类似的输出结果：
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=2.61s).
Accumulating evaluation results...
DONE (t=0.74s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.245
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.424
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.264
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.220
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.243
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.603
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.604
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.538
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
[05/27 16:26:41] ppdet.engine INFO: Total sample number: 2258, averge FPS: 82.94079298317101
[05/27 16:26:41] ppdet.engine INFO: Best test bbox ap is 0.245.
官方注释：
?在括号中 IoU=0.50:0.95代表IoU 0.5到0.95的区间每隔0.05分别计算一次AP，最终相加求平均值, area = all代表所有尺寸的检测目标，maxDets=100代表Average Call given 100 detection per image. maxDets等于多少，就是多少detection。
?
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.245
尺寸判定条件
| 尺寸 | 判定条件 |
|---|---|
| small | 尺寸小于32x32个像素 |
| medium | 尺寸大于，32x32个像素，小于96x96个像素 |
| large | 尺寸大于96x96个像素 |
?那么怎么能实现paddledetection模型评估呢？（重点！！！！！）
paddledetection官方评估方法参考
一般过程是先训练、再评估、再推理
训练/评估/预测
PaddleDetection提供了训练/评估/预测，支持通过不同可选参数实现特定功能
# 设置PYTHONPATH路径
export PYTHONPATH=$PYTHONPATH:.
# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python tools/train.py -c configs/faster_rcnn_r50_1x.yml
# GPU评估
export CUDA_VISIBLE_DEVICES=0
python tools/eval.py -c configs/faster_rcnn_r50_1x.yml
# 预测
python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/000000570688.jpg
可选参数列表
以下列表可以通过–help查看
PaddleDetection训练/评估/预测流程_我是天才很好的博客-CSDN博客_paddledetection训练?