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

Update README.md

This commit is contained in:
Glenn Jocher 2019-04-12 14:24:51 +02:00 committed by GitHub
parent bce3dd03e8
commit 24f86b008a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 27 additions and 26 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

53
README.md
View File

@ -149,36 +149,36 @@ YOLOv3-608 | 57.9 (58.2) | 57.9
`YOLOv3-spp 608` | 60.7 | 60.6 `YOLOv3-spp 608` | 60.7 | 60.6
``` bash ``` bash
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh # bash yolov3/data/get_coco_dataset.sh
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3 git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3 cd yolov3
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16 python3 test.py --save-json --img-size 416
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights') Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80) Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Image Total P R mAP Class Images Targets P R mAP F1
Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00, 1.55s/it] Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00, 1.71s/it]
5000 5000 0.0966 0.786 0.579 all 5e+03 3.58e+04 0.109 0.773 0.57 0.186
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.582 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.349
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198 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 ] = 0.362 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493
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= 1 ] = 0.280
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.437 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 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 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 ] = 0.577 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620
python3 test.py --weights weights/yolov3-spp.weights --cfg cfg/yolov3-spp.cfg --save-json --img-size 608 --batch-size 8 python3 test.py --save-json --img-size 608 --batch-size 16
Namespace(batch_size=8, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80) Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Image Total P R mAP Class Images Targets P R mAP F1
Calculating mAP: 100%|█████████████████████████████████| 625/625 [07:01<00:00, 1.56it/s] Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00, 1.01it/s]
5000 5000 0.12 0.81 0.611 all 5e+03 3.58e+04 0.12 0.81 0.611 0.203
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386
@ -191,6 +191,7 @@ Calculating mAP: 100%|███████████████████
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
``` ```
# Citation # Citation