deformable detr中test_stats有哪些数据

### Deformable DETR Model Test Stats Data Contents In the context of evaluating models like Deformable DETR, `test_stats` typically encompasses a comprehensive set of metrics and statistics that reflect how well the model performs on unseen data during testing phases. For Deformable DETR specifically, these include precision-recall curves, mean average precision (mAP), per-class accuracy scores, loss values over epochs, and inference time measurements[^1]. Additionally, detailed breakdowns by object size—small, medium, large—and category-specific performance indicators are often included to provide insights into where the model excels or requires improvement. These evaluations help researchers understand not only overall effectiveness but also specific areas needing attention within detection tasks. For practical demonstration purposes, consider this simplified Python code snippet showing how one might access such information from an evaluation process: ```python import json def display_test_statistics(test_stats_path): with open(test_stats_path, 'r') as file: test_stats = json.load(file) print(f"Mean Average Precision: {test_stats['mean_average_precision']}") print("Per-Class Accuracy:") for class_name, accuracy in test_stats["per_class_accuracy"].items(): print(f"- {class_name}: {accuracy:.2f}%") display_test_statistics('path_to_test_stats.json') ``` This script reads JSON-formatted output files generated after running tests on the Deformable DETR model, printing out key pieces of statistical information relevant for analysis. --related questions-- 1. What methods can improve small object detection rates in deformable convolution networks? 2. How does changing anchor box sizes affect mAP results in DETR architectures? 3. Can you explain the impact of different backbone choices on inference speed versus accuracy trade-offs?

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下一篇 Python自动化测试执行的时候可以隐藏部分画面吗