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

Related tags

Deep Learning image deep-learning pytorch transformer language-model multimodal vision-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
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Comments
  • Cosine Distance Calculation

    Cosine Distance Calculation

    Very appreciates the code here, it's very helpful to me. However, in line 40 of the main.py, the logits you calculate might be the Cosine Similarity but not the Cosine Distance, should you put a minus Symbol there?

    opened by jiajingchen113322 0
Owner
Myeongjun Kim
Computer Vision Research using Deep Learning
Myeongjun Kim
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