From 0be5e4132d728a7675de7eecc33facf7ea42f237 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 16 Oct 2019 01:32:07 +0200 Subject: [PATCH] updates --- README.md | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) 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