saifulislamsarfaraz/mambavision

Official PyTorch implementation of MambaVision: A Hybrid Mamba-Transformer Vision Backbone

MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Official PyTorch implementation of MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

Ali Hatamizadeh and Jan Kautz.

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing

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MambaVision demonstrates a strong performance by achieving a new SOTA Pareto-front in terms of Top-1 accuracy and throughput.

We introduce a novel mixer block by creating a symmetric path without SSM to enhance the modeling of global context:

MambaVision has a hierarchial architecture that employs both self-attention and mixer blocks:

💥 News 💥

• [07.12.2024] Paper is now available on arXiv !

• [07.11.2024] Mambavision pip package is released !

• [07.10.2024] We have released the code and model checkpoints for Mambavision !

Quick Start

Classification

We can import pre-trained MambaVision models with 1 line of code:

pip install mambavision

A pretrained MambaVision model with default hyper-parameters can be created as in:

>>> from mambavision import create_model

# Define mamba_vision_T model with 224 x 224 resolution

>>> model = create_model('mamba_vision_T', pretrained=True, model_path="/tmp/mambavision_tiny_1k.pth.tar")

Available list of pretrained models include mamba_vision_T, mamba_vision_T2, mamba_vision_S, mamba_vision_B, mamba_vision_L and mamba_vision_L2.

We can also simply test the model by passing a dummy input image. The output is the logits:

>>> import torch

>>> image = torch.rand(1, 3, 224, 224).cuda() # place image on cuda
>>> model = model.cuda() # place model on cuda
>>> output = model(image) # output logit size is [1, 1000]

Using the pretrained models from our pip package, you can simply run validation:

python validate_pip_model.py --model mamba_vision_T --data_dir=$DATA_PATH --batch-size $BS 

FAQ

1. Can I apply MambaVision for downstream tasks like detection, segmentation ?

Yes ! we are working to have it released very soon. But employing MambaVision backbones for these tasks is very similar to other models in mmseg or mmdet packages.

2. I am interested in re-implementing MambaVision in my own repository. Can we use the pretrained weights ?

Yes ! the pretrained weights are released under CC-BY-NC-SA-4.0. Please submit an issue in this repo and we will add your repository to the README of our codebase and properly acknowledge your efforts.

Results + Pretrained Models

ImageNet-1K

MambaVision ImageNet-1K Pretrained Models

Name	Acc@1(%)	Acc@5(%)	Throughput(Img/Sec)	Resolution	#Params(M)	FLOPs(G)	Download
MambaVision-T	82.3	96.2	6298	224x224	31.8	4.4	model
MambaVision-T2	82.7	96.3	5990	224x224	35.1	5.1	model
MambaVision-S	83.3	96.5	4700	224x224	50.1	7.5	model
MambaVision-B	84.2	96.9	3670	224x224	97.7	15.0	model
MambaVision-L	85.0	97.1	2190	224x224	227.9	34.9	model
MambaVision-L2	85.3	97.2	1021	224x224	241.5	37.5	model

Installation

We provide a docker file. In addition, assuming that a recent PyTorch package is installed, the dependencies can be installed by running:

pip install -r requirements.txt

Evaluation

The MambaVision models can be evaluated on ImageNet-1K validation set using the following:

python validate.py \
--model <model-name>
--checkpoint <checkpoint-path>
--data_dir <imagenet-path>
--batch-size <batch-size-per-gpu

Here --model is the MambaVision variant (e.g. mambavision_tiny_1k), --checkpoint is the path to pretrained model weights, --data_dir is the path to ImageNet-1K validation set and --batch-size is the number of batch size. We also provide a sample script here.

Star History

Licenses

Copyright © 2024, NVIDIA Corporation. All rights reserved.

This work is made available under the NVIDIA Source Code License-NC. Click here to view a copy of this license.

The pre-trained models are shared under CC-BY-NC-SA-4.0. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

For license information regarding the timm repository, please refer to its repository.

For license information regarding the ImageNet dataset, please see the ImageNet official website.

Acknowledgement

This repository is built on top of the timm repository. We thank Ross Wrightman for creating and maintaining this high-quality library.