Evaluation is a process that takes a number of inputs/outputs pairs and aggregate them. You can always use the model directly and just parse its inputs/outputs manually to perform evaluation. Alternatively, evaluation is implemented in detectron2 using the DatasetEvaluator interface.

Detectron2 includes a few DatasetEvaluator that computes metrics using standard dataset-specific APIs (e.g., COCO, LVIS). You can also implement your own DatasetEvaluator that performs some other jobs using the inputs/outputs pairs. For example, to count how many instances are detected on the validation set:

class Counter(DatasetEvaluator):
  def reset(self):
    self.count = 0
  def process(self, inputs, outputs):
    for output in outputs:
      self.count += len(output["instances"])
  def evaluate(self):
    # save self.count somewhere, or print it, or return it.
    return {"count": self.count}

Use evaluators

To evaluate using the methods of evaluators manually:

def get_all_inputs_outputs():
  for data in data_loader:
    yield data, model(data)

for inputs, outputs in get_all_inputs_outputs():
  evaluator.process(inputs, outputs)
eval_results = evaluator.evaluate()

Evaluators can also be used with inference_on_dataset. For example,

eval_results = inference_on_dataset(
    DatasetEvaluators([COCOEvaluator(...), Counter()]))

This will execute model on all inputs from data_loader, and call evaluator to process them.

Compared to running the evaluation manually using the model, the benefit of this function is that evaluators can be merged together using DatasetEvaluators, and all the evaluation can finish in one forward pass over the dataset. This function also provides accurate speed benchmarks for the given model and dataset.

Evaluators for custom dataset

Many evaluators in detectron2 are made for specific datasets, in order to obtain scores using each dataset’s official API. In addition to that, two evaluators are able to evaluate any generic dataset that follows detectron2’s standard dataset format, so they can be used to evaluate custom datasets:

  • COCOEvaluator is able to evaluate AP (Average Precision) for box detection, instance segmentation, keypoint detection on any custom dataset.

  • SemSegEvaluator is able to evaluate semantic segmentation metrics on any custom dataset.