EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

News

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• [2023/09/18] The first version of the EfficientViT Segment Anything Model has been released.
• [2023/09/12] EfficientViT is highlighted by MIT home page and MIT News.
• [2023/07/18] EfficientViT is accepted by ICCV 2023.

Demo

EfficientViT-L0 for Segment Anything (1009 image/s on A100 GPU)

EfficientViT-L1 for Semantic Segmentation (45.9ms on Nvidia Jetson AGX Orin, 82.7 mIoU on Cityscapes)

About EfficientViT Models

EfficientViT is a new family of vision models for efficient high-resolution dense prediction. The core building block of EfficientViT is a new lightweight multi-scale linear attention module that achieves global receptive field and multi-scale learning with only hardware-efficient operations.

Getting Started

Installation

conda create -n efficientvit python=3.10
conda activate efficientvit
conda install -c conda-forge mpi4py openmpi
pip install -r requirements.txt

Dataset

ImageNet: https://www.image-net.org/

Our code expects the ImageNet dataset directory to follow the following structure:

imagenet
├── train
├── val

Cityscapes: https://www.cityscapes-dataset.com/

Our code expects the Cityscapes dataset directory to follow the following structure:

cityscapes
├── gtFine
|   ├── train
|   ├── val
├── leftImg8bit
|   ├── train
|   ├── val

ADE20K: https://groups.csail.mit.edu/vision/datasets/ADE20K/

Our code expects the ADE20K dataset directory to follow the following structure:

ade20k
├── annotations
|   ├── training
|   ├── validation
├── images
|   ├── training
|   ├── validation

Pretrained Models

Latency/Throughput is measured on NVIDIA Jetson Nano, NVIDIA Jetson AGX Orin, and NVIDIA A100 GPU with TensorRT, fp16. Data transfer time is included.

Segment Anything

In this version, the EfficientViT segment anything models are trained using the image embedding extracted by SAM ViT-H as the target. The prompt encoder and mask decoder are the same as SAM ViT-H.

Image Encoder	COCO-val2017 mIoU (all)	COCO-val2017 mIoU (large)	COCO-val2017 mIoU (medium)	COCO-val2017 mIoU (small)	Params	MACs	A100 Throughput	Checkpoint
NanoSAM	70.6	79.6	73.8	62.4	-	-	744 image/s	-
MobileSAM	72.8	80.4	75.9	65.8	-	-	297 image/s	-
EfficientViT-L0	74.5	81.4	77.2	68.2	31M	35G	1009 image/s	link
EfficientViT-L1	75.2	81.8	78.1	68.9	44M	49G	815 image/s	link

ImageNet

All EfficientViT classification models are trained on ImageNet-1K with random initialization (300 epochs + 20 warmup epochs) using supervised learning.

Model	Resolution	ImageNet Top1 Acc	ImageNet Top5 Acc	Params	MACs	A100 Throughput	Checkpoint
EfficientNetV2-S	384x384	83.9	-	22M	8.8G	2869 image/s	-
EfficientNetV2-M	480x480	85.1	-	54M	24G	1160 image/s	-
EfficientViT-L1	224x224	84.5	96.9	53M	5.3G	6207 image/s	link
EfficientViT-L2	224x224	85.0	97.1	64M	6.9G	4998 image/s	link
EfficientViT-L2	256x256	85.4	97.2	64M	9.1G	3969 image/s	link
EfficientViT-L2	288x288	85.6	97.4	64M	11G	3102 image/s	link
EfficientViT-L2	320x320	85.8	97.4	64M	14G	2525 image/s	link
EfficientViT-L2	352x352	85.9	97.5	64M	17G	2099 image/s	link
EfficientViT-L2	384x384	86.0	97.5	64M	20G	1784 image/s	link

EfficientViT B series

Model	Resolution	ImageNet Top1 Acc	ImageNet Top5 Acc	Params	MACs	Jetson Nano (bs1)	Jetson Orin (bs1)	Checkpoint
EfficientViT-B1	224x224	79.4	94.3	9.1M	0.52G	24.8ms	1.48ms	link
EfficientViT-B1	256x256	79.9	94.7	9.1M	0.68G	28.5ms	1.57ms	link
EfficientViT-B1	288x288	80.4	95.0	9.1M	0.86G	34.5ms	1.82ms	link
EfficientViT-B2	224x224	82.1	95.8	24M	1.6G	50.6ms	2.63ms	link
EfficientViT-B2	256x256	82.7	96.1	24M	2.1G	58.5ms	2.84ms	link
EfficientViT-B2	288x288	83.1	96.3	24M	2.6G	69.9ms	3.30ms	link
EfficientViT-B3	224x224	83.5	96.4	49M	4.0G	101ms	4.36ms	link
EfficientViT-B3	256x256	83.8	96.5	49M	5.2G	120ms	4.74ms	link
EfficientViT-B3	288x288	84.2	96.7	49M	6.5G	141ms	5.63ms	link

