Finetune ClipSeg model

### Feature request Quite recently, I was exploring zero-shot classification to segment medical images. And it looks quite promising. I stumbled upon ```ClipSeg``` a few days ago and it looked wonderful and just well-suited for my work. Unfortunately, I couldn't find any tutorials or notebooks that showed how to perform fine-tuning on ClipSeg model. I am assuming, we have to train the decoder with a dataset containing binary classification images of cells and their corresponding masks and a text description. Unfortunately, a bit confused. is there any tutorials/resources anyone could suggest on this topic? Cuz I couldn't none. ### Motivation ```ClipSeg``` shows a lot of potential than SAM (Segment Anything Model). Unfortunately, there's no fine-tuning script neither instructions on **How to prepare the dataset?** which is very frustrating. Will love some help from the community. And another point, Zero shot classification looks a way lot better option with fine-tuning than training a model like ```U-Net```, ```R-CNN``` and others from scratch while you have very few images and don't have much room to play around with. ### Your contribution I could provide a PR on my LinkedIn, where I have a lot of AI experts as my connections and then I contribute in the programming as well.