1. How to automatically deskew (straighten) a text image using OpenCV
2. Explanation of YOLO V4 a one stage detector
3. 5 Best Artificial Intelligence Online Courses for Beginners in 2020
4. A Non Mathematical guide to the mathematics behind Machine Learning
Step 1: Collecting Images
It’s very difficult to manually download each and every image from the internet. NO worries today I’m here to make your life easier
Here is a Chrome extension to download images in bulk in one go Imageye. Just add it to your browser, search for the object you are looking for. Click on the extension and BOOM!! there you go all the images will be downloaded.
Step 2: Uploading the dataset for training
Open this link. just like show in the picture you have to create a class give the class a label, click on upload>choose images from your files>select the images that you have previously downloaded, repeat this step for all the objects that you want to detect.
Now you are ready to train your model by just clicking on the Train Model button.
Step 3: Exporting the trained model
After the model is trained you can test it by uploading an image and the result will be displayed below.
If everything is working well, now you can export the model. Click on the Export Model button and this popup will open, now select the TensoflowLite tab and click on Download my model. It will download a zip file for you.
Step 4: Creating the flutter project
Clone this repository and install the dependencies by running the command in the terminal
Flutter pub get
Step 5: Replacing the custom trained model with your model
In the assets/tflite folder, you will find two files i.e. ssd_mobilenet.tflite and ssd_mobilenet.txt. Extract the zip file that you downloaded above and replace these files in the assets folder and make sure to rename them.
Step 6: Run the app and test it
Now simply you just have to run the app and the interface looks like this, obviously, this is a demo UI you can make your own tweaks and changes to make it shine.
There You Goo, CONGRATULATIONS 🎊🎉 on your own custom object detector