Cityscapes

Model	Resolution	Cityscapes mIoU	Params	MACs	Jetson Orin Latency (bs1)	A100 Throughput (bs1)	Checkpoint
EfficientViT-L1	1024x2048	82.7	40M	282G	45.9ms	122 image/s	link
EfficientViT-L2	1024x2048	83.2	53M	396G	60.0ms	102 image/s	link

EfficientViT B series

Model	Resolution	Cityscapes mIoU	Params	MACs	Jetson Nano (bs1)	Jetson Orin (bs1)	Checkpoint
EfficientViT-B0	1024x2048	75.7	0.7M	4.4G	275ms	9.9ms	link
EfficientViT-B1	1024x2048	80.5	4.8M	25G	819ms	24.3ms	link
EfficientViT-B2	1024x2048	82.1	15M	74G	1676ms	46.5ms	link
EfficientViT-B3	1024x2048	83.0	40M	179G	3192ms	81.8ms	link

ADE20K

Model	Resolution	ADE20K mIoU	Params	MACs	Jetson Orin Latency (bs1)	A100 Throughput (bs16)	Checkpoint
EfficientViT-L1	512x512	49.1	40M	36G	7.2ms	947 image/s	link
EfficientViT-L2	512x512	50.7	51M	45G	9.0ms	758 image/s	link

EfficientViT B series

Model	Resolution	ADE20K mIoU	Params	MACs	Jetson Nano (bs1)	Jetson Orin (bs1)	Checkpoint
EfficientViT-B1	512x512	42.8	4.8M	3.1G	110ms	4.0ms	link
EfficientViT-B2	512x512	45.9	15M	9.1G	212ms	7.3ms	link
EfficientViT-B3	512x512	49.0	39M	22G	411ms	12.5ms	link

Usage

# segment anything
from efficientvit.sam_model_zoo import create_sam_model

efficientvit_sam = create_sam_model(
  name="l1", weight_url="assets/checkpoints/sam/l1.pt",
)
efficientvit_sam = efficientvit_sam.cuda().eval()

from efficientvit.models.efficientvit.sam import EfficientViTSamPredictor

efficientvit_sam_predictor = EfficientViTSamPredictor(efficientvit_sam)

from efficientvit.models.efficientvit.sam import EfficientViTSamAutomaticMaskGenerator

efficientvit_mask_generator = EfficientViTSamAutomaticMaskGenerator(efficientvit_sam)

# classification
from efficientvit.cls_model_zoo import create_cls_model

model = create_cls_model(
  name="l2", weight_url="assets/checkpoints/cls/l2-r384.pt"
)

# semantic segmentation
from efficientvit.seg_model_zoo import create_seg_model

model = create_seg_model(
  name="l2", dataset="cityscapes", weight_url="assets/checkpoints/seg/cityscapes/l2.pt"
)

model = create_seg_model(
  name="l2", dataset="ade20k", weight_url="assets/checkpoints/seg/ade20k/l2.pt"
)

Evaluation

Please run eval_sam_coco.py, eval_cls_model.py or eval_seg_model.py to evaluate our models.

Examples: segment anything, classification, segmentation

Visualization

Please run demo_sam_model.py to visualize our segment anything models.

Example:

# segment everything
python demo_sam_model.py --model l1 --mode all

# prompt with points
python demo_sam_model.py --model l1 --mode point

# prompt with box
python demo_sam_model.py --model l1 --mode box --box "[150,70,630,400]"

Please run eval_seg_model.py to visualize the outputs of our semantic segmentation models.

Example:

python eval_seg_model.py --dataset cityscapes --crop_size 1024 --model b3 --save_path demo/cityscapes/b3/

Benchmarking with TFLite

To generate TFLite files, please refer to tflite_export.py. It requires the TinyNN package.

pip install git+https://github.com/alibaba/TinyNeuralNetwork.git

Example:

python tflite_export.py --export_path model.tflite --task seg --dataset ade20k --model b3 --resolution 512 512

Benchmarking with TensorRT

To generate onnx files, please refer to onnx_export.py.

Benchmarking with ONNXRuntime

bash run.sh encoder
bash run.sh decoder
bash run.sh demo_onnx

Training

Please see TRAINING.md for detailed training instructions.

Contact

Han Cai: [email protected]

TODO

• ImageNet Pretrained models
• Segmentation Pretrained models
• ImageNet training code
• EfficientViT L series, designed for cloud
• EfficientViT for segment anything
• EfficientViT for super-resolution
• Segmentation training code

Citation

If EfficientViT is useful or relevant to your research, please kindly recognize our contributions by citing our paper:

@article{cai2022efficientvit,
  title={Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition},
  author={Cai, Han and Gan, Chuang and Han, Song},
  journal={arXiv preprint arXiv:2205.14756},
  year={2022}
}