Contrastive Language-Image Pretraining

Related tags

Deep LearningCLIP
Overview

CLIP

[Blog] [Paper] [Model Card] [Colab]

CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision.

Approach

CLIP

Usage

First, install PyTorch 1.7.1 and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick:

$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
$ pip install ftfy regex tqdm
$ pip install git+https://github.com/openai/CLIP.git

Replace cudatoolkit=11.0 above with the appropriate CUDA version on your machine or cpuonly when installing on a machine without a GPU.

import torch
import clip
from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)

with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    
    logits_per_image, logits_per_text = model(image, text)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("Label probs:", probs)  # prints: [[0.9927937  0.00421068 0.00299572]]

API

The CLIP module clip provides the following methods:

clip.available_models()

Returns the names of the available CLIP models.

clip.load(name, device=..., jit=False)

Returns the model and the TorchVision transform needed by the model, specified by the model name returned by clip.available_models(). It will download the model as necessary. The name argument can also be a path to a local checkpoint.

The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. When jit is False, a non-JIT version of the model will be loaded.

clip.tokenize(text: Union[str, List[str]], context_length=77)

Returns a LongTensor containing tokenized sequences of given text input(s). This can be used as the input to the model


The model returned by clip.load() supports the following methods:

model.encode_image(image: Tensor)

Given a batch of images, returns the image features encoded by the vision portion of the CLIP model.

model.encode_text(text: Tensor)

Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model.

model(image: Tensor, text: Tensor)

Given a batch of images and a batch of text tokens, returns two Tensors, containing the logit scores corresponding to each image and text input. The values are cosine similarities between the corresponding image and text features, times 100.

More Examples

Zero-Shot Prediction

The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. This example takes an image from the CIFAR-100 dataset, and predicts the most likely labels among the 100 textual labels from the dataset.

import os
import clip
import torch
from torchvision.datasets import CIFAR100

# Load the model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load('ViT-B/32', device)

# Download the dataset
cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)

# Prepare the inputs
image, class_id = cifar100[3637]
image_input = preprocess(image).unsqueeze(0).to(device)
text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)

# Calculate features
with torch.no_grad():
    image_features = model.encode_image(image_input)
    text_features = model.encode_text(text_inputs)

# Pick the top 5 most similar labels for the image
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
values, indices = similarity[0].topk(5)

# Print the result
print("\nTop predictions:\n")
for value, index in zip(values, indices):
    print(f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%")

The output will look like the following (the exact numbers may be slightly different depending on the compute device):

Top predictions:

           snake: 65.31%
          turtle: 12.29%
    sweet_pepper: 3.83%
          lizard: 1.88%
       crocodile: 1.75%

Note that this example uses the encode_image() and encode_text() methods that return the encoded features of given inputs.

Linear-probe evaluation

The example below uses scikit-learn to perform logistic regression on image features.

import os
import clip
import torch

import numpy as np
from sklearn.linear_model import LogisticRegression
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR100
from tqdm import tqdm

# Load the model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load('ViT-B/32', device)

# Load the dataset
root = os.path.expanduser("~/.cache")
train = CIFAR100(root, download=True, train=True, transform=preprocess)
test = CIFAR100(root, download=True, train=False, transform=preprocess)


def get_features(dataset):
    all_features = []
    all_labels = []
    
    with torch.no_grad():
        for images, labels in tqdm(DataLoader(dataset, batch_size=100)):
            features = model.encode_image(images.to(device))

            all_features.append(features)
            all_labels.append(labels)

    return torch.cat(all_features).cpu().numpy(), torch.cat(all_labels).cpu().numpy()

# Calculate the image features
train_features, train_labels = get_features(train)
test_features, test_labels = get_features(test)

# Perform logistic regression
classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1)
classifier.fit(train_features, train_labels)

# Evaluate using the logistic regression classifier
predictions = classifier.predict(test_features)
accuracy = np.mean((test_labels == predictions).astype(np.float)) * 100.
print(f"Accuracy = {accuracy:.3f}")

Note that the C value should be determined via a hyperparameter sweep using a validation split.

