Fine-Tuning for Pedestrian and Vehicle Detection in a Warehouse

Transfer learning is a deep learning technique that seeks to repurpose a neural network trained for one task to perform another.

One application of transfer learning is fine-tuning, which involves taking an existing model that performs well on a general task and training it on a new, more specific dataset.

The most obvious application is fine-tuning for object recognition. Starting from a model capable of identifying a large number of classes, you refine it so that it recognizes a specific class.

The Benefits of Fine-Tuning

The original model performs well because it was trained:

• On a large-scale dataset
• With significant computing power
• Over a long period of time

Fine-tuning saves on all three of these requirements: it uses a smaller dataset, less computing power, and less time.

The goal is to achieve a higher accuracy rate than the general model on the new, more specific task.

How Does It Work?

Choosing the Base Model

The general model must be capable of solving a task similar to yours.

Adjusting the Model Architecture

In most cases, this means changing the shape of the model's last layer so that it outputs the correct number of classes. This modified layer is initialized with random weights. The remaining layers of the network are frozen so that their weights are not altered during training.

Training

Train the model on the specific dataset. This is typically done with a small learning rate, which enables finer learning than during the original model's training.

Application Example

Following this PyTorch tutorial, I used fine-tuning to recognize pedestrians and vehicles in a warehouse.

Preparing the Dataset

I generated the dataset in Blender. This process is detailed in this article: Synthetic Dataset Using 3D Rendering in Blender - AI Squad by Reboot Conseil (rebootia.com)

Choosing the Base Model

I selected the pre-trained MaskRCNN model with ResNet50 from PyTorch, a 50-layer model that performs well across a large number of classes.

Adjusting the Model Architecture

Here, we need to identify 3 classes: pedestrians, pallet jacks, and the background. The model's last layer is therefore replaced with a layer that has 3 outputs.

Results

The objective was achieved. Starting from a general model, I now have a model that meets my specific needs, all with minimal training time and a small dataset.