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Image Classification

Datasets

ImageNet: https://www.image-net.org/ 
Our code expects the ImageNet dataset directory to follow the following structure:

imagenet
├── train
├── val

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.

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 8.4G 2869 image/s -
EfficientNetV2-M 480x480 85.2 - 54M 25G 1160 image/s -
EfficientViT-L1 224x224 84.484 96.862 53M 5.3G 6207 image/s link
EfficientViT-L2 224x224 85.050 97.090 64M 6.9G 4998 image/s link
EfficientViT-L2 256x256 85.366 97.216 64M 9.1G 3969 image/s link
EfficientViT-L2 288x288 85.630 97.364 64M 11G 3102 image/s link
EfficientViT-L2 320x320 85.734 97.438 64M 14G 2525 image/s link
EfficientViT-L2 384x384 85.978 97.518 64M 20G 1784 image/s link
EfficientViT-L3 224x224 85.814 97.198 246M 28G 2081 image/s link
EfficientViT-L3 256x256 85.938 97.318 246M 36G 1641 image/s link
EfficientViT-L3 288x288 86.070 97.440 246M 46G 1276 image/s link
EfficientViT-L3 320x320 86.230 97.474 246M 56G 1049 image/s link
EfficientViT-L3 384x384 86.408 97.632 246M 81G 724 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 79.390 94.346 9.1M 0.52G 24.8ms 1.48ms link
EfficientViT-B1 256x256 79.918 94.704 9.1M 0.68G 28.5ms 1.57ms link
EfficientViT-B1 288x288 80.410 94.984 9.1M 0.86G 34.5ms 1.82ms link
EfficientViT-B2 224x224 82.100 95.782 24M 1.6G 50.6ms 2.63ms link
EfficientViT-B2 256x256 82.698 96.096 24M 2.1G 58.5ms 2.84ms link
EfficientViT-B2 288x288 83.086 96.302 24M 2.6G 69.9ms 3.30ms link
EfficientViT-B3 224x224 83.468 96.356 49M 4.0G 101ms 4.36ms link
EfficientViT-B3 256x256 83.806 96.514 49M 5.2G 120ms 4.74ms link
EfficientViT-B3 288x288 84.150 96.732 49M 6.5G 141ms 5.63ms link

Usage

# classification
from efficientvit.cls_model_zoo import create_cls_model

model = create_cls_model(
  name="l3", weight_url="assets/checkpoints/cls/l3-r384.pt"
)

Evaluation

Please run eval_cls_model.py to evaluate our models.

Examples: classification

Export

Onnx

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

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 --model b3 --resolution 224 224

Training

Please refer to train_cls_model.py for training models on imagenet.

Single-Node Training Examples:

torchrun --nnodes 1 --nproc_per_node=8 \
train_cls_model.py configs/cls/imagenet/b1.yaml \
    --data_provider.image_size "[128,160,192,224,256,288]" \
    --run_config.eval_image_size "[288]" \
    --path .exp/cls/imagenet/b1_r288/

torchpack dist-run -np 8 \
python train_cls_model.py configs/cls/imagenet/b1.yaml \
    --data_provider.image_size "[128,160,192,224,256,288]" \
    --run_config.eval_image_size "[288]" \
    --path .exp/cls/imagenet/b1_r288/

EfficientViT L Series

torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/l1.yaml --amp bf16 \
    --path .exp/cls/imagenet/l1_r224/
torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/l2.yaml --amp bf16 \
    --path .exp/cls/imagenet/l2_r224/
torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/l3.yaml --amp bf16 \
    --path .exp/cls/imagenet/l3_r224/

EfficientViT B Series

torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/b1.yaml \
    --path .exp/cls/imagenet/b1_r224/
torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/b1.yaml \
    --data_provider.image_size "[128,160,192,224,256,288]" \
    --run_config.eval_image_size "[288]" \
    --path .exp/cls/imagenet/b1_r288/
torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/b2.yaml \
    --path .exp/cls/imagenet/b2_r224/
torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/b2.yaml \
    --data_provider.image_size "[128,160,192,224,256,288]" \
    --run_config.eval_image_size "[288]" \
    --data_provider.data_aug "{n:1,m:5}" \
    --path .exp/cls/imagenet/b2_r288/
torchpack dist-run -np 16 -H $server1:8,$server2:8 \
python train_cls_model.py configs/cls/imagenet/b3.yaml \
    --path .exp/cls/imagenet/b3_r224/

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}
}