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recnext_a0.base_300e_in1k

Name: recnext_a0.base_300e_in1k
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Model Card for RecNeXt-A0

Abstract

Recent advances in vision transformers (ViTs) have demonstrated the advantage of global modeling capabilities, prompting widespread integration of large-kernel convolutions for enlarging the effective receptive field (ERF). However, the quadratic scaling of parameter count and computational complexity (FLOPs) with respect to kernel size poses significant efficiency and optimization challenges. This paper introduces RecConv, a recursive decomposition strategy that efficiently constructs multi-frequency representations using small-kernel convolutions. RecConv establishes a linear relationship between parameter growth and decomposing levels which determines the effective receptive field $k\times 2^\ell$ for a base kernel $k$ and $\ell$ levels of decomposition, while maintaining constant FLOPs regardless of the ERF expansion. Specifically, RecConv achieves a parameter expansion of only $\ell+2$ times and a maximum FLOPs increase of $5/3$ times, compared to the exponential growth ($4^\ell$) of standard and depthwise convolutions. RecNeXt-M3 outperforms RepViT-M1.1 by 1.9 $AP^{box}$ on COCO with similar FLOPs. This innovation provides a promising avenue towards designing efficient and compact networks across various modalities. Codes and models can be found at https://github.com/suous/RecNeXt.

Model Details

Model Type: Image Classification / Feature Extraction
Model Series: A
Model Stats:
- Parameters: 2.8M
- MACs: 0.4G
- Latency: 1.4ms (iPhone 13, iOS 18)
- Throughput: 4891 (RTX 3090)
- Image Size: 224x224
Architecture Configuration:
- Embedding Dimensions: (40, 80, 160, 320)
- Depths: (2, 2, 9, 1)
- MLP Ratio: (2, 2, 2, 2)
Paper: RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations
Code: https://github.com/suous/RecNeXt
Dataset: ImageNet-1K

Recent Updates

UPDATES 🔥

2025/07/23: Added a simple architecture, the overall design follows LSNet.
2025/07/04: Uploaded classification models to HuggingFace🤗.
2025/07/01: Added more comparisons with LSNet.
2025/06/27: Added A series code and logs, replacing convolution with linear attention.
2025/03/19: Added more ablation study results, including using attention with RecConv design.
2025/01/02: Uploaded checkpoints and training logs of RecNeXt-M0.
2024/12/29: Uploaded checkpoints and training logs of RecNeXt-M1 - M5.

Model Usage

Image Classification

python

from urllib.request import urlopen
from PIL import Image
import timm
import torch

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('recnext_a0', pretrained=True, distillation=False)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Converting to Inference Mode

python

import utils

# Convert training-time model to inference structure, fuse batchnorms
utils.replace_batchnorm(model)

Model Comparison

Classification

We introduce two series of models: the A series uses linear attention and nearest interpolation, while the M series employs convolution and bilinear interpolation for simplicity and broader hardware compatibility (e.g., to address suboptimal nearest interpolation support in some iOS versions).

dist: distillation; base: without distillation (all models are trained over 300 epochs).

model	top_1_accuracy	params	gmacs	npu_latency	cpu_latency	throughput	fused_weights	training_logs
M0	74.7* \| 73.2	2.5M	0.4	1.0ms	189ms	750	dist \| base	dist \| base
M1	79.2* \| 78.0	5.2M	0.9	1.4ms	361ms	384	dist \| base	dist \| base
M2	80.3* \| 79.2	6.8M	1.2	1.5ms	431ms	325	dist \| base	dist \| base
M3	80.9* \| 79.6	8.2M	1.4	1.6ms	482ms	314	dist \| base	dist \| base
M4	82.5* \| 81.4	14.1M	2.4	2.4ms	843ms	169	dist \| base	dist \| base
M5	83.3* \| 82.9	22.9M	4.7	3.4ms	1487ms	104	dist \| base	dist \| base
A0	75.0* \| 73.6	2.8M	0.4	1.4ms	177ms	4891	dist \| base	dist \| base
A1	79.6* \| 78.3	5.9M	0.9	1.9ms	334ms	2730	dist \| base	dist \| base
A2	80.8* \| 79.6	7.9M	1.2	2.2ms	413ms	2331	dist \| base	dist \| base
A3	81.1* \| 80.1	9.0M	1.4	2.4ms	447ms	2151	dist \| base	dist \| base
A4	82.5* \| 81.6	15.8M	2.4	3.6ms	764ms	1265	dist \| base	dist \| base
A5	83.5* \| 83.1	25.7M	4.7	5.6ms	1376ms	733	dist \| base	dist \| base

Comparison with LSNet

We present a simple architecture, the overall design follows LSNet. This framework centers around sharing channel features from the previous layers. Our motivation for doing so is to reduce the computational cost of token mixers and minimize feature redundancy in the final stage.

