mirrors
/
yolov5
mirror of https://github.com/ultralytics/yolov5.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

rename

pull/426/head
Jirka 2020-07-16 12:11:38 +02:00
parent da6c7cea2b
commit eac0dbc46a
3 changed files with 9 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
.github/workflows/ci-testing.yml vendored
View File

@ -69,9 +69,9 @@ jobs:
# detect custom
python detect.py --weights runs/exp0/weights/last.pt --device $di
# test official
python test.py --weights weights/${{ matrix.yolo5-model }}.pt --device $di --batch-size 2
python eval.py --weights weights/${{ matrix.yolo5-model }}.pt --device $di --batch-size 2
# test custom
python test.py --weights runs/exp0/weights/last.pt --device $di --batch-size 2
python eval.py --weights runs/exp0/weights/last.pt --device $di --batch-size 2
# inspect
python models/yolo.py --cfg models/${{ matrix.yolo5-model }}.yaml
# export

4
README.md
View File

@ -27,8 +27,8 @@ This repository represents Ultralytics open-source research into future object d
** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 736 --conf 0.001`
** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1`
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python eval.py --data coco.yaml --img 736 --conf 0.001`
** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python eval.py --data coco.yaml --img 640 --conf 0.1`
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).

10
tutorial.ipynb
View File

@ -236,7 +236,7 @@
},
"source": [
"# Run YOLOv5x on COCO val2017\n",
"!python test.py --weights yolov5x.pt --data coco.yaml --img 672"
"!python eval.py --weights yolov5x.pt --data coco.yaml --img 672"
],
"execution_count": null,
"outputs": [
@ -319,7 +319,7 @@
},
"source": [
"# Run YOLOv5s on COCO test-dev2017 with argument --task test\n",
"!python test.py --weights yolov5s.pt --data ./data/coco.yaml --task test"
"!python eval.py --weights yolov5s.pt --data ./data/coco.yaml --task test"
],
"execution_count": null,
"outputs": []
@ -717,7 +717,7 @@
"for x in best*\n",
"do\n",
" gsutil cp gs://*/*/*/$x.pt .\n",
" python test.py --weights $x.pt --data coco.yaml --img 672\n",
" python eval.py --weights $x.pt --data coco.yaml --img 672\n",
"done"
],
"execution_count": null,
@ -744,8 +744,8 @@
" do\n",
" python detect.py --weights $x.pt --device $di # detect official\n",
" python detect.py --weights runs/exp0/weights/last.pt --device $di # detect custom\n",
" python test.py --weights $x.pt --device $di # test official\n",
" python test.py --weights runs/exp0/weights/last.pt --device $di # test custom\n",
" python eval.py --weights $x.pt --device $di # test official\n",
" python eval.py --weights runs/exp0/weights/last.pt --device $di # test custom\n",
" done\n",
" python models/yolo.py --cfg $x.yaml # inspect\n",
" python models/export.py --weights $x.pt --img 640 --batch 1 # export\n",