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Unverified 提交 4821d076 authored 作者: Glenn Jocher's avatar Glenn Jocher 提交者： GitHub
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Increment train, test, detect runs/ (#1322)

* Increment train, test, detect runs/ * Update ci-testing.yml * inference/images to data/images * move images * runs/exp to runs/train/exp * update 'results saved to %s' str
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.github/workflows/ci-testing.yml
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......@@ -66,10 +66,10 @@ jobs:
python train.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di
# detect
python detect.py --weights weights/${{ matrix.model }}.pt --device $di
python detect.py --weights runs/exp0/weights/last.pt --device $di
python detect.py --weights runs/train/exp0/weights/last.pt --device $di
# test
python test.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --device $di
python test.py --img 256 --batch 8 --weights runs/exp0/weights/last.pt --device $di
python test.py --img 256 --batch 8 --weights runs/train/exp0/weights/last.pt --device $di
python models/yolo.py --cfg models/${{ matrix.model }}.yaml # inspect
python models/export.py --img 256 --batch 1 --weights weights/${{ matrix.model }}.pt # export
......
.gitignore
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......@@ -26,8 +26,8 @@
storage.googleapis.com
runs/*
data/*
!data/samples/zidane.jpg
!data/samples/bus.jpg
!data/images/zidane.jpg
!data/images/bus.jpg
!data/coco.names
!data/coco_paper.names
!data/coco.data
......
Dockerfile
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......@@ -46,7 +46,7 @@ COPY . /usr/src/app
# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume
# Send weights to GCP
# python -c "from utils.general import *; strip_optimizer('runs/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt
# python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt
# Clean up
# docker system prune -a --volumes
README.md
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......@@ -70,7 +70,7 @@ YOLOv5 may be run in any of the following up-to-date verified environments (with
## Inference
detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `inference/output`.
detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
$ python detect.py --source 0 # webcam
file.jpg # image
......@@ -82,20 +82,20 @@ $ python detect.py --source 0 # webcam
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
```
To run inference on example images in `inference/images`:
To run inference on example images in `data/images`:
```bash
$ python detect.py --source inference/images --weights yolov5s.pt --conf 0.25
$ python detect.py --source data/images --weights yolov5s.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, output='inference/output', save_conf=False, save_txt=False, source='inference/images', update=False, view_img=False, weights='yolov5s.pt')
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, output='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16160MB)
Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s]
Fusing layers...
Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients
image 1/2 yolov5/inference/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.013s)
image 2/2 yolov5/inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.013s)
Results saved to yolov5/inference/output
image 1/2 data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.013s)
image 2/2 data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.013s)
Results saved to runs/detect/exp0
Done. (0.124s)
```
<img src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg" width="500">
......
detect.py
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import argparse
import os
import shutil
import time
from pathlib import Path
......@@ -11,23 +10,25 @@ from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import (
check_img_size, non_max_suppression, apply_classifier, scale_coords,
xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
plot_one_box, strip_optimizer, set_logging, increment_dir
from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(save_img=False):
out, source, weights, view_img, save_txt, imgsz = \
opt.save_dir, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
save_dir, source, weights, view_img, save_txt, imgsz = \
Path(opt.save_dir), opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.startswith(('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt')
# Directories
if save_dir == Path('runs/detect'): # if default
os.makedirs('runs/detect', exist_ok=True) # make base
save_dir = Path(increment_dir(save_dir / 'exp', opt.name)) # increment run
os.makedirs(save_dir / 'labels' if save_txt else save_dir, exist_ok=True) # make new dir
# Initialize
set_logging()
device = select_device(opt.device)
if os.path.exists(out): # output dir
shutil.rmtree(out) # delete dir
os.makedirs(out) # make new dir
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
......@@ -83,12 +84,12 @@ def detect(save_img=False):
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
p, s, im0 = Path(path), '', im0s
save_path = str(Path(out) / Path(p).name)
txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
save_path = str(save_dir / p.name)
txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
......@@ -104,7 +105,7 @@ def detect(save_img=False):
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, conf, *xywh) if opt.save_conf else (cls, *xywh) # label format
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line) + '\n') % line)
......@@ -139,7 +140,7 @@ def detect(save_img=False):
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % Path(out))
print('Results saved to %s' % save_dir)
print('Done. (%.3fs)' % (time.time() - t0))
......@@ -147,15 +148,16 @@ def detect(save_img=False):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-txt', action='store_false', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-dir', type=str, default='inference/output', help='directory to save results')
parser.add_argument('--save-dir', type=str, default='runs/detect', help='directory to save results')
parser.add_argument('--name', default='', help='name to append to --save-dir: i.e. runs/{N} -> runs/{N}_{name}')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
......
hubconf.py
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......@@ -113,6 +113,6 @@ if __name__ == '__main__':
# Verify inference
from PIL import Image
img = Image.open('inference/images/zidane.jpg')
img = Image.open('data/images/zidane.jpg')
y = model(img)
print(y[0].shape)
sotabench.py deleted 100644 → 0
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差异被折叠。
test.py
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......@@ -2,7 +2,6 @@ import argparse
import glob
import json
import os
import shutil
from pathlib import Path
import numpy as np
......@@ -12,9 +11,9 @@ from tqdm import tqdm
from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import (
coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords,
xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, \
non_max_suppression, scale_coords, xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, \
ap_per_class, set_logging, increment_dir
from utils.torch_utils import select_device, time_synchronized
......@@ -46,16 +45,11 @@ def test(data,
device = select_device(opt.device, batch_size=batch_size)
save_txt = opt.save_txt # save *.txt labels
# Remove previous
if os.path.exists(save_dir):
shutil.rmtree(save_dir) # delete dir
os.makedirs(save_dir) # make new dir
if save_txt:
out = save_dir / 'autolabels'
if os.path.exists(out):
shutil.rmtree(out) # delete dir
os.makedirs(out) # make new dir
# Directories
if save_dir == Path('runs/test'): # if default
os.makedirs('runs/test', exist_ok=True) # make base
save_dir = Path(increment_dir(save_dir / 'exp', opt.name)) # increment run
os.makedirs(save_dir / 'labels' if save_txt else save_dir, exist_ok=True) # make new dir
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
......@@ -144,8 +138,8 @@ def test(data,
x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original
for *xyxy, conf, cls in x:
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, conf, *xywh) if save_conf else (cls, *xywh) # label format
with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f:
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(str(save_dir / 'labels' / Path(paths[si]).stem) + '.txt', 'a') as f:
f.write(('%g ' * len(line) + '\n') % line)
# W&B logging
......@@ -268,6 +262,7 @@ def test(data,
print('ERROR: pycocotools unable to run: %s' % e)
# Return results
print('Results saved to %s' % save_dir)
model.float() # for training
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
......@@ -292,6 +287,7 @@ if __name__ == '__main__':
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-dir', type=str, default='runs/test', help='directory to save results')
parser.add_argument('--name', default='', help='name to append to --save-dir: i.e. runs/{N} -> runs/{N}_{name}')
opt = parser.parse_args()
opt.save_json |= opt.data.endswith('coco.yaml')
opt.data = check_file(opt.data) # check file
......@@ -313,8 +309,6 @@ if __name__ == '__main__':
save_conf=opt.save_conf,
)
print('Results saved to %s' % opt.save_dir)
elif opt.task == 'study': # run over a range of settings and save/plot
for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
......
train.py
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import argparse
import logging
import math
import os
import random
import shutil
......@@ -7,7 +8,6 @@ import time
from pathlib import Path
from warnings import warn
import math
import numpy as np
import torch.distributed as dist
import torch.nn as nn
......@@ -404,14 +404,14 @@ if __name__ == '__main__':
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
parser.add_argument('--name', default='', help='renames experiment folder exp{N} to exp{N}_{name} if supplied')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
parser.add_argument('--logdir', type=str, default='runs/', help='logging directory')
parser.add_argument('--logdir', type=str, default='runs/train', help='logging directory')
parser.add_argument('--name', default='', help='name to append to --save-dir: i.e. runs/{N} -> runs/{N}_{name}')
parser.add_argument('--log-imgs', type=int, default=10, help='number of images for W&B logging, max 100')
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
......@@ -428,7 +428,7 @@ if __name__ == '__main__':
# Resume
if opt.resume: # resume an interrupted run
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
log_dir = Path(ckpt).parent.parent # runs/exp0
log_dir = Path(ckpt).parent.parent # runs/train/exp0
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
with open(log_dir / 'opt.yaml') as f:
opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace
......@@ -467,14 +467,13 @@ if __name__ == '__main__':
if opt.global_rank in [-1, 0]:
# Tensorboard
logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.logdir}", view at http://localhost:6006/')
tb_writer = SummaryWriter(log_dir=log_dir) # runs/exp0
tb_writer = SummaryWriter(log_dir=log_dir) # runs/train/exp0
# W&B
try:
import wandb
assert os.environ.get('WANDB_DISABLED') != 'true'
logger.info("Weights & Biases logging enabled, to disable set os.environ['WANDB_DISABLED'] = 'true'")
except (ImportError, AssertionError):
opt.log_imgs = 0
logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)")
......
