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

Related tags

Deep Learningimagedeep-learningpytorchtransformerlanguage-modelmultimodalvision-transformer
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

model

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
Myeongjun Kim
GitHub Repository
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