Prompt For Object Detection

# Object Detection
In the provious notebook we saw how to create masks via points and bounding boxes. Here we will see how we can use **natural text** to generate these masks. In order to do this we are going to use a pipeline of models, the output of the first model will be fed into the second model. The first model in this pipeline will be a **zero-shot object detection model**, which will then be followed by a SAM model that will take the generated bounding box from the zero-shot object detection.<br>
The zero-shot object detection model we will be using is called **OWL-ViT**. This model can detect objects within an image based on simple text prompts. The fact that it is a zero-shot model, means that we don't need to train it in any way for it to detect any object within an image. The way we will be using OWL-ViT within this is by using a text prompt that is a string of text to generate a bounding box.<br>
<img src="Images/owlvit.png" width="600" height="800"><br>
The OWL-ViT was trained on two tasks, one pre-training phase and a second fine tuning phase. In the pre-training phase,the model learns to associate an image with a piece of text using a technique that leverages contrastive loss, and this process allows the OWL-ViT model to develop a strong understanding of both an image and its corresponding text. In order to achieve good performance, it also required a fine tuning stage. During this stage the model is trained specifically for object detection. While in the pre-training phase the model was just learning how to associate a piece of text and an image, during the fine tuning stage, the model learns to identify, object and associate them with a particular word or string.<br>

We will create a Comet experiment which will allow us to compare the generated masks that will be produced at the end of this pipeline.