From 4a7d9bdba9cea2d009d40aa3352c7fb5a80ab541 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 26 Mar 2020 14:14:52 -0700 Subject: [PATCH] mAP increases --- README.md | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 10318d0b..95e0555a 100755 --- a/README.md +++ b/README.md @@ -147,34 +147,34 @@ $ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt |Size |COCO mAP
@0.5...0.95 |COCO mAP
@0.5 --- | --- | --- | --- -YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0
28.7
30.5
**36.6** |29.1
51.8
52.3
**56.0** -YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0
31.2
33.9
**40.4** |33.0
55.4
56.9
**60.2** -YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6
32.7
35.6
**41.6** |34.9
57.7
59.5
**61.7** -YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6
33.1
37.0
**42.1** |35.4
58.2
60.7
**61.7** +YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0
28.7
30.5
**38.9** |29.1
51.8
52.3
**56.9** +YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0
31.2
33.9
**42.5** |33.0
55.4
56.9
**61.1** +YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6
32.7
35.6
**43.6** |34.9
57.7
59.5
**62.5** +YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6
33.1
37.0
**44.0** |35.4
58.2
60.7
**62.6** ```bash $ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 608 -Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weights/yolov3-spp-ultralytics.pt') +Namespace(batch_size=16, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weights/yolov3-spp-ultralytics.pt') Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB) Class Images Targets P R mAP@0.5 F1: 100%|█████| 157/157 [02:46<00:00, 1.06s/it] - all 5e+03 3.51e+04 0.51 0.667 0.611 0.574 + all 5e+03 3.51e+04 0.515 0.665 0.61 0.577 - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.419 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.618 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.448 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.247 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.462 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.534 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.341 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.606 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.440 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.735 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.434 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.469 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.480 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.346 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.617 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.786 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.730 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.836 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.863 -Speed: 6.5/1.5/8.1 ms inference/NMS/total per 608x608 image at batch-size 32 +Speed: 6.9/2.1/9.0 ms inference/NMS/total per 608x608 image at batch-size 16 ``` # Reproduce Our Results