With Shared-Channel Blocks

model	top_1_accuracy	params	gmacs	npu_latency	cpu_latency	throughput	fused_weights	training_logs
T	76.8 \| 75.2	12.1M	0.3	1.8ms	105ms	13957	dist \| norm	dist \| norm
S	79.5 \| 78.3	15.8M	0.7	2.0ms	182ms	8034	dist \| norm	dist \| norm
B	81.5 \| 80.3	19.2M	1.1	2.5ms	296ms	4472	dist \| norm	dist \| norm

Without Shared-Channel Blocks

model	top_1_accuracy	params	gmacs	npu_latency	cpu_latency	throughput	fused_weights	training_logs
T	76.6* \| 75.1	12.1M	0.3	1.8ms	109ms	13878	dist \| base	dist \| base
S	79.6* \| 78.3	15.8M	0.7	2.0ms	188ms	7989	dist \| base	dist \| base
B	81.4* \| 80.3	19.3M	1.1	2.5ms	290ms	4450	dist \| base	dist \| base

The NPU latency is measured on an iPhone 13 with models compiled by Core ML Tools. The CPU latency is accessed on a Quad-core ARM Cortex-A57 processor in ONNX format. And the throughput is tested on an Nvidia RTX3090 with maximum power-of-two batch size that fits in memory.

Latency Measurement

The latency reported in RecNeXt for iPhone 13 (iOS 18) uses the benchmark tool from XCode 14.

RecNeXt-M0

RecNeXt-M1

RecNeXt-M2

RecNeXt-M3

RecNeXt-M4

RecNeXt-M5

RecNeXt-A0

RecNeXt-A1

RecNeXt-A2

RecNeXt-A3

RecNeXt-A4

RecNeXt-A5

RecNeXt-T

RecNeXt-S

RecNeXt-B

Tips: export the model to Core ML model

python export_coreml.py --model recnext_m1 --ckpt pretrain/recnext_m1_distill_300e.pth

Tips: measure the throughput on GPU

python speed_gpu.py --model recnext_m1

ImageNet (Training and Evaluation)

Prerequisites

conda virtual environment is recommended.

conda create -n recnext python=3.8
pip install -r requirements.txt

Data preparation

Download and extract ImageNet train and val images from http://image-net.org/. The training and validation data are expected to be in the train folder and val folder respectively:

bash

# script to extract ImageNet dataset: https://github.com/pytorch/examples/blob/main/imagenet/extract_ILSVRC.sh
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)

# organize the ImageNet dataset as follows:
imagenet
├── train
│   ├── n01440764
│   │   ├── n01440764_10026.JPEG
│   │   ├── n01440764_10027.JPEG
│   │   ├── ......
│   ├── ......
├── val
│   ├── n01440764
│   │   ├── ILSVRC2012_val_00000293.JPEG
│   │   ├── ILSVRC2012_val_00002138.JPEG
│   │   ├── ......
│   ├── ......

Training

To train RecNeXt-M1 on an 8-GPU machine:

python -m torch.distributed.launch --nproc_per_node=8 --master_port 12346 --use_env main.py --model recnext_m1 --data-path ~/imagenet --dist-eval

Tips: specify your data path and model name!

Testing

For example, to test RecNeXt-M1:

python main.py --eval --model recnext_m1 --resume pretrain/recnext_m1_distill_300e.pth --data-path ~/imagenet

Use pretrained model without knowledge distillation from HuggingFace 🤗.

python main.py --eval --model recnext_m1 --data-path ~/imagenet --pretrained --distillation-type none

Use pretrained model with knowledge distillation from HuggingFace 🤗.

python main.py --eval --model recnext_m1 --data-path ~/imagenet --pretrained --distillation-type hard

Fused model evaluation

For example, to evaluate RecNeXt-M1 with the fused model:

Colab

python fuse_eval.py --model recnext_m1 --resume pretrain/recnext_m1_distill_300e_fused.pt --data-path ~/imagenet

Extract model for publishing

# without distillation
python publish.py --model_name recnext_m1 --checkpoint_path pretrain/checkpoint_best.pth --epochs 300

