Increment train, test, detect runs/ (#1322)

.github/workflows/ci-testing.yml

@@ -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

@@ -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

@@ -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

@@ -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

 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

@@ -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)

test.py

@@ -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

 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

@@ -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

@@ -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()