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Simba

graph A simpler Pytorch + Zeta Implementation of the paper: "SiMBA: Simplified Mamba-based Architecture for Vision and Multivariate Time series"

install

$ pip install simba-torch

usage

import torch 
from simba_torch.main import Simba

# Forward pass with images
img = torch.randn(1, 3, 224, 224)

# Create model
model = Simba(
    dim = 4,                # Dimension of the transformer
    dropout = 0.1,          # Dropout rate for regularization
    d_state=64,             # Dimension of the transformer state
    d_conv=64,              # Dimension of the convolutional layers
    num_classes=64,         # Number of output classes
    depth=8,                # Number of transformer layers
    patch_size=16,          # Size of the image patches
    image_size=224,         # Size of the input image
    channels=3,             # Number of input channels
    # use_pos_emb=True # If you want
)

# Forward pass
out = model(img)
print(out.shape)

Train

Dependencies: download and extract the datasets through wget wget http://images.cocodataset.org/zips/train2017.zip -O coco_train2017.zip wget http://images.cocodataset.org/zips/val2017.zip -O coco_val2017.zip wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip -O coco_ann2017.zip

Then run the following script: python3 train.py

License

MIT

Todo

  • Add paper link
  • Add citation bibtex
  • cleanup

simba's People

Contributors

kyegomez avatar dependabot[bot] avatar wisebreadloaf avatar

Stargazers

 avatar  avatar Kasturi Gnanaguna Sagar avatar  avatar  avatar Tony Davis avatar Yuhao Wang  avatar  avatar Lau Van Kiet avatar  avatar zzp avatar Zhangtao Cheng avatar  avatar dongwiki avatar Amirreza salimi avatar Tao Hu avatar

Watchers

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simba's Issues

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