# with distillation
python publish.py --model_name recnext_m1 --checkpoint_path pretrain/checkpoint_best.pth --epochs 300 --distillation

# fused model
python publish.py --model_name recnext_m1 --checkpoint_path pretrain/checkpoint_best.pth --epochs 300 --fused

Downstream Tasks

Object Detection and Instance Segmentation

model	$AP^b$	$AP_{50}^b$	$AP_{75}^b$	$AP^m$	$AP_{50}^m$	$AP_{75}^m$	Latency	Ckpt	Log
M3	41.7	63.4	45.4	38.6	60.5	41.4	5.2ms	M3	M3
M4	43.5	64.9	47.7	39.7	62.1	42.4	7.6ms	M4	M4
M5	44.6	66.3	49.0	40.6	63.5	43.5	12.4ms	M5	M5
A3	42.1	64.1	46.2	38.8	61.1	41.6	8.3ms	A3	A3
A4	43.5	65.4	47.6	39.8	62.4	42.9	14.0ms	A4	A4
A5	44.4	66.3	48.9	40.3	63.3	43.4	25.3ms	A5	A5

Semantic Segmentation

Model	mIoU	Latency	Ckpt	Log
RecNeXt-M3	41.0	5.6ms	M3	M3
RecNeXt-M4	43.6	7.2ms	M4	M4
RecNeXt-M5	46.0	12.4ms	M5	M5
RecNeXt-A3	41.9	8.4ms	A3	A3
RecNeXt-A4	43.0	14.0ms	A4	A4
RecNeXt-A5	46.5	25.3ms	A5	A5

Ablation Study

Overall Experiments

Ablation Logs

RecConv Recurrent Aggregation

python

class RecConv2d(nn.Module):
  def __init__(self, in_channels, kernel_size=5, bias=False, level=1, mode='nearest'):
      super().__init__()
      self.level = level
      self.mode = mode
      kwargs = {
          'in_channels': in_channels,
          'out_channels': in_channels,
          'groups': in_channels,
          'kernel_size': kernel_size,
          'padding': kernel_size // 2,
          'bias': bias
      }
      self.n = nn.Conv2d(stride=2, **kwargs)
      self.a = nn.Conv2d(**kwargs) if level >1 else None
      self.b = nn.Conv2d(**kwargs) 
      self.c = nn.Conv2d(**kwargs)
      self.d = nn.Conv2d(**kwargs)

  def forward(self, x):
      # 1. Generate Multi-scale Features.
      fs = [x]
      for _ in range(self.level):
          fs.append(self.n(fs[-1]))

      # 2. Multi-scale Recurrent Aggregation.
      h = None
      for i, o in reversed(list(zip(fs[1:], fs[:-1]))):
          h = self.a(h) + self.b(i) if h is not None else self.b(i)
          h = nn.functional.interpolate(h, size=o.shape[2:], mode=self.mode)
      return self.c(h) + self.d(x)

RecConv Variants

RecConv Variant Details

RecConv using group convolutions

python

# RecConv Variant A
# recursive decomposition on both spatial and channel dimensions
# downsample and upsample through group convolutions
class RecConv2d(nn.Module):
    def __init__(self, in_channels, kernel_size=5, bias=False, level=2):
        super().__init__()
        self.level = level
        kwargs = {'kernel_size': kernel_size, 'padding': kernel_size // 2, 'bias': bias}
        downs = []
        for l in range(level):
            i_channels = in_channels // (2 ** l)
            o_channels = in_channels // (2 ** (l+1))
            downs.append(nn.Conv2d(in_channels=i_channels, out_channels=o_channels, groups=o_channels, stride=2, **kwargs))
        self.downs = nn.ModuleList(downs)

        convs = []
        for l in range(level+1):
            channels = in_channels // (2 ** l)
            convs.append(nn.Conv2d(in_channels=channels, out_channels=channels, groups=channels, **kwargs))
        self.convs = nn.ModuleList(reversed(convs))

        # this is the simplest modification, only support resoltions like 256, 384, etc
        kwargs['kernel_size'] = kernel_size + 1
        ups = []
        for l in range(level):
            i_channels = in_channels // (2 ** (l+1))
            o_channels = in_channels // (2 ** l)
            ups.append(nn.ConvTranspose2d(in_channels=i_channels, out_channels=o_channels, groups=i_channels, stride=2, **kwargs))
        self.ups = nn.ModuleList(reversed(ups))
        
    def forward(self, x):
        i = x
        features = []
        for down in self.downs:
            x, s = down(x), x.shape[2:]
            features.append((x, s))

        x = 0
        for conv, up, (f, s) in zip(self.convs, self.ups, reversed(features)):
            x = up(conv(f + x))
        return self.convs[self.level](i + x)

