Edit model card

TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance

[ICCV 2023] - TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance

TinyCLIP is a novel cross-modal distillation method for large-scale language-image pre-trained models. The method introduces two core techniques: affinity mimicking and weight inheritance. This work unleashes the capacity of small CLIP models, fully leveraging large-scale models as well as pre-training data and striking the best trade-off between speed and accuracy.

Use with Transformers

from PIL import Image
import requests

from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M")
processor = CLIPProcessor.from_pretrained("wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities

Highlights

  • TinyCLIP ViT-45M/32 uses only half parameters of ViT-B/32 to achieves comparable zero-shot performance.
  • TinyCLIP ResNet-19M reduces the parameters by 50% while getting 2x inference speedup, and obtains 56.4% accuracy on ImageNet.

Model Zoo

Model Weight inheritance Pretrain IN-1K Acc@1(%) MACs(G) Throughput(pairs/s) Link
TinyCLIP ViT-39M/16 Text-19M manual YFCC-15M 63.5 9.5 1,469 Model
TinyCLIP ViT-8M/16 Text-3M manual YFCC-15M 41.1 2.0 4,150 Model
TinyCLIP ResNet-30M Text-29M manual LAION-400M 59.1 6.9 1,811 Model
TinyCLIP ResNet-19M Text-19M manual LAION-400M 56.4 4.4 3,024 Model
TinyCLIP ViT-61M/32 Text-29M manual LAION-400M 62.4 5.3 3,191 Model
TinyCLIP ViT-40M/32 Text-19M manual LAION-400M 59.8 3.5 4,641 Model
TinyCLIP ViT-63M/32 Text-31M auto LAION-400M 63.9 5.6 2,905 Model
TinyCLIP ViT-45M/32 Text-18M auto LAION-400M 61.4 3.7 3,682 Model
TinyCLIP ViT-22M/32 Text-10M auto LAION-400M 53.7 1.9 5,504 Model
TinyCLIP ViT-63M/32 Text-31M auto LAION+YFCC-400M 64.5 5.6 2,909 Model
TinyCLIP ViT-45M/32 Text-18M auto LAION+YFCC-400M 62.7 1.9 3,685 Model

Note: The configs of models with auto inheritance are generated automatically.

Official PyTorch Implementation

https://github.com/microsoft/Cream/tree/main/TinyCLIP

Citation

If this repo is helpful for you, please consider to cite it. :mega: Thank you! :)

@InProceedings{tinyclip,
    title     = {TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance},
    author    = {Wu, Kan and Peng, Houwen and Zhou, Zhenghong and Xiao, Bin and Liu, Mengchen and Yuan, Lu and Xuan, Hong and Valenzuela, Michael and Chen, Xi (Stephen) and Wang, Xinggang and Chao, Hongyang and Hu, Han},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {21970-21980}
}

Acknowledge

Our code is based on CLIP, OpenCLIP, CoFi and PyTorch. Thank contributors for their awesome contribution!

License

Downloads last month
5,539
Safetensors
Model size
23.4M params
Tensor type
I64
·
F32
·

Collection including wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M