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Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute
| Date | Stars |
|---|---|
| 2026-07-24 | 1528 |
| 2026-07-25 | 1528 |
| 2026-07-28 | 1528 |
| 2026-07-30 | 1528 |
| 2026-08-06 | 1528 |
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<img src="./λ.png" width="500px"></img>
## Lambda Networks - Pytorch
Implementation of λ Networks, a new approach to image recognition that reaches SOTA on ImageNet. The new method utilizes λ layer, which captures interactions by transforming contexts into linear functions, termed lambdas, and applying these linear functions to each input separately.
<a href="https://www.youtube.com/watch?v=3qxJ2WD8p4w">Yannic Kilcher's paper review</a>
## Install
```bash
$ pip install lambda-networks
```
## Usage
Global context
```python
import torch
from lambda_networks import LambdaLayer
layer = LambdaLayer(
dim = 32, # channels going in
dim_out = 32, # channels out
n = 64, # size of the receptive window - max(height, width)
dim_k = 16, # key dimension
heads = 4, # number of heads, for multi-query
dim_u = 1 # 'intra-depth' dimension
)
x = torch.randn(1, 32, 64, 64)
layer(x) # (1, 32, 64, 64)
```
Localized context
```python
import torch
from lambda_networks import LambdaLayer
layer = LambdaLayer(
dim = 32,
dim_out = 32,
r = 23, # the receptive field for relative positional encoding (23 x 23)
dim_k = 16,
heads = 4,
dim_u = 4
)
x = torch.randn(1, 32, 64, 64)
layer(x) # (1, 32, 64, 64)
```
For fun, you can also import this as follows
```python
from lambda_networks import λLayer
```
## Tensorflow / Keras version
<a href="https://github.com/shinel94">Shinel94</a> has added a Keras implementation! It won't be officially supported in this repository, so either copy / paste the code under `./lambda_networks/tfkeras.py` or make sure to install `tensorflow` and `keras` before running the following.
```python
import tensorflow as tf
from lambda_networks.tfkeras import LambdaLayer
layer = LambdaLayer(
dim_out = 32,
r = 23,
dim_k = 16,
heads = 4,
dim_u = 1
)
x = tf.random.normal((1, 64, 64, 16)) # channel last format
layer(x) # (1, 64, 64, 32)
```
## Citations
```bibtex
@inproceedings{
anonymous2021lambdanetworks,
title={LambdaNetworks: Modeling long-range Interactions without Attention},
author={Anonymous},
booktitle={Submitted to International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=xTJEN-ggl1b},
note={under review}
}
```
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Phil Wang · United States
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1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d722b22a4942030c, topic:deep-learning
matched fp:d722b22a4942030c, topic:computer-vision