update to ultralytics/yolov5 04.08.22 (#131)

* update files

* update readme

* fix val

* update version to 6.1.7

README.md

@@ -25,7 +25,7 @@ You can finally install <a href="https://github.com/ultralytics/yolov5">YOLOv5 o
 </div>
 
 <br>
-This yolov5 package contains everything from ultralytics/yolov5 <a href="https://github.com/ultralytics/yolov5/tree/177da7f348181abdf4820ad26707eb8b3dd4fdc9">at this commit</a> plus:
+This yolov5 package contains everything from ultralytics/yolov5 <a href="https://github.com/ultralytics/yolov5/tree/84e7748564f83ba04601770f17a38cc55e6be661">at this commit</a> plus:
 <br>
 1. Easy installation via pip: `pip install yolov5`
 <br>

requirements.txt

@@ -1,40 +1,46 @@
-# pip install -r requirements.txt
+# YOLOv5 requirements
+# Usage: pip install -r requirements.txt
 
 # Base ----------------------------------------
 matplotlib>=3.2.2
 numpy>=1.18.5
-opencv-python>=4.1.2
+opencv-python>=4.1.1
 Pillow>=7.1.2
 PyYAML>=5.3.1
 requests>=2.23.0
 scipy>=1.4.1
 torch>=1.7.0
 torchvision>=0.8.1
-tqdm>=4.41.0
+tqdm>=4.64.0
+protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
 
 # Logging -------------------------------------
 tensorboard>=2.4.1
-protobuf<=3.20.1
 # wandb
 
 # Plotting ------------------------------------
 pandas>=1.1.4
 seaborn>=0.11.0
 
 # Export --------------------------------------
-# coremltools>=4.1  # CoreML export
+# coremltools>=5.2  # CoreML export
 # onnx>=1.9.0  # ONNX export
-# onnx-simplifier>=0.3.6  # ONNX simplifier
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
 # scikit-learn==0.19.2  # CoreML quantization
-# tensorflow>=2.4.1  # TFLite export
+# tensorflow>=2.4.1  # TFLite export (or tensorflow-cpu, tensorflow-aarch64)
 # tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
 
 # Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop>=0.1.1  # FLOPs computation
 # albumentations>=1.0.3
-# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
 # pycocotools>=2.0  # COCO mAP
 # roboflow
-thop  # FLOPs computation
+
 # CLI
 fire
 # AWS

yolov5/__init__.py

@@ -1,4 +1,4 @@
 from yolov5.helpers import YOLOv5
 from yolov5.helpers import load_model as load
 
-__version__ = "6.1.6"
+__version__ = "6.1.7"

yolov5/data/Argoverse.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── Argoverse  ← downloads here
+#     └── Argoverse  ← downloads here (31.3 GB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
@@ -32,7 +32,7 @@ download: |
       for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
           img_id = annot['image_id']
           img_name = a['images'][img_id]['name']
-          img_label_name = img_name[:-3] + "txt"
+          img_label_name = f'{img_name[:-3]}txt'
 
           cls = annot['category_id']  # instance class id
           x_center, y_center, width, height = annot['bbox']

yolov5/data/GlobalWheat2020.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── GlobalWheat2020  ← downloads here
+#     └── GlobalWheat2020  ← downloads here (7.0 GB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

yolov5/data/Objects365.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── Objects365  ← downloads here
+#     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
@@ -60,11 +60,12 @@ names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Gla
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
-  from pycocotools.coco import COCO
   from tqdm import tqdm
 
-  from utils.general import Path, download, np, xyxy2xywhn
+  from utils.general import Path, check_requirements, download, np, xyxy2xywhn
 
+  check_requirements(('pycocotools>=2.0',))
+  from pycocotools.coco import COCO
 
   # Make Directories
   dir = Path(yaml['path'])  # dataset root dir

yolov5/data/SKU-110K.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── SKU-110K  ← downloads here
+#     └── SKU-110K  ← downloads here (13.6 GB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

yolov5/data/VOC.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── VOC  ← downloads here
+#     └── VOC  ← downloads here (2.8 GB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
@@ -59,20 +59,21 @@ download: |
   # Download
   dir = Path(yaml['path'])  # dataset root dir
   url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
-  urls = [url + 'VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
-          url + 'VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
-          url + 'VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
-  download(urls, dir=dir / 'images', delete=False, threads=3)
+  urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
+          f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
+          f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
+  download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
 
