JunseokLee42 / clip

This repository is about finetuning CLIP and zero-shot classification

Finetuning-CLIP

Introduction

This repository is about finetuning CLIP and zero-shot classification via pre-trained CLIP.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.6 or later
• PyTorch 1.7.1 or later
• transformers and clip libraries installed
• Colab Pro V100 GPU or an equivalent GPU while fine-tuning CLIP
• Wandb(NOT NECESSARY, it is used to track losses of text and image.)

Experiments

Dataset

In this project, you can download Indo fashion dataset.

The dataset is split into 3 section, that is, training, validation, and test. The total number of categories is 15. Each class is one kind of indo fashion categories.

Training	Validation	Test	Total
91K	7.5K	7.5K	106K

Hyperparameter's Configuration

Epochs	Batch sizes	Optimizer	Loss Function
30	256	Adam	Cross Entropy

The hyperparameters used in Adam optimizer are below:

learning rate	$B_1	$B_2	Weight Decay
5e-5	0.9	0.98	0.2

Result

Reference

Inference code: CLIP

Baseline Training code: #83