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updates

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Glenn Jocher 2019-10-16 01:32:07 +02:00
parent 376e00a3cf
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@ -69,7 +69,7 @@ Reflection | 50% probability (horizontal-only)
H**S**V Saturation | +/- 50%
HS**V** Intensity | +/- 50%
<img src="https://user-images.githubusercontent.com/26833433/61579359-507b7d80-ab04-11e9-8a2a-bd6f59bbdfb4.jpg" width="900">
<img src="https://user-images.githubusercontent.com/26833433/66699231-27beea80-ece5-11e9-9cad-bdf9d82c500a.jpg" width="900">
## Speed
@ -125,14 +125,14 @@ To run a specific models:
## Darknet Conversion
```bash
git clone https://github.com/ultralytics/yolov3 && cd yolov3
$ git clone https://github.com/ultralytics/yolov3 && cd yolov3
# convert darknet cfg/weights to pytorch model
python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
Success: converted 'weights/yolov3-spp.weights' to 'converted.pt'
# convert cfg/pytorch model to darknet weights
python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
```
@ -148,11 +148,11 @@ 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 | 56.8 | 60.7 (60.6)
`YOLOv3-SPP` | 52.6 | 57.0 | 60.7 (60.6)
`YOLOv3-tiny` | 29.0 | 32.9 (33.1) | 35.5
```bash
python3 test.py --save-json --img-size 608
$ 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]
@ -170,23 +170,23 @@ Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
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/yolov3-spp.weights')
$ 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 0.107 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 ] = 0.152
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
all 5e+03 3.58e+04 0.099 0.743 0.561 0.17
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.364 <---
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570 <---
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.394
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.516
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= 10 ] = 0.472
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.493
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.272
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.530
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.664
```
# Citation