updates
This commit is contained in:
Glenn Jocher
parent
92f742618c
commit
0fe40cb687
65
README.md
65
README.md
|
|
@ -138,54 +138,37 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
|
|||
# mAP
|
||||
|
||||
- `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
|
||||
- `test.py --weights weights/last.pt` tests most recent checkpoint.
|
||||
- `test.py --weights weights/best.pt` tests best checkpoint.
|
||||
- `test.py --weights weights/last.pt` tests latest checkpoint.
|
||||
- Compare to darknet published results https://arxiv.org/abs/1804.02767.
|
||||
|
||||
[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 ([darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5)
|
||||
[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 vs. [darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5
|
||||
|
||||
<i></i> | 320 | 416 | 608
|
||||
--- | --- | --- | ---
|
||||
`YOLOv3` | 51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9)
|
||||
`YOLOv3-SPP` | 53.7 | 57.7 | 60.7 (60.6)
|
||||
`YOLOv3-tiny` | 29.0 | 32.9 (33.1) | 35.5
|
||||
<i></i> | 320 | 416 | 608
|
||||
--- | --- | --- | ---
|
||||
darknet `YOLOv3-tiny` | 29.0 | 33.1 | 35.5
|
||||
darknet `YOLOv3` | 51.5 | 55.3 | 57.9
|
||||
darknet `YOLOv3-SPP` | 52.3 | 56.8 | **60.6**
|
||||
ultralytics `YOLOv3-SPP` | **53.9** | **58.7** | 60.1
|
||||
|
||||
```bash
|
||||
$ 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')
|
||||
$ python3 test.py --save-json --img-size 608 --weights ultralytics68.pt
|
||||
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='ultralytics68.pt')
|
||||
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 0.119 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
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
|
||||
|
||||
$ 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')
|
||||
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 0.11 0.739 0.569 0.185
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.577
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.392
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175
|
||||
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 ] = 0.537
|
||||
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 ] = 0.482
|
||||
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 ] = 0.266
|
||||
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 ] = 0.693
|
||||
Class Images Targets P R mAP@0.5 F1: 100% 313/313 [06:52<00:00, 1.24it/s]
|
||||
all 5e+03 3.58e+04 0.107 0.779 0.59 0.182
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.398 <---
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.601 <---
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.425
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.325
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.519
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.543
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.366
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.584
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665
|
||||
```
|
||||
|
||||
# Citation
|
||||
|
|
|
|||
Loading…
Reference in New Issue