mmpretrain/configs/clip/README.md

CLIP

Learning Transferable Visual Models From Natural Language Supervision

Abstract

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at this https URL.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Test Command

Prepare your dataset according to the docs.

Test:

python tools/test.py configs/clip/vit-base-p32_pt-64xb64_in1k.py https://download.openmmlab.com/mmclassification/v0/clip/clip-vit-base-p32_laion2b-in12k-pre_3rdparty_in1k_20221220-b384e830.pth

Models and results

Image Classification on ImageNet-1k

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	Config	Download
vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k*	CLIP LAION2B ImageNet-12k	88.22	4.36	83.06	96.49	config	model
vit-base-p32_clip-laion2b-pre_3rdparty_in1k*	CLIP LAION2B	88.22	4.36	82.46	96.12	config	model
vit-base-p32_clip-openai-pre_3rdparty_in1k*	CLIP OPENAI	88.22	4.36	81.77	95.89	config	model
vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k-384px*	CLIP LAION2B ImageNet-12k	88.22	12.66	85.39	97.67	config	model
vit-base-p32_clip-openai-in12k-pre_3rdparty_in1k-384px*	CLIP OPENAI ImageNet-12k	88.22	12.66	85.13	97.42	config	model
vit-base-p16_clip-laion2b-in12k-pre_3rdparty_in1k*	CLIP LAION2B ImageNet-12k	86.57	16.86	86.02	97.76	config	model
vit-base-p16_clip-laion2b-pre_3rdparty_in1k*	CLIP LAION2B	86.57	16.86	85.49	97.59	config	model
vit-base-p16_clip-openai-in12k-pre_3rdparty_in1k*	CLIP OPENAI ImageNet-12k	86.57	16.86	85.99	97.72	config	model
vit-base-p16_clip-openai-pre_3rdparty_in1k*	CLIP OPENAI	86.57	16.86	85.30	97.50	config	model
vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k-448px*	CLIP LAION2B ImageNet-12k	88.22	17.20	85.76	97.63	config	model
vit-base-p16_clip-laion2b-in12k-pre_3rdparty_in1k-384px*	CLIP LAION2B ImageNet-12k	86.57	49.37	87.17	98.02	config	model
vit-base-p16_clip-laion2b-pre_3rdparty_in1k-384px*	CLIP LAION2B	86.57	49.37	86.52	97.97	config	model
vit-base-p16_clip-openai-in12k-pre_3rdparty_in1k-384px*	CLIP OPENAI ImageNet-12k	86.57	49.37	86.87	98.05	config	model
vit-base-p16_clip-openai-pre_3rdparty_in1k-384px*	CLIP OPENAI	86.57	49.37	86.25	97.90	config	model

Models with * are converted from the timm. The config files of these models are only for inference. We haven't reprodcue the training results.

Citation

@InProceedings{pmlr-v139-radford21a,
title = {Learning Transferable Visual Models From Natural Language Supervision},
author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
year = {2021},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
}