | Model Card                                                         |                             url                              |

|:-------:|:--------:|:------:|:-----:|:-----:|--------------------------------------------------------------------|:------------------------------------------------------------:|

| RDNet-T |   224    |  22M   | 5.0G  | 82.8  | [model_card](./resources/hfhub_model_cards/rdnet_tiny.nv_in1k.md)  | [HFHub](https://huggingface.co/naver-ai/rdnet_tiny.nv_in1k)  |

| RDNet-S |   224    |  50M   | 8.7G  | 83.7  | [model_card](./resources/hfhub_model_cards/rdnet_small.nv_in1k.md) | [HFHub](https://huggingface.co/naver-ai/rdnet_small.nv_in1k) |

| RDNet-B |   224    |  87M   | 15.4G | 84.4  | [model_card](./resources/hfhub_model_cards/rdnet_base.nv_in1k.md)  | [HFHub](https://huggingface.co/naver-ai/rdnet_base.nv_in1k)  |

| RDNet-L |   224    |  186M  | 34.7G | 84.8  | [model_card](./resources/hfhub_model_cards/rdnet_large.nv_in1k.md) | [HFHub](https://huggingface.co/naver-ai/rdnet_large.nv_in1k) |

#### ImageNet-1K fine-tuned models

|     Model     | fine-tune from | IMG Size | Params | FLOPs  | Top-1 | Model Card                                                                     |                                   url                                    |

|:-------------:|:--------------:|:--------:|:------:|:------:|:-----:|--------------------------------------------------------------------------------|:------------------------------------------------------------------------:|

| RDNet-L (384) |    RDNet-L     |   384    |  186M  | 101.9G | 85.8  | [model_card](./resources/hfhub_model_cards/rdnet_large.nv_in1k_ft_in1k_384.md) | [HFHub](https://huggingface.co/naver-ai/rdnet_large.nv_in1k_ft_in1k_384) |

### Training

We provide the graphs of the training procedure. The graph is generated by the Weights & Biases service. You can check the graph by clicking the link below.

https://api.wandb.ai/links/dhkim0225/822w2zsj

For training commands, please refer to the [TRAINING.md](TRAINING.md).

### Acknowledgement

This repository is built using the [timm](https://github.com/huggingface/pytorch-image-models), [MMDetection](https://github.com/open-mmlab/mmdetection), and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation).

### Citation

```

@misc{kim2024densenets,

    title={DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs}, 

    author={Donghyun Kim and Byeongho Heo and Dongyoon Han},

    year={2024},

    eprint={2403.19588},

    archivePrefix={arXiv},

}

```

### License

```

Copyright (c) 2024-present NAVER Cloud Corp.

Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

```

### License for Dataset

ImageNet - ImageNet Terms of access, https://image-net.org/download

Images from ADE20K - ADE20K Terms of Use, https://groups.csail.mit.edu/vision/datasets/ADE20K/terms/

MS COCO images dataset - Creative Commons Attribution 4.0 License, https://viso.ai/computer-vision/coco-dataset/