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convnext_base.clip_laiona_augreg_ft_in1k_384

Image Classification
timm
PyTorch
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Model card for convnext_base.clip_laiona_augreg_ft_in1k_384

A ConvNeXt image classification model. CLIP image tower weights pretrained in OpenCLIP on LAION and fine-tuned on ImageNet-1k in timm by Ross Wightman.

Please see related OpenCLIP model cards for more details on pretrain:

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('convnext_base.clip_laiona_augreg_ft_in1k_384', 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(
    'convnext_base.clip_laiona_augreg_ft_in1k_384',
    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, 128, 96, 96])
    #  torch.Size([1, 256, 48, 48])
    #  torch.Size([1, 512, 24, 24])
    #  torch.Size([1, 1024, 12, 12])

    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(
    'convnext_base.clip_laiona_augreg_ft_in1k_384',
    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, 1024, 12, 12) 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 

@software{ilharco_gabriel_2021_5143773,
  author       = {Ilharco, Gabriel and
                  Wortsman, Mitchell and
                  Wightman, Ross and
                  Gordon, Cade and
                  Carlini, Nicholas and
                  Taori, Rohan and
                  Dave, Achal and
                  Shankar, Vaishaal and
                  Namkoong, Hongseok and
                  Miller, John and
                  Hajishirzi, Hannaneh and
                  Farhadi, Ali and
                  Schmidt, Ludwig},
  title        = {OpenCLIP},
  month        = jul,
  year         = 2021,
  note         = {If you use this software, please cite it as below.},
  publisher    = {Zenodo},
  version      = {0.1},
  doi          = {10.5281/zenodo.5143773},
  url          = {https://doi.org/10.5281/zenodo.5143773}
}

@inproceedings{schuhmann2022laionb,
  title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
  author={Christoph Schuhmann and
          Romain Beaumont and
          Richard Vencu and
          Cade W Gordon and
          Ross Wightman and
          Mehdi Cherti and
          Theo Coombes and
          Aarush Katta and
          Clayton Mullis and
          Mitchell Wortsman and
          Patrick Schramowski and
          Srivatsa R Kundurthy and
          Katherine Crowson and
          Ludwig Schmidt and
          Robert Kaczmarczyk and
          Jenia Jitsev},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2022},
  url={https://openreview.net/forum?id=M3Y74vmsMcY}
}

@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}}
}

@inproceedings{Radford2021LearningTV,
  title={Learning Transferable Visual Models From Natural Language Supervision},
  author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
  booktitle={ICML}, 
  year={2021}
}

@article{liu2022convnet,
  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title   = {A ConvNet for the 2020s},
  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2022},
}
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Dataset used to train timm/convnext_base.clip_laiona_augreg_ft_in1k_384