Larq Zoo Pretrained Models

Larq Zoo provides reference implementations of deep neural networks with extremely low precision weights and activations that are made available alongside pre-trained weights. These models can be used for prediction, feature extraction, and fine-tuning.

The code for all models including a reproducible training pipeline is available at larq/zoo.

We believe that a collection of tested implementations with pretrained weights is greatly beneficial for the field of Extremely Quantized Neural Networks. To improve reproducibility we have implemented a few commonly used models found in the literature. If you have developed or reimplemented a Binarized or other Extremely Quantized Neural Network and want to share it with the community such that future papers can build on top of your work, please add it to Larq Zoo or get in touch with us if you need any help.

Available models

The following models are trained on the ImageNet dataset. The Top-1 and Top-5 accuracy refers to the model's performance on the ImageNet validation dataset, memory refers to the memory after quantization of the weights.

The model definitions and the train loop are available in the Larq Zoo repository.

Model Top-1 Accuracy Top-5 Accuracy Parameters Memory
BinaryDenseNet45 64.59 % 85.21 % 13 939 240 7.54 MB
BinaryDenseNet37Dilated 64.34 % 85.15 % 8 734 120 5.25 MB
BinaryDenseNet37 62.89 % 84.19 % 8 734 120 5.25 MB
BinaryDenseNet28 60.91 % 82.83 % 5 150 504 4.12 MB
BinaryResNetE18 58.32 % 80.79 % 11 699 368 4.03 MB
Bi-Real Net 57.47 % 79.84 % 11 699 112 4.03 MB
DoReFaNet 53.39 % 76.50 % 62 403 912 22.84 MB
XNOR-Net 44.96 % 69.18 % 62 396 768 22.81 MB
Binary AlexNet 36.30 % 61.53 % 61 859 192 7.49 MB


Larq Zoo is not included in Larq by default. To start using it, you can install it with Python's pip package manager:

pip install larq-zoo

Weights can be downloaded automatically when instantiating a model. They are stored at ~/.larq/models/.

Training Models from Scratch

Larq Zoo ships with a command-line interface powered by zookeeper, allowing you to reproduce the entire training process. If you want to improve an existing model or implement your own, we recommend to installing Larq Zoo in development mode.

E.g. to reproduce the training of Binary AlexNet run:

lqz train binary_alexnet --dataset imagenet2012 --dataset-version 5.0.0

To experiment with different hyperparameters you can either edit the HParams for this model or overwrite them from the command line, e.g.:

lqz train binary_alexnet --dataset imagenet2012 --dataset-version 5.0.0 --hparams epochs=150,batch_size=256

To view a TensorBoard for the current training, replace the lqz train command with lqz tensorboard.

For all available commands and options run lqz --help or lqz train --help or checkout the documentation of zookeeper if you want to implement your model for Larq Zoo.