How to Identify Alzheimer’s Disease Using CLIP
In recent years, the field of artificial intelligence and machine learning has made significant progress, enabling researchers and developers to achieve remarkable results. The CLIP (Contrastive Language-Image Pretraining) model from OpenAI is a revolutionary leap in the AI arena, taking advantage of its multimodal capability to comprehend and interrelate text and images. CLIP presents enormous potential in a multitude of applications, especially zero-shot classification, as discussed in our previous post.
CLIP (Contrastive Language-Image Pretraining)
The CLIP model is a powerful tool that can understand and correlate images and text simultaneously. However, the model’s generalized training on a large corpus of internet text and images might not make it an expert in understanding certain specific or specialized types of images or text. To truly leverage the capabilities of the pre-trained CLIP model for a specific task or domain, fine-tuning is a crucial step.
The following sections of this post will provide you with a step-by-step guide on how to fine-tune the CLIP model with your own custom dataset using Python. The guide will cover the following steps:
- Importing necessary libraries
- Preparing the dataset
- Defining the model
- Defining the optimizer and loss function
- Training the model