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

updates

This commit is contained in:
Glenn Jocher 2019-09-16 19:39:54 +02:00
parent b62dc6f06a
commit 77dce00fa8
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
--- | --- | --- | ---
`YOLOv3` | 51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9)
`YOLOv3-SPP` | 52.4 | 56.5 | 60.7 (60.6)
`YOLOv3-SPP` | 52.4 | 56.8 | 60.7 (60.6)
`YOLOv3-tiny` | 29.0 | 32.9 (33.1) | 35.5
```bash
@ -156,7 +156,7 @@ python3 test.py --save-json --img-size 608
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it]
all 5e+03 3.58e+04 0.117 0.788 0.595 0.199
all 5e+03 3.58e+04 0.119 0.788 0.594 0.201
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367 <---
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <---
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387
@ -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/yolov3-spp.weights')
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]
all 5e+03 3.58e+04 0.105 0.746 0.554 0.18
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.336 <---
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 <---
all 5e+03 3.58e+04 0.107 0.749 0.557 0.182
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337 <---
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568 <---
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.350
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.361
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.152
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.359
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.496
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.460
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.257
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.623
```
# Citation