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convnextv2_large.fcmae

Image Feature Extraction
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metadata
license: cc-by-nc-4.0
library_name: timm
tags:
  - image-feature-extraction
  - timm

Model card for convnextv2_large.fcmae

A ConvNeXt-V2 self-supervised feature representation model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE). This model has no pretrained head and is only useful for fine-tune or feature extraction.

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('convnextv2_large.fcmae', 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(
    'convnextv2_large.fcmae',
    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, 192, 56, 56])
    #  torch.Size([1, 384, 28, 28])
    #  torch.Size([1, 768, 14, 14])
    #  torch.Size([1, 1536, 7, 7])

    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(
    'convnextv2_large.fcmae',
    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, 1536, 7, 7) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

Model Comparison

Explore the dataset and runtime metrics of this model in timm model results.

All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

model top1 top5 img_size param_count gmacs macts samples_per_sec batch_size
convnextv2_huge.fcmae_ft_in22k_in1k_512 88.848 98.742 512 660.29 600.81 413.07 28.58 48
convnextv2_huge.fcmae_ft_in22k_in1k_384 88.668 98.738 384 660.29 337.96 232.35 50.56 64
convnext_xxlarge.clip_laion2b_soup_ft_in1k 88.612 98.704 256 846.47 198.09 124.45 122.45 256
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384 88.312 98.578 384 200.13 101.11 126.74 196.84 256
convnextv2_large.fcmae_ft_in22k_in1k_384 88.196 98.532 384 197.96 101.1 126.74 128.94 128
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320 87.968 98.47 320 200.13 70.21 88.02 283.42 256
convnext_xlarge.fb_in22k_ft_in1k_384 87.75 98.556 384 350.2 179.2 168.99 124.85 192
convnextv2_base.fcmae_ft_in22k_in1k_384 87.646 98.422 384 88.72 45.21 84.49 209.51 256
convnext_large.fb_in22k_ft_in1k_384 87.476 98.382 384 197.77 101.1 126.74 194.66 256
convnext_large_mlp.clip_laion2b_augreg_ft_in1k 87.344 98.218 256 200.13 44.94 56.33 438.08 256
convnextv2_large.fcmae_ft_in22k_in1k 87.26 98.248 224 197.96 34.4 43.13 376.84 256
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384 87.138 98.212 384 88.59 45.21 84.49 365.47 256
convnext_xlarge.fb_in22k_ft_in1k 87.002 98.208 224 350.2 60.98 57.5 368.01 256
convnext_base.fb_in22k_ft_in1k_384 86.796 98.264 384 88.59 45.21 84.49 366.54 256
convnextv2_base.fcmae_ft_in22k_in1k 86.74 98.022 224 88.72 15.38 28.75 624.23 256
convnext_large.fb_in22k_ft_in1k 86.636 98.028 224 197.77 34.4 43.13 581.43 256
convnext_base.clip_laiona_augreg_ft_in1k_384 86.504 97.97 384 88.59 45.21 84.49 368.14 256
convnext_base.clip_laion2b_augreg_ft_in12k_in1k 86.344 97.97 256 88.59 20.09 37.55 816.14 256
convnextv2_huge.fcmae_ft_in1k 86.256 97.75 224 660.29 115.0 79.07 154.72 256
convnext_small.in12k_ft_in1k_384 86.182 97.92 384 50.22 25.58 63.37 516.19 256
convnext_base.clip_laion2b_augreg_ft_in1k 86.154 97.68 256 88.59 20.09 37.55 819.86 256
convnext_base.fb_in22k_ft_in1k 85.822 97.866 224 88.59 15.38 28.75 1037.66 256
convnext_small.fb_in22k_ft_in1k_384 85.778 97.886 384 50.22 25.58 63.37 518.95 256
convnextv2_large.fcmae_ft_in1k 85.742 97.584 224 197.96 34.4 43.13 375.23 256
convnext_small.in12k_ft_in1k 85.174 97.506 224 50.22 8.71 21.56 1474.31 256
convnext_tiny.in12k_ft_in1k_384 85.118 97.608 384 28.59 13.14 39.48 856.76 256
convnextv2_tiny.fcmae_ft_in22k_in1k_384 85.112 97.63 384 28.64 13.14 39.48 491.32 256
convnextv2_base.fcmae_ft_in1k 84.874 97.09 224 88.72 15.38 28.75 625.33 256
convnext_small.fb_in22k_ft_in1k 84.562 97.394 224 50.22 8.71 21.56 1478.29 256
convnext_large.fb_in1k 84.282 96.892 224 197.77 34.4 43.13 584.28 256
convnext_tiny.in12k_ft_in1k 84.186 97.124 224 28.59 4.47 13.44 2433.7 256
convnext_tiny.fb_in22k_ft_in1k_384 84.084 97.14 384 28.59 13.14 39.48 862.95 256
convnextv2_tiny.fcmae_ft_in22k_in1k 83.894 96.964 224 28.64 4.47 13.44 1452.72 256
convnext_base.fb_in1k 83.82 96.746 224 88.59 15.38 28.75 1054.0 256
convnextv2_nano.fcmae_ft_in22k_in1k_384 83.37 96.742 384 15.62 7.22 24.61 801.72 256
convnext_small.fb_in1k 83.142 96.434 224 50.22 8.71 21.56 1464.0 256
convnextv2_tiny.fcmae_ft_in1k 82.92 96.284 224 28.64 4.47 13.44 1425.62 256
convnext_tiny.fb_in22k_ft_in1k 82.898 96.616 224 28.59 4.47 13.44 2480.88 256
convnext_nano.in12k_ft_in1k 82.282 96.344 224 15.59 2.46 8.37 3926.52 256
convnext_tiny_hnf.a2h_in1k 82.216 95.852 224 28.59 4.47 13.44 2529.75 256
convnext_tiny.fb_in1k 82.066 95.854 224 28.59 4.47 13.44 2346.26 256
convnextv2_nano.fcmae_ft_in22k_in1k 82.03 96.166 224 15.62 2.46 8.37 2300.18 256
convnextv2_nano.fcmae_ft_in1k 81.83 95.738 224 15.62 2.46 8.37 2321.48 256
convnext_nano_ols.d1h_in1k 80.866 95.246 224 15.65 2.65 9.38 3523.85 256
convnext_nano.d1h_in1k 80.768 95.334 224 15.59 2.46 8.37 3915.58 256
convnextv2_pico.fcmae_ft_in1k 80.304 95.072 224 9.07 1.37 6.1 3274.57 256
convnext_pico.d1_in1k 79.526 94.558 224 9.05 1.37 6.1 5686.88 256
convnext_pico_ols.d1_in1k 79.522 94.692 224 9.06 1.43 6.5 5422.46 256
convnextv2_femto.fcmae_ft_in1k 78.488 93.98 224 5.23 0.79 4.57 4264.2 256
convnext_femto_ols.d1_in1k 77.86 93.83 224 5.23 0.82 4.87 6910.6 256
convnext_femto.d1_in1k 77.454 93.68 224 5.22 0.79 4.57 7189.92 256
convnextv2_atto.fcmae_ft_in1k 76.664 93.044 224 3.71 0.55 3.81 4728.91 256
convnext_atto_ols.a2_in1k 75.88 92.846 224 3.7 0.58 4.11 7963.16 256
convnext_atto.d2_in1k 75.664 92.9 224 3.7 0.55 3.81 8439.22 256

Citation 

@article{Woo2023ConvNeXtV2,
  title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
  author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
  year={2023},
  journal={arXiv preprint arXiv:2301.00808},
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}