Tensorflow Object Detection Walkthrough

Steps

Step 1. Clone this repository: https://github.com/LearnWithDhruv/TFODCourse

Step 2. Create a new virtual environment

python -m venv tfod

Step 3. Activate your virtual environment

source tfod/bin/activate # Linux
.\tfod\Scripts\activate # Windows 

Step 4. Install dependencies and add virtual environment to the Python Kernel

python -m pip install --upgrade pip
pip install ipykernel
python -m ipykernel install --user --name=tfodj

Step 5. Collect images using the Notebook 1. Image Collection.ipynb - ensure you change the kernel to the virtual environment as shown below

Step 6. Manually divide collected images into two folders train and test. So now all folders and annotations should be split between the following two folders.
\TFODCourse\Tensorflow\workspace\images\train
\TFODCourse\Tensorflow\workspace\images\test

Step 7. Begin training process by opening 2. Training and Detection.ipynb, this notebook will walk you through installing Tensorflow Object Detection, making detections, saving and exporting your model.

Step 8. During this process the Notebook will install Tensorflow Object Detection. You should ideally receive a notification indicating that the API has installed successfully at Step 8 with the last line stating OK.

If not, resolve installation errors by referring to the Error Guide.md in this folder.


Step 9. You can optionally evaluate your model inside of Tensorboard. Once the model has been trained and you have run the evaluation command under Step 7. Navigate to the evaluation folder for your trained model e.g.

 cd Tensorlfow/workspace/models/my_ssd_mobnet/eval

and open Tensorboard with the following command

tensorboard --logdir=. 

Tensorboard will be accessible through your browser and you will be able to see metrics including mAP - mean Average Precision, and Recall.