You only look once (YOLO) is a state-of-the-art, real-time object detection system. The latest version is YOLOV5. These YOLO models are well known for speed. They can detect multiple images in video frames in a fraction of seconds. The inference time of YOLO model with compare to the other models like EfficientDet and RetinaNet is better. YOLO is known for its speed for detecting the objects in image/video. If your usecase is more concern about real time detection of multiple objects then YOLO is the most suitable. Now, We have YOLO V5 which has around **476 FPS **in its small version of the model.
In this article, I will walk you through the steps to deploy your own custom YOLO model in localhost.
Let’s begin then
Here, I will walk through all the other steps in brief as our agenda is to deploy the model and use it on our website.
- **Grab images for labeling: **It is the first step. Here, you can go to google and search for the pictures you want to build your custom for. Or, you can go around and click photos of objects and gather them for the model. That is your choice how better you want your model to be.
- **Label your images. **Give a fair amount of data for this step as it is essential for your accuracy. You can use any tools for labeling your data. There is no automatic approach to label your custom data. You have to do it manually. And this is the most frustrating and time-consuming part of the object detention, but surely you will get the fruitful result if you give your dedication to this part. I have used LabelImg tool for labeling.
- Select object detection algorithm Here, we will be using YOLO-tiny. And, you can refer the custom object detection code here. https://github.com/pranjalAI/Yolo-tiny-insurance. There are many other approaches for object detection like SSD, Yolo etc.
- After getting your Yolo weight file from step 3, You need to convert the Yolo generated darknet model .weight file into TensorFlow serving so that we can serve it using TensorFlow to our webpage.
So, if you have reached till here. Cheers!! You have done a great job. You are ready to move your model to production.
#deployment #image-processing #deep-learning #object-detection #python
