A simpler Pytorch + Zeta Implementation of the paper: "SiMBA: Simplified Mamba-based Architecture for Vision and Multivariate Time series"
$ pip install simba-torch
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)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
MIT
- Add paper link
- Add citation bibtex
- cleanup