RecConv using channel-wise concatenation

python

# recursive decomposition on both spatial and channel dimensions
# downsample using channel-wise split, followed by depthwise convolution with a stride of 2
# upsample through channel-wise concatenation
class RecConv2d(nn.Module):
    def __init__(self, in_channels, kernel_size=5, bias=False, level=2):
        super().__init__()
        self.level = level
        kwargs = {'kernel_size': kernel_size, 'padding': kernel_size // 2, 'bias': bias}
        downs = []
        for l in range(level):
            channels = in_channels // (2 ** (l+1))
            downs.append(nn.Conv2d(in_channels=channels, out_channels=channels, groups=channels, stride=2, **kwargs))
        self.downs = nn.ModuleList(downs)

        convs = []
        for l in range(level+1):
            channels = in_channels // (2 ** l)
            convs.append(nn.Conv2d(in_channels=channels, out_channels=channels, groups=channels, **kwargs))
        self.convs = nn.ModuleList(reversed(convs))

 .      # this is the simplest modification, only support resoltions like 256, 384, etc
        kwargs['kernel_size'] = kernel_size + 1
        ups = []
        for l in range(level):
            channels = in_channels // (2 ** (l+1))
            ups.append(nn.ConvTranspose2d(in_channels=channels, out_channels=channels, groups=channels, stride=2, **kwargs))
        self.ups = nn.ModuleList(reversed(ups))

    def forward(self, x):
        features = []
        for down in self.downs:
            r, x = torch.chunk(x, 2, dim=1)
            x, s = down(x), x.shape[2:]
            features.append((r, s))

        for conv, up, (r, s) in zip(self.convs, self.ups, reversed(features)):
            x = torch.cat([r, up(conv(x))], dim=1)
        return self.convs[self.level](x)

RecConv Beyond

We apply RecConv to MLLA small variants, replacing linear attention and downsampling layers. Result in higher throughput and less training memory usage.

Ablation Logs

mlla/logs ├── 1_mlla_nano │ ├── 01_baseline.txt │ ├── 02_recconv_5x5_conv_trans.txt │ ├── 03_recconv_5x5_nearest_interp.txt │ ├── 04_recattn_nearest_interp.txt │ └── 05_recattn_nearest_interp_simplify.txt └── 2_mlla_mini ├── 01_baseline.txt ├── 02_recconv_5x5_conv_trans.txt ├── 03_recconv_5x5_nearest_interp.txt ├── 04_recattn_nearest_interp.txt └── 05_recattn_nearest_interp_simplify.txt

Limitations

RecNeXt exhibits the lowest throughput among models of comparable parameter size due to extensive use of bilinear interpolation, which can be mitigated by employing transposed convolution.
The recursive decomposition may introduce numerical instability during mixed precision training, which can be alleviated by using fixed-point or BFloat16 arithmetic.
Compatibility issues with bilinear interpolation and transposed convolution on certain iOS versions may also result in performance degradation.

Acknowledgement

Classification (ImageNet) code base is partly built with LeViT, PoolFormer, EfficientFormer, RepViT, LSNet, MLLA, and MogaNet.

The detection and segmentation pipeline is from MMCV (MMDetection and MMSegmentation).

Thanks for the great implementations!

Citation

BibTeX

@misc{zhao2024recnext,
      title={RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations},
      author={Mingshu Zhao and Yi Luo and Yong Ouyang},
      year={2024},
      eprint={2412.19628},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

suous/recnext_a0.base_300e_in1k

Model Card for RecNeXt-A0

Abstract

Model Details

Recent Updates

Model Usage

Image Classification

Converting to Inference Mode

Model Comparison

Classification

Comparison with LSNet

With Shared-Channel Blocks

Without Shared-Channel Blocks

Latency Measurement

ImageNet (Training and Evaluation)

Prerequisites

Data preparation

Training

Testing

Fused model evaluation

Extract model for publishing

Downstream Tasks

Ablation Study

Overall Experiments

RecConv Variants

RecConv Beyond

Limitations

Acknowledgement

Citation

suous/
recnext_a0.base_300e_in1k