Owner
OpenAI
OpenAI
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat

Hanzhe Hu 99 Dec 12, 2022
GNN-based Recommendation Benchmark

GRecX A Fair Benchmark for GNN-based Recommendation Homepage and Documentation Homepage: Documentation: Paper: GRecX: An Efficient and Unified Benchma

73 Oct 17, 2022
Bottom-up Human Pose Estimation

Introduction This is the official code of Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation. This paper has been accepted to CVPR2

108 Dec 01, 2022
Tool which allow you to detect and translate text.

Text detection and recognition This repository contains tool which allow to detect region with text and translate it one by one. Description Two pretr

Damian Panek 176 Nov 28, 2022
Flax is a neural network ecosystem for JAX that is designed for flexibility.

Flax: A neural network library and ecosystem for JAX designed for flexibility Overview | Quick install | What does Flax look like? | Documentation See

Google 3.9k Jan 02, 2023
DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation

DFFNet Paper DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation. Xiangyan Tang, Wenxuan Tu, Keqiu Li, J

4 Sep 23, 2022
A Comparative Framework for Multimodal Recommender Systems

Cornac Cornac is a comparative framework for multimodal recommender systems. It focuses on making it convenient to work with models leveraging auxilia

Preferred.AI 671 Jan 03, 2023
Code for NeurIPS 2021 paper: Invariant Causal Imitation Learning for Generalizable Policies

Invariant Causal Imitation Learning for Generalizable Policies Ioana Bica, Daniel Jarrett, Mihaela van der Schaar Neural Information Processing System

Ioana Bica 17 Dec 01, 2022
CycleTransGAN-EVC: A CycleGAN-based Emotional Voice Conversion Model with Transformer

CycleTransGAN-EVC CycleTransGAN-EVC: A CycleGAN-based Emotional Voice Conversion Model with Transformer Demo emotion CycleTransGAN CycleTransGAN Cycle

24 Dec 15, 2022
A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking.

BeatNet A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking. This repository

Mojtaba Heydari 157 Dec 27, 2022
Efficient Speech Processing Tookit for Automatic Speaker Recognition

Sugar Efficient Speech Processing Tookit for Automatic Speaker Recognition | HuggingFace | What's New EfficientTDNN: Efficient Architecture Search for

WangRui 14 Sep 14, 2022
PaddleBoBo是基于PaddlePaddle和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目

PaddleBoBo - 元宇宙时代,你也可以动手做一个虚拟主播。 PaddleBoBo是基于飞桨PaddlePaddle深度学习框架和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目。PaddleBoBo致力于简单高效、可复用性强,只需要一张带人像的图片和一段文字,就能

502 Jan 08, 2023
Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021) Authors: Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song Link to pap

Xinshi Chen 2 Dec 20, 2021
这是一个yolox-pytorch的源码,可以用于训练自己的模型。

YOLOX:You Only Look Once目标检测模型在Pytorch当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤

Bubbliiiing 613 Jan 05, 2023
This repository builds a basic vision transformer from scratch so that one beginner can understand the theory of vision transformer.

vision-transformer-from-scratch This repository includes several kinds of vision transformers from scratch so that one beginner can understand the the

1 Dec 24, 2021
Code for paper "Multi-level Disentanglement Graph Neural Network"

Multi-level Disentanglement Graph Neural Network (MD-GNN) This is a PyTorch implementation of the MD-GNN, and the code includes the following modules:

Lirong Wu 6 Dec 29, 2022
Header-only library for using Keras models in C++.

frugally-deep Use Keras models in C++ with ease Table of contents Introduction Usage Performance Requirements and Installation FAQ Introduction Would

Tobias Hermann 927 Jan 05, 2023
Deep Learning Head Pose Estimation using PyTorch.

Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.

Nataniel Ruiz 1.3k Dec 26, 2022
Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Colour Detection On Image Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely e

Astitva Veer Garg 1 Jan 13, 2022
Code for 1st place solution in Sleep AI Challenge SNU Hospital

Sleep AI Challenge SNU Hospital 2021 Code for 1st place solution for Sleep AI Challenge (Note that the code is not fully organized) Refer to the notio

Saewon Yang 13 Jan 03, 2022