--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for mambaout_base.in1k A MambaOut image classification model. Pretrained on ImageNet-1k by paper authors. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 84.8 - GMACs: 15.8 - Activations (M): 36.9 - Image size: train = 224 x 224, test = 288 x 288 - **Dataset:** ImageNet-1k - **Papers:** - MambaOut: Do We Really Need Mamba for Vision?: https://arxiv.org/abs/2405.07992 - **Original:** https://github.com/yuweihao/MambaOut ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('mambaout_base.in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'mambaout_base.in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 56, 56, 128]) # torch.Size([1, 28, 28, 256]) # torch.Size([1, 14, 14, 512]) # torch.Size([1, 7, 7, 768]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'mambaout_base.in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 7, 7, 768) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison ### By Top-1 |model |img_size|top1 |top5 |param_count| |---------------------------------------------------------------------------------------------------------------------|--------|------|------|-----------| |[mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k)|384 |87.506|98.428|101.66 | |[mambaout_base_plus_rw.sw_e150_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_in12k_ft_in1k)|288 |86.912|98.236|101.66 | |[mambaout_base_plus_rw.sw_e150_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_in12k_ft_in1k)|224 |86.632|98.156|101.66 | |[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k) |288 |84.974|97.332|86.48 | |[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k) |288 |84.962|97.208|94.45 | |[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k) |288 |84.832|97.27 |88.83 | |[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k) |288 |84.72 |96.93 |84.81 | |[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k) |288 |84.598|97.098|48.5 | |[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k) |288 |84.5 |96.974|48.49 | |[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k) |224 |84.454|96.864|94.45 | |[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k) |224 |84.434|96.958|86.48 | |[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k) |224 |84.362|96.952|88.83 | |[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k) |224 |84.168|96.68 |84.81 | |[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k) |224 |84.086|96.63 |48.49 | |[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k) |224 |84.024|96.752|48.5 | |[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k) |288 |83.448|96.538|26.55 | |[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k) |224 |82.736|96.1 |26.55 | |[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k) |288 |81.054|95.718|9.14 | |[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k) |224 |79.986|94.986|9.14 | |[mambaout_femto.in1k](http://huggingface.co/timm/mambaout_femto.in1k) |288 |79.848|95.14 |7.3 | |[mambaout_femto.in1k](http://huggingface.co/timm/mambaout_femto.in1k) |224 |78.87 |94.408|7.3 | ## Citation ```bibtex @article{yu2024mambaout, title={MambaOut: Do We Really Need Mamba for Vision?}, author={Yu, Weihao and Wang, Xinchao}, journal={arXiv preprint arXiv:2405.07992}, year={2024} } ```