TinyCLIP ViT 61M 32 Text 29M LAION400M EU AI Act Compliance Profile

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-61M-32-Text-29M-LAION400M")
processor = CLIPProcessor.from_pretrained("wkcn/TinyCLIP-ViT-61M-32-Text-29M-LAION400M")

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 | |-

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