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

Name: TinyCLIP-ViT-8M-16-Text-3M-YFCC15M
Availability: InStock

zero-shot-image-classification

tinyclip

mit

endpoints_compatible

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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

python

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! :)

bibtex

@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

License