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Glenn Jocher 2019-12-26 12:52:25 -08:00
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@ -140,7 +140,7 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
python3 test.py --weights ... --cfg ... python3 test.py --weights ... --cfg ...
``` ```
- mAP@0.5 run at `--nms-thres 0.5`, mAP@0.5...0.95 run at `--nms-thres 0.7` - mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7`
- YOLOv3-SPP ultralytics is `ultralytics68.pt` with `yolov3-spp.cfg` - YOLOv3-SPP ultralytics is `ultralytics68.pt` with `yolov3-spp.cfg`
- Darknet results: https://arxiv.org/abs/1804.02767 - Darknet results: https://arxiv.org/abs/1804.02767
@ -153,106 +153,24 @@ YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |608 |16.6<br>
```bash ```bash
$ python3 test.py --save-json --img-size 608 --nms-thres 0.5 --weights ultralytics68.pt $ python3 test.py --img-size 608 --iou-thr 0.5 --weights ultralytics68.pt --cfg yolov3-spp.cfg
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='1', img_size=608, iou_thres=0.5, nms_thres=0.7, save_json=True, weights='ultralytics68.pt') Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.5, save_json=True, task='test', weights='ultralytics68.pt')
Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB) Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Class Images Targets P R mAP@0.5 F1: 100% 157/157 [04:13<00:00, 1.16it/s]
Class Images Targets P R mAP@0.5 F1: 100%|███████████████████████████████████████████████████████████████████████████████████| 313/313 [09:46<00:00, 1.09it/s] all 5e+03 3.51e+04 0.0437 0.88 0.607 0.0822
all 5e+03 3.58e+04 0.0823 0.798 0.595 0.145 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.406
person 5e+03 1.09e+04 0.0999 0.903 0.771 0.18 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615
bicycle 5e+03 316 0.0491 0.782 0.56 0.0925 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.431
car 5e+03 1.67e+03 0.0552 0.845 0.646 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.238
motorcycle 5e+03 391 0.11 0.847 0.704 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.444
airplane 5e+03 131 0.099 0.947 0.878 0.179 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.516
bus 5e+03 261 0.142 0.874 0.825 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.337
train 5e+03 212 0.152 0.863 0.806 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548
truck 5e+03 352 0.0849 0.682 0.514 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.592
boat 5e+03 475 0.0498 0.787 0.504 0.0937 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.418
traffic light 5e+03 516 0.0304 0.752 0.516 0.0584 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.635
fire hydrant 5e+03 83 0.144 0.916 0.882 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.729
stop sign 5e+03 84 0.0833 0.917 0.809 0.153
parking meter 5e+03 59 0.0607 0.695 0.611 0.112
bench 5e+03 473 0.0294 0.685 0.363 0.0564
bird 5e+03 469 0.0521 0.716 0.524 0.0972
cat 5e+03 195 0.252 0.908 0.78 0.395
dog 5e+03 223 0.192 0.883 0.829 0.315
horse 5e+03 305 0.121 0.911 0.843 0.214
sheep 5e+03 321 0.114 0.854 0.724 0.201
cow 5e+03 384 0.105 0.849 0.695 0.187
elephant 5e+03 284 0.184 0.944 0.912 0.308
bear 5e+03 53 0.358 0.925 0.875 0.516
zebra 5e+03 277 0.176 0.935 0.858 0.297
giraffe 5e+03 170 0.171 0.959 0.892 0.29
backpack 5e+03 384 0.0426 0.708 0.392 0.0803
umbrella 5e+03 392 0.0672 0.878 0.65 0.125
handbag 5e+03 483 0.0238 0.629 0.242 0.0458
tie 5e+03 297 0.0419 0.805 0.599 0.0797
suitcase 5e+03 310 0.0823 0.855 0.628 0.15
frisbee 5e+03 109 0.126 0.872 0.796 0.221
skis 5e+03 282 0.0473 0.748 0.454 0.089
snowboard 5e+03 92 0.0579 0.804 0.559 0.108
sports ball 5e+03 236 0.057 0.733 0.622 0.106
kite 5e+03 399 0.087 0.852 0.645 0.158
baseball bat 5e+03 125 0.0496 0.776 0.603 0.0932
baseball glove 5e+03 139 0.0511 0.734 0.563 0.0956
skateboard 5e+03 218 0.0655 0.844 0.73 0.122
surfboard 5e+03 266 0.0709 0.827 0.651 0.131
tennis racket 5e+03 183 0.0694 0.858 0.759 0.128
bottle 5e+03 966 0.0484 0.812 0.513 0.0914
wine glass 5e+03 366 0.0735 0.738 0.543 0.134
cup 5e+03 897 0.0637 0.788 0.538 0.118
fork 5e+03 234 0.0411 0.662 0.487 0.0774
knife 5e+03 291 0.0334 0.557 0.292 0.0631
spoon 5e+03 253 0.0281 0.621 0.307 0.0537
bowl 5e+03 620 0.0624 0.795 0.514 0.116
banana 5e+03 371 0.052 0.83 0.41 0.0979
apple 5e+03 158 0.0293 0.741 0.262 0.0564
sandwich 5e+03 160 0.0913 0.725 0.522 0.162
orange 5e+03 189 0.0382 0.688 0.32 0.0723
broccoli 5e+03 332 0.0513 0.88 0.445 0.097
carrot 5e+03 346 0.0398 0.766 0.362 0.0757
hot dog 5e+03 164 0.0958 0.646 0.494 0.167
pizza 5e+03 224 0.0886 0.875 0.699 0.161
donut 5e+03 237 0.0925 0.827 0.64 0.166
cake 5e+03 241 0.0658 0.71 0.539 0.12
chair 5e+03 1.62e+03 0.0432 0.793 0.489 0.0819
couch 5e+03 236 0.118 0.801 0.584 0.205
potted plant 5e+03 431 0.0373 0.852 0.505 0.0714
bed 5e+03 195 0.149 0.846 0.693 0.253
dining table 5e+03 634 0.0546 0.82 0.49 0.102
toilet 5e+03 179 0.161 0.95 0.81 0.275
tv 5e+03 257 0.0922 0.903 0.79 0.167
laptop 5e+03 237 0.127 0.869 0.744 0.222
mouse 5e+03 95 0.0648 0.863 0.732 0.12
remote 5e+03 241 0.0436 0.788 0.535 0.0827
keyboard 5e+03 117 0.0668 0.923 0.755 0.125
cell phone 5e+03 291 0.0364 0.704 0.436 0.0692
microwave 5e+03 88 0.154 0.841 0.743 0.261
oven 5e+03 142 0.0618 0.803 0.576 0.115
toaster 5e+03 11 0.0565 0.636 0.191 0.104
sink 5e+03 211 0.0439 0.853 0.544 0.0835
refrigerator 5e+03 107 0.0791 0.907 0.742 0.145
book 5e+03 1.08e+03 0.0399 0.667 0.233 0.0753
clock 5e+03 292 0.0542 0.836 0.733 0.102
vase 5e+03 353 0.0675 0.799 0.591 0.125
scissors 5e+03 56 0.0397 0.75 0.461 0.0755
teddy bear 5e+03 245 0.0995 0.882 0.669 0.179
hair drier 5e+03 11 0.00508 0.0909 0.0475 0.00962
toothbrush 5e+03 77 0.0371 0.74 0.418 0.0706
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.409
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.600
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.446
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.243
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.514
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.326
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.536
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.422
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.640
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.707
``` ```
# Reproduce Our Results # Reproduce Our Results