diff --git a/README.md b/README.md
index 6011e979..6e0b2af3 100755
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ Reflection | 50% probability (horizontal-only)
H**S**V Saturation | +/- 50%
HS**V** Intensity | +/- 50%
-
+
## 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'
| 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