From eac0dbc46a1d774ba2617b8e6affc0a3ee8c3af9 Mon Sep 17 00:00:00 2001 From: Jirka Date: Thu, 16 Jul 2020 12:11:38 +0200 Subject: [PATCH] rename --- .github/workflows/ci-testing.yml | 4 ++-- README.md | 4 ++-- tutorial.ipynb | 10 +++++----- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index cda65a9..6080816 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -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 diff --git a/README.md b/README.md index c80b139..0d5a38e 100755 --- a/README.md +++ b/README.md @@ -27,8 +27,8 @@ This repository represents Ultralytics open-source research into future object d ** APtest 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` -** SpeedGPU 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` +** SpeedGPU 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). diff --git a/tutorial.ipynb b/tutorial.ipynb index d3418cc..228a79b 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -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",