The Bosch Small Traffic Lights Dataset (BSTLD) is a treasure trove for anyone delving into the realm of traffic light detection and classification using machine learning. In this guide, we’ll explore the steps to efficiently utilize this dataset, from downloading it to converting annotations for your models. Let’s dive in!

Downloading the Dataset

The first step in your journey is to download the Bosch Small Traffic Lights Dataset. You can do so by clicking the link below:

• Download BSTLD

Previewing the Dataset

Before getting into the nitty-gritty, it’s helpful to watch a preview of the dataset, which can be found on YouTube. Click the image below to view:

Unzipping the Dataset Files

If you’ve downloaded the dataset, you might notice that it comes in *.zip formats. To unzip these, follow the instructions provided in this link:

• Unzipping Instructions

Using the Dataset

Inside the dataset, you’ll find label files located in the label_files folder. To convert the Bosch Small Traffic Lights Dataset annotations to Pascal VOC format, you can run the following Python script:

python bosch_to_pascal.py input_yaml out_folder

Understanding the Code: An Analogy

Think of the process of converting dataset annotations like baking a cake. Imagine the input YAML file as the recipe you’re going to follow, while the output folder is your kitchen where all the ingredients (converted annotations) will come together. When you execute the command, it’s like mixing those ingredients according to the recipe (script) to produce a finished cake (output in Pascal VOC format). Just as you wouldn’t want to skip steps while baking, ensure that all parameters are correctly set to avoid errors in your dataset preparation.

Viewing Sample Detections

To give you a glimpse of how effective your model can get, a sample detection based on an adapted YOLO v1 model has been showcased. Click below to review:

Evaluation Results

The dataset includes evaluations that help you understand various models’ performance metrics. Below are some key results:

Method	Execution Time	Weighted mAP	mAP	Link
Baseline	100 ms	0.36	—	Link
Hierarchical Deep Architecture	~150 ms	0.53	—	Link
SSD Mobilenet V1	38 ms	0.60	0.41	Link
Faster RCNN NAS-A	~1560s	0.65	0.43	Link

Troubleshooting

If you run into any issues during your usage of the Bosch Small Traffic Lights Dataset or while executing the Python script, here are some troubleshooting ideas to consider:

• Ensure that all required dependencies are installed prior to running the script.
• Check for typos in file names and paths to avoid common errors.
• Make sure that you are using the correct Python version as mentioned in the repository.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.