Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2019-11-01 22:34:20 -07:00
parent 8d1ab548c1
commit d5dfbedcda
1 changed files with 14 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
README.md
View File

@ -148,7 +148,7 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
<i></i> | 320 | 416 | 608 <i></i> | 320 | 416 | 608
--- | --- | --- | --- --- | --- | --- | ---
`YOLOv3` | 51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9) `YOLOv3` | 51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9)
`YOLOv3-SPP` | 52.6 | 57.0 | 60.7 (60.6) `YOLOv3-SPP` | 52.6 | 57.7 | 60.7 (60.6)
`YOLOv3-tiny` | 29.0 | 32.9 (33.1) | 35.5 `YOLOv3-tiny` | 29.0 | 32.9 (33.1) | 35.5
```bash ```bash
@ -174,19 +174,19 @@ $ python3 test.py --save-json --img-size 416
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3s-ultralytics.pt') Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3s-ultralytics.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB) Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it] Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it]
all 5e+03 3.58e+04 0.099 0.743 0.561 0.17 all 5e+03 3.58e+04 0.11 0.739 0.569 0.185
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.364 <--- Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570 <--- Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.577
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.392
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.394 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.516 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.537
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.472 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.482
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.664 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693
``` ```
# Citation # Citation