More classes in config than trained on tensorflow object detection API
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Understanding "More Classes in Config Than Trained On" in TensorFlow Object Detection API
The TensorFlow Object Detection API is a popular framework for building custom object detection models. When configuring a model for training or inference, users often encounter the warning: "More classes in config than trained on". This message may seem confusing at first, so let's delve into what it means and how to address it.
Background
Object detection models in TensorFlow require a few key components:
- Model Configuration (`pipeline.config`): Describes how the model should be trained, including the architecture, hyperparameters, and number of classes.
- Label Map: Maps each class name to a numerical ID used by the model.
- Training Data: Images with annotated bounding boxes, indicating where and what the objects are.
More Classes in Config Than Trained On: What Does It Mean?
When you see the warning "More classes in config than trained on", it indicates a discrepancy between the number of classes specified in the model configuration (e.g., in the `pipeline.config` file) and the actual classes present in your dataset.
Technical Explanation
- Model Configuration: The `num_classes` parameter in your `pipeline.config` file specifies how many classes your model is expected to detect.
- Label Map: This file, often in `pbtxt` format, associates human-readable labels with numeric class IDs. Every image in your dataset should be annotated with these labels.
- Training Dataset: Should contain annotations for each class listed in the label map with corresponding images.
When "more classes" are specified in `pipeline.config` but not represented in your training data, TensorFlow triggers the warning. This may lead to suboptimal model performance because the architecture anticipates a higher number of outputs than necessary.
Example Scenario
Suppose your model configuration specifies 10 classes, but your dataset only contains annotations for 8. This could stem from:
- Incorrect Label Map: Your `labels.pbtxt` file lists more classes than are actually used.
- Dataset Inconsistency: Some classes are configured but not included in annotations, perhaps due to filtering or errors during dataset creation.
Consider the following configuration snippet:
