diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index 1a5ea58..3bfb6f2 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -71,9 +71,9 @@ jobs: # detect custom python detect.py --weights runs/exp0/weights/last.pt --device $di # test official - python eval.py --weights weights/${{ matrix.yolo5-model }}.pt --device $di --batch-size 1 + python test.py --weights weights/${{ matrix.yolo5-model }}.pt --device $di --batch-size 1 # test custom - python eval.py --weights runs/exp0/weights/last.pt --device $di --batch-size 1 + python test.py --weights runs/exp0/weights/last.pt --device $di --batch-size 1 # inspect python models/yolo.py --cfg models/${{ matrix.yolo5-model }}.yaml # export diff --git a/README.md b/README.md index 0d5a38e..c80b139 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 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 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 checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). diff --git a/eval.py b/test.py similarity index 99% rename from eval.py rename to test.py index 4aa692e..ed7e29c 100644 --- a/eval.py +++ b/test.py @@ -233,7 +233,7 @@ def test(data, if __name__ == '__main__': - parser = argparse.ArgumentParser(prog='eval.py') + parser = argparse.ArgumentParser(prog='test.py') parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path') parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch') diff --git a/train.py b/train.py index 1c28fec..879bb2e 100644 --- a/train.py +++ b/train.py @@ -7,7 +7,7 @@ import torch.optim.lr_scheduler as lr_scheduler import torch.utils.data from torch.utils.tensorboard import SummaryWriter -import eval # import eval.py to get mAP after each epoch +import test # import test.py to get mAP after each epoch from models.yolo import Model from utils import google_utils from utils.datasets import * @@ -291,7 +291,7 @@ def train(hyp): ema.update_attr(model, include=['md', 'nc', 'hyp', 'gr', 'names', 'stride']) final_epoch = epoch + 1 == epochs if not opt.notest or final_epoch: # Calculate mAP - results, maps, times = eval.test(opt.data, + results, maps, times = test.test(opt.data, batch_size=batch_size, imgsz=imgsz_test, save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'), diff --git a/tutorial.ipynb b/tutorial.ipynb index 228a79b..d3418cc 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -236,7 +236,7 @@ }, "source": [ "# Run YOLOv5x on COCO val2017\n", - "!python eval.py --weights yolov5x.pt --data coco.yaml --img 672" + "!python test.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 eval.py --weights yolov5s.pt --data ./data/coco.yaml --task test" + "!python test.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 eval.py --weights $x.pt --data coco.yaml --img 672\n", + " python test.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 eval.py --weights $x.pt --device $di # test official\n", - " python eval.py --weights runs/exp0/weights/last.pt --device $di # test 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", " done\n", " python models/yolo.py --cfg $x.yaml # inspect\n", " python models/export.py --weights $x.pt --img 640 --batch 1 # export\n", diff --git a/utils/utils.py b/utils/utils.py index 209e884..ce1d910 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -1087,7 +1087,7 @@ def plot_targets_txt(): # from utils.utils import *; plot_targets_txt() def plot_study_txt(f='study.txt', x=None): # from utils.utils import *; plot_study_txt() - # Plot study.txt generated by eval.py + # Plot study.txt generated by test.py fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) ax = ax.ravel()