tutorial.ipynb
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......@@ -596,22 +596,22 @@
}
},
"source": [
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source inference/images/\n",
"Image(filename='inference/output/zidane.jpg', width=600)"
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
"Image(filename='runs/detect/exp0/zidane.jpg', width=600)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='inference/output', save_txt=False, source='inference/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n",
"Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n",
"Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)\n",
"\n",
"Fusing layers... \n",
"Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients\n",
"image 1/2 /content/yolov5/inference/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)\n",
"image 2/2 /content/yolov5/inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)\n",
"Results saved to inference/output\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)\n",
"Results saved to runs/detect/exp0\n",
"Done. (0.113s)\n"
],
"name": "stdout"
......@@ -640,7 +640,7 @@
"id": "4qbaa3iEcrcE"
},
"source": [
"Results are saved to `inference/output`. A full list of available inference sources:\n",
"Results are saved to `runs/detect`. A full list of available inference sources:\n",
"<img src=\"https://user-images.githubusercontent.com/26833433/98274798-2b7a7a80-1f94-11eb-91a4-70c73593e26b.jpg\" width=\"900\"> "
]
},
......@@ -887,7 +887,7 @@
"source": [
"Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with dataset `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n",
"\n",
"All training results are saved to `runs/exp0` for the first experiment, then `runs/exp1`, `runs/exp2` etc. for subsequent experiments.\n"
"All training results are saved to `runs/train/exp0` for the first experiment, then `runs/exp1`, `runs/exp2` etc. for subsequent experiments.\n"
]
},
{
......@@ -969,7 +969,7 @@
"Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
"Image sizes 640 train, 640 test\n",
"Using 2 dataloader workers\n",
"Logging results to runs/exp0\n",
"Logging results to runs/train/exp0\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch gpu_mem box obj cls total targets img_size\n",
......@@ -986,8 +986,8 @@
" 2/2 3.17G 0.04445 0.06545 0.01666 0.1266 149 640: 100% 8/8 [00:01<00:00, 4.33it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 2.78it/s]\n",
" all 128 929 0.395 0.766 0.701 0.455\n",
"Optimizer stripped from runs/exp0/weights/last.pt, 15.2MB\n",
"Optimizer stripped from runs/exp0/weights/best.pt, 15.2MB\n",
"Optimizer stripped from runs/train/exp0/weights/last.pt, 15.2MB\n",
"Optimizer stripped from runs/train/exp0/weights/best.pt, 15.2MB\n",
"3 epochs completed in 0.005 hours.\n",
"\n"
],
......@@ -1030,7 +1030,7 @@
"source": [
"## Local Logging\n",
"\n",
"All results are logged by default to the `runs/exp0` directory, with a new directory created for each new training as `runs/exp1`, `runs/exp2`, etc. View train and test jpgs to see mosaics, labels/predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
"All results are logged by default to the `runs/train/exp0` directory, with a new directory created for each new training as `runs/exp1`, `runs/exp2`, etc. View train and test jpgs to see mosaics, labels/predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
]
},
{
......@@ -1039,9 +1039,9 @@
"id": "riPdhraOTCO0"
},
"source": [
"Image(filename='runs/exp0/train_batch0.jpg', width=800) # train batch 0 mosaics and labels\n",
"Image(filename='runs/exp0/test_batch0_gt.jpg', width=800) # test batch 0 ground truth\n",
"Image(filename='runs/exp0/test_batch0_pred.jpg', width=800) # test batch 0 predictions"
"Image(filename='runs/train/exp0/train_batch0.jpg', width=800) # train batch 0 mosaics and labels\n",
"Image(filename='runs/train/exp0/test_batch0_gt.jpg', width=800) # test batch 0 ground truth\n",
"Image(filename='runs/train/exp0/test_batch0_pred.jpg', width=800) # test batch 0 predictions"
],
"execution_count": null,
"outputs": []
......@@ -1078,7 +1078,7 @@
},
"source": [
"from utils.utils import plot_results \n",
"plot_results(save_dir='runs/exp0') # plot results.txt as results.png\n",
"plot_results(save_dir='runs/train/exp0') # plot results.txt as results.png\n",
"Image(filename='results.png', width=800) "
],
"execution_count": null,
......@@ -1170,9 +1170,9 @@
" for di in 0 cpu # inference devices\n",
" 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 detect.py --weights runs/train/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 test.py --weights runs/train/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",
......
utils/general.py
浏览文件 @ 4821d076
......@@ -955,9 +955,15 @@ def increment_dir(dir, comment=''):
# Increments a directory runs/exp1 --> runs/exp2_comment
n = 0 # number
dir = str(Path(dir)) # os-agnostic
if os.path.isdir(dir):
stem = ''
dir += os.sep # removed by Path
else:
stem = Path(dir).stem
dirs = sorted(glob.glob(dir + '*')) # directories
if dirs:
matches = [re.search(r"exp(\d+)", d) for d in dirs]
matches = [re.search(r"%s(\d+)" % stem, d) for d in dirs]
idxs = [int(m.groups()[0]) for m in matches if m]
if idxs:
n = max(idxs) + 1 # increment
......@@ -1262,7 +1268,7 @@ def plot_results_overlay(start=0, stop=0): # from utils.general import *; plot_
def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
# from utils.general import *; plot_results(save_dir='runs/exp0')
# from utils.general import *; plot_results(save_dir='runs/train/exp0')
# Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
fig, ax = plt.subplots(2, 5, figsize=(12, 6))
ax = ax.ravel()
......
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