timm
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Image Classification
timm
PyTorch
Safetensors
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Model card for mambaout_small.in1k

A MambaOut image classification model. Pretrained on ImageNet-1k by paper authors.

Model Details

Model Usage

Image Classification

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_small.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

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_small.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, 96])
    #  torch.Size([1, 28, 28, 192])
    #  torch.Size([1, 14, 14, 384])
    #  torch.Size([1, 7, 7, 576])

    print(o.shape)

Image Embeddings

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_small.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, 576) 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 384 87.506 98.428 101.66
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 224 86.632 98.156 101.66
mambaout_base_tall_rw.sw_e500_in1k 288 84.974 97.332 86.48
mambaout_base_wide_rw.sw_e500_in1k 288 84.962 97.208 94.45
mambaout_base_short_rw.sw_e500_in1k 288 84.832 97.27 88.83
mambaout_base.in1k 288 84.72 96.93 84.81
mambaout_small_rw.sw_e450_in1k 288 84.598 97.098 48.5
mambaout_small.in1k 288 84.5 96.974 48.49
mambaout_base_wide_rw.sw_e500_in1k 224 84.454 96.864 94.45
mambaout_base_tall_rw.sw_e500_in1k 224 84.434 96.958 86.48
mambaout_base_short_rw.sw_e500_in1k 224 84.362 96.952 88.83
mambaout_base.in1k 224 84.168 96.68 84.81
mambaout_small.in1k 224 84.086 96.63 48.49
mambaout_small_rw.sw_e450_in1k 224 84.024 96.752 48.5
mambaout_tiny.in1k 288 83.448 96.538 26.55
mambaout_tiny.in1k 224 82.736 96.1 26.55
mambaout_kobe.in1k 288 81.054 95.718 9.14
mambaout_kobe.in1k 224 79.986 94.986 9.14
mambaout_femto.in1k 288 79.848 95.14 7.3
mambaout_femto.in1k 224 78.87 94.408 7.3

Citation

@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}
}
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Dataset used to train timm/mambaout_small.in1k