   # Convert
-  path = dir / f'images/VOCdevkit'
+  path = dir / 'images/VOCdevkit'
   for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
       imgs_path = dir / 'images' / f'{image_set}{year}'
       lbs_path = dir / 'labels' / f'{image_set}{year}'
       imgs_path.mkdir(exist_ok=True, parents=True)
       lbs_path.mkdir(exist_ok=True, parents=True)
 
-      image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split()
+      with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+          image_ids = f.read().strip().split()
       for id in tqdm(image_ids, desc=f'{image_set}{year}'):
           f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
           lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path

yolov5/data/VisDrone.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── VisDrone  ← downloads here
+#     └── VisDrone  ← downloads here (2.3 GB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
@@ -54,7 +54,7 @@ download: |
           'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
           'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
           'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
-  download(urls, dir=dir, threads=4)
+  download(urls, dir=dir, curl=True, threads=4)
 
   # Convert
   for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':

yolov5/data/coco.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── coco  ← downloads here
+#     └── coco  ← downloads here (20.1 GB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

yolov5/data/coco128.yaml

@@ -4,7 +4,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── coco128  ← downloads here
+#     └── coco128  ← downloads here (7 MB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

yolov5/data/xView.yaml

@@ -5,7 +5,7 @@
 # parent
 # ├── yolov5
 # └── datasets
-#     └── xView  ← downloads here
+#     └── xView  ← downloads here (20.7 GB)
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

yolov5/detect.py

@@ -3,7 +3,7 @@
 Run inference on images, videos, directories, streams, etc.
 
 Usage - sources:
-    $ yolov5 detect --weights yolov5s.pt --source 0              # webcam
+    $ yolov5 detect --weights yolov5s.pt --source 0                         # webcam
                                                              img.jpg        # image
                                                              vid.mp4        # video
                                                              path/          # directory
@@ -12,7 +12,7 @@
                                                              'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 
 Usage - formats:
-    $ yolov5 detect --weights yolov5s.pt                 # PyTorch
+    $ yolov5 detect --weights yolov5s.pt                            # PyTorch
                                          yolov5s.torchscript        # TorchScript
                                          yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                          yolov5s.xml                # OpenVINO
@@ -26,7 +26,7 @@
 
 import argparse
 import os
-import sys
+import platform
 from pathlib import Path
 
 import torch
@@ -37,10 +37,10 @@
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from yolov5.models.common import DetectMultiBackend
-from yolov5.utils.datasets import (IMG_FORMATS, VID_FORMATS, LoadImages,
-                                   LoadStreams)
+from yolov5.utils.dataloaders import (IMG_FORMATS, VID_FORMATS, LoadImages,
+                                      LoadStreams)
 from yolov5.utils.general import (LOGGER, check_file, check_img_size,
-                                  check_imshow, check_requirements, colorstr,
+                                  check_imshow, colorstr,
                                   cv2, increment_path, non_max_suppression,
                                   print_args, scale_coords, strip_optimizer,
                                   xyxy2xywh)
@@ -117,7 +117,7 @@ def run(
 
     # Run inference
     model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
-    dt, seen = [0.0, 0.0, 0.0], 0
+    seen, windows, dt = 0, [], [0.0, 0.0, 0.0]
     for path, im, im0s, vid_cap, s in dataset:
         t1 = time_sync()
         im = torch.from_numpy(im).to(device)
@@ -171,19 +171,23 @@ def run(
                     if save_txt:  # Write to file
                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                         line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
-                        with open(txt_path + '.txt', 'a') as f:
+                        with open(f'{txt_path}.txt', 'a') as f:
                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
                     if save_img or save_crop or view_img:  # Add bbox to image
                         c = int(cls)  # integer class
                         label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                         annotator.box_label(xyxy, label, color=colors(c, True))
-                        if save_crop:
-                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+                    if save_crop:
+                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 
             # Stream results
             im0 = annotator.result()
             if view_img:
+                if platform.system() == 'Linux' and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                 cv2.imshow(str(p), im0)
                 cv2.waitKey(1)  # 1 millisecond
 
@@ -216,7 +220,7 @@ def run(
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
         LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
     if update:
-        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
 
 
 def parse_opt():