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DaiShiResearch/
upernet-transnext-tiny-ade

Name: upernet-transnext-tiny-ade
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TransNeXt

Official Model release for "TransNeXt: Robust Foveal Visual Perception for Vision Transformers" [CVPR 2024] .

Model Details

Code: https://github.com/DaiShiResearch/TransNeXt
Paper: TransNeXt: Robust Foveal Visual Perception for Vision Transformers
Author: Dai Shi
Email: daishiresearch@gmail.com

Methods

Pixel-focused attention (Left) & aggregated attention (Right):

Convolutional GLU (First on the right):

Results

Image Classification, Detection and Segmentation:

Attention Visualization:

Model Zoo

Image Classification

Classification code & weights & configs & training logs are >>>here<<<.

ImageNet-1K 224x224 pre-trained models:

Model	#Params	#FLOPs	IN-1K	IN-A	IN-C↓	IN-R	Sketch	IN-V2	Download	Config	Log
TransNeXt-Micro	12.8M	2.7G	82.5	29.9	50.8	45.8	33.0	72.6	model	config	log
TransNeXt-Tiny	28.2M	5.7G	84.0	39.9	46.5	49.6	37.6	73.8	model	config	log
TransNeXt-Small	49.7M	10.3G	84.7	47.1	43.9	52.5	39.7	74.8	model	config	log
TransNeXt-Base	89.7M	18.4G	84.8	50.6	43.5	53.9	41.4	75.1	model	config	log

ImageNet-1K 384x384 fine-tuned models:

Model	#Params	#FLOPs	IN-1K	IN-A	IN-R	Sketch	IN-V2	Download	Config
TransNeXt-Small	49.7M	32.1G	86.0	58.3	56.4	43.2	76.8	model	config
TransNeXt-Base	89.7M	56.3G	86.2	61.6	57.7	44.7	77.0	model	config

ImageNet-1K 256x256 pre-trained model fully utilizing aggregated attention at all stages:

(See Table.9 in Appendix D.6 for details)

Model	Token mixer	#Params	#FLOPs	IN-1K	Download	Config	Log
TransNeXt-Micro	A-A-A-A	13.1M	3.3G	82.6	model	config	log

Object Detection

Object detection code & weights & configs & training logs are >>>here<<<.

COCO object detection and instance segmentation results using the Mask R-CNN method:

Backbone	Pretrained Model	Lr Schd	box mAP	mask mAP	#Params	Download	Config	Log
TransNeXt-Tiny	ImageNet-1K	1x	49.9	44.6	47.9M	model	config	log
TransNeXt-Small	ImageNet-1K	1x	51.1	45.5	69.3M	model	config	log
TransNeXt-Base	ImageNet-1K	1x	51.7	45.9	109.2M	model	config	log

COCO object detection results using the DINO method:

Backbone	Pretrained Model	scales	epochs	box mAP	#Params	Download	Config	Log
TransNeXt-Tiny	ImageNet-1K	4scale	12	55.1	47.8M	model	config	log
TransNeXt-Tiny	ImageNet-1K	5scale	12	55.7	48.1M	model	config	log
TransNeXt-Small	ImageNet-1K	5scale	12	56.6	69.6M	model	config	log
TransNeXt-Base	ImageNet-1K	5scale	12	57.1	110M	model	config	log

Semantic Segmentation

Semantic segmentation code & weights & configs & training logs are >>>here<<<.

ADE20K semantic segmentation results using the UPerNet method:

Backbone	Pretrained Model	Crop Size	Lr Schd	mIoU	mIoU (ms+flip)	#Params	Download	Config	Log
TransNeXt-Tiny	ImageNet-1K	512x512	160K	51.1	51.5/51.7	59M	model	config	log
TransNeXt-Small	ImageNet-1K	512x512	160K	52.2	52.5/52.8	80M	model	config	log
TransNeXt-Base	ImageNet-1K	512x512	160K	53.0	53.5/53.7	121M	model	config	log

In the context of multi-scale evaluation, TransNeXt reports test results under two distinct scenarios: interpolation and extrapolation of relative position bias.

ADE20K semantic segmentation results using the Mask2Former method:

Backbone	Pretrained Model	Crop Size	Lr Schd	mIoU	#Params	Download	Config	Log
TransNeXt-Tiny	ImageNet-1K	512x512	160K	53.4	47.5M	model	config	log
TransNeXt-Small	ImageNet-1K	512x512	160K	54.1	69.0M	model	config	log
TransNeXt-Base	ImageNet-1K	512x512	160K	54.7	109M	model	config	log

Citation

If you find our work helpful, please consider citing the following bibtex. We would greatly appreciate a star for this project.

@InProceedings{shi2023transnext,
  author    = {Dai Shi},
  title     = {TransNeXt: Robust Foveal Visual Perception for Vision Transformers},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2024},
  pages     = {17773-17783}
}

DaiShiResearch/upernet-transnext-tiny-ade