Custom Object Detection Model Validation

TensorFlow General Discussion
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I recently completed the TensorFlow custom object detection model tutorial using a pre-trained model from the model zoo for a project I am working on. The tutorial was helpful, but I want to use a dataset for validation and see the performance of my model while training. I have looked online on multiple websites and forums and I haven’t been able to get this information. Could you please direct me to a solution or make one available for future reference?

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@courtney_ann Welcome to Tensorflow Forum!

Adding Validation Dataset to TensorFlow Object Detection Model

To incorporate a validation dataset and monitor your model’s performance during training with TensorFlow Object Detection API, you can follow these steps:

1. Prepare the Validation Dataset:

  • Split your dataset : Divide your original dataset into two parts: training and validation sets. Generally, a split of 70% training and 30% validation is used.
  • Generate TFRecords : Convert your validation images and annotations into TFRecord format, similar to your training dataset.

2. Modify the Training Configuration:

  • Update the pipeline.config : In the training configuration file, add a section for the validation dataset. Specify the path to your validation TFRecord file and adjust the relevant parameters like num_validation_shards .
  • Add evaluation metrics : Define the evaluation metrics you want to track, such as Average Precision (AP) or mean Intersection over Union (mIOU). These metrics can be calculated on the validation dataset during training to assess the model’s performance.

3. Train with Validation:

  • Run the training script : When you run the training script, the model will be evaluated on the validation dataset at regular intervals (e.g., every 1000 steps). This will provide you with insights into how the model is performing on unseen data and allow you to track its progress.
  • Monitor metrics : Analyze the reported validation metrics like AP or mIOU. If these metrics start to plateau or decrease, it might indicate overfitting, and you might need to adjust training parameters or hyperparameters.

Resources:

Here are some resources that provide further information and examples on adding validation to your TensorFlow Object Detection model:

Additional Tips:

  • Consider using early stopping to avoid overfitting. This technique automatically stops training if the validation metrics stop improving for a certain number of epochs.
  • Employ visualization tools like TensorBoard to monitor the training process and visualize the validation metrics over time.
  • Adjust hyperparameters like learning rate, batch size, and model architecture based on the validation performance to improve the overall accuracy andgeneralizability of your model.

By incorporating a validation dataset and monitoring its performance, you can effectively train and evaluate your custom object detection model in TensorFlow, leading to better results for your specific project.

Let us know if this helps!