TensorFlow Object Detection
faster CS:GO aim bot

Posted December 11, 2018 by Rokas Balsys

TensorFlow faster CSGO aim bot

You can download code on my GitHub page.

Welcome to part 8 of the TensorFlow Object Detection API tutorial series. In this tutorial I will show you how to export newly trained model and we'll test it out.

So in previous tutorial we made a final working model, which shoots enemies, but our FPS were really slow, so I decided to try training another model, so that's what we will talk about in this tutorial.

I used almost all the same files from 5th tutorial part, so if you don't have them yet you can clone my GitHub repository. In this part I am not covering how to label pictures, generate tfrecord or configure your training files. I already did this on my 5th tutorial. In this tutorial I will cover only this, which were not covered before.

At first trained model in 5th tutorial I used faster_rcnn_inception_v2_coco model, now I decided to train ssdlite_mobilenet_v2_coco, this model detects objects 21% worse but it is 53% faster, so I decided give it a try. Here is the link of all models, so download one if you decided to train model by yourself.

This time when I tries to use train.py file it said that I am using wrong training method, and offered to use model_main.py file. So I uploaded it if someone has problems finding it. I faced some problems when I tried to start it training the model, there were some error but I didn't made a note about them so I can't tell it exactly, so if you face errors, write it on YouTube comments, we'll try to solve it.

When training new model I was using same file structure, so you will need only to update ssdlite_mobilenet_v2_coco.config file and download your pretrained model. From TensorFlow/research/object_detection folder continue with following line in cmd:

python model_main.py --alsologtostderr --model_dir=CSGO_training_dir/ --pipeline_config_path=CSGO_training/ssdlite_mobilenet_v2_coco.config

When training model, it will not show steps as it was doing in 5th tutorial, but training routine periodically saves model checkpoints about every ten minutes to CSGO_training_dir directory. So you should check how your training is going in TensorFlow tensorboard, you can do so with following command:

C:\TensorFlow\research\object_detection>tensorboard --logdir=CSGO_training_dir

These few line of code were only for one object, we do this for all four objects:

I was training my model since I saw that my loss curve stopped dropping. It took for me close to 24 hours and did around 21k training steps:

FPS Slow

Then I used same export_inference_graph.py as we used in 6th tutorial. From command promt issued the following command, where “XXXX” in “model.ckpt-XXXX” should be replaced with the highest-numbered .ckpt file in the training folder:

python export_inference_graph.py --input_type image_tensor --pipeline_config_path CSGO_training/ssdlite_mobilenet_v2_coco.config --trained_checkpoint_prefix CSGO_training_dir/model.ckpt-XXXX --output_directory CSGO_inference_graph

In final step, we tooks all files from my 7th tutorial and replaced CSGO_frozen_inference_graph.pb file with newly trained inference_graph.

Next we tried to play CS:GO and I let my bot to shoot enemies, you can check this out on my YouTube video.

That’s all for this tutorial. With new model I didn't solved FPS problem, it improved performance slightly but not that we could play our game. So for future work I decided to learn doing stuff on multiprocessing and run our code processes in parallel. So in next tutorial I will be doing stuff with multiprocessing.