CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

Last update: Jan 07, 2023

Overview

CLIP (Contrastive Language–Image Pre-training)

Experiments (Evaluation)

Model	Dataset	Acc (%)
ViT-B/32 (Paper)	CIFAR100	65.1
ViT-B/32 (Our)	CIFAR100	61.71
ViT-B/32 (Paper	CIFAR10	91.3
ViT-B/32 (Our)	CIFAR10	88.8

Overview

Training

Work In Process

Usage

Evaluation

python evaluation.py --dataset CIFAR100 --cuda True

args
- dataset (str): CIFAR10, CIFAR100 (default: CIFAR100)
- num_workers (int): default: 0
- batch_size (int): default: 128
- cuda (bool): False
Training
- Prepare Data
  - Visual Genome Dataset link
  - Download (images, region descriptions)
- training
```
python main.py --base_dir ./ --cuda True
```

Reference

paper link
Author: Alec Radford, Jong Wook Kim, Chris Hallacy, Girish Sastry, Amanda Askell, Pamela Mishkin, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Jack Clark, Gretchen Krueger, Ilya Sutskever
OpenAI

Owner

Myeongjun Kim

Computer Vision Research using Deep Learning

GitHub Repository

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CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

Related tags

Overview

CLIP (Contrastive Language–Image Pre-training)

Experiments (Evaluation)

Overview

Training

Usage

Reference

Owner

Myeongjun Kim

TLDR; Train custom adaptive filter optimizers without hand tuning or extra labels.

NAVER BoostCamp Final Project

Official implementation of ACTION-Net: Multipath Excitation for Action Recognition (CVPR'21).

Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

Generate indoor scenes with Transformers

Understanding the Generalization Benefit of Model Invariance from a Data Perspective

LAnguage Model Analysis

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Code for "Solving Graph-based Public Good Games with Tree Search and Imitation Learning"

Citation Intent Classification in scientific papers using the Scicite dataset an Pytorch

Driller: augmenting AFL with symbolic execution!

SegNet model implemented using keras framework

DI-smartcross - Decision Intelligence Platform for Traffic Crossing Signal Control

Learning from graph data using Keras

Model serving at scale

Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)

IhoneyBakFileScan Modify - 批量网站备份文件扫描器，增加文件规则，优化内存占用

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

PyAF is an Open Source Python library for Automatic Time Series Forecasting built on top of popular pydata modules.

List of content farm sites like g.penzai.com.

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.