update to ultralytics/yolov5 04.04.22 (#90)

* update to ultralytics/yolov5 04.04.22 * fix train args * update data_dict * fix val data_dict * load weight inside yolov5 context * handle fire args in export * attempt to fix export * update comment * add differences in readme * update readme
fcakyon · Apr 8, 2022 · b0bac98 · b0bac98
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diff --git a/README.md b/README.md
@@ -19,10 +19,20 @@
 
 <div align="center">
 You can finally install <a href="https://github.com/ultralytics/yolov5">YOLOv5 object detector</a> using <a href="https://pypi.org/project/yolov5/">pip</a> and integrate into your project easily.
-</p>
 <img src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png" width="1000">
+<br>
+This yolov5 package contains everything from ultralytics/yolov5 <a href="https://github.com/ultralytics/yolov5/commit/ea72b84f5e690cb516642ce2d9ae200145b0af34">at this commit</a> plus:
+<br>
+1. NeptuneAI logger support (metric, model and dataset logging)
+<br>
+2. S3 support (model and dataset upload)
+<br>
+3. Classwise AP logging during experiment
+<br>
+4. COCO dataset format support (for training)
 </div>
 
+
 ## <div align="center">Install</div>
 
 <details open>

diff --git a/requirements.txt b/requirements.txt
@@ -39,4 +39,4 @@ fire
 # AWS
 boto3>=1.19.1
 # coco to yolov5 conversion
-sahi>=0.8.9
+sahi>=0.9.1
diff --git a/yolov5/__init__.py b/yolov5/__init__.py
@@ -1,4 +1,4 @@
 from yolov5.helpers import YOLOv5
 from yolov5.helpers import load_model as load
 
-__version__ = "6.0.7"
+__version__ = "6.1.0"
diff --git a/yolov5/data/Argoverse.yaml b/yolov5/data/Argoverse.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
 # Example usage: python train.py --data Argoverse.yaml
 # parent
 # ├── yolov5

diff --git a/yolov5/data/GlobalWheat2020.yaml b/yolov5/data/GlobalWheat2020.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Global Wheat 2020 dataset http://www.global-wheat.com/
+# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
 # Example usage: python train.py --data GlobalWheat2020.yaml
 # parent
 # ├── yolov5
@@ -34,6 +34,7 @@ names: ['wheat_head']  # class names
 download: |
   from utils.general import download, Path
 
+
   # Download
   dir = Path(yaml['path'])  # dataset root dir
   urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',

diff --git a/yolov5/data/Objects365.yaml b/yolov5/data/Objects365.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Objects365 dataset https://www.objects365.org/
+# Objects365 dataset https://www.objects365.org/ by Megvii
 # Example usage: python train.py --data Objects365.yaml
 # parent
 # ├── yolov5
@@ -62,21 +62,22 @@ names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Gla
 download: |
   from pycocotools.coco import COCO
   from tqdm import tqdm
-  
+
   from utils.general import Path, download, np, xyxy2xywhn
-  
+
+
   # Make Directories
   dir = Path(yaml['path'])  # dataset root dir
   for p in 'images', 'labels':
       (dir / p).mkdir(parents=True, exist_ok=True)
       for q in 'train', 'val':
           (dir / p / q).mkdir(parents=True, exist_ok=True)
-  
+
   # Train, Val Splits
   for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
       print(f"Processing {split} in {patches} patches ...")
       images, labels = dir / 'images' / split, dir / 'labels' / split
-  
+
       # Download
       url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
       if split == 'train':
@@ -86,11 +87,11 @@ download: |
           download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
           download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
           download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
-  
+
       # Move
       for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
           f.rename(images / f.name)  # move to /images/{split}
-  
+
       # Labels
       coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
       names = [x["name"] for x in coco.loadCats(coco.getCatIds())]

diff --git a/yolov5/data/SKU-110K.yaml b/yolov5/data/SKU-110K.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
 # Example usage: python train.py --data SKU-110K.yaml
 # parent
 # ├── yolov5
@@ -24,6 +24,7 @@ download: |
   from tqdm import tqdm
   from utils.general import np, pd, Path, download, xyxy2xywh
 
+
   # Download
   dir = Path(yaml['path'])  # dataset root dir
   parent = Path(dir.parent)  # download dir

diff --git a/yolov5/data/VOC.yaml b/yolov5/data/VOC.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
 # Example usage: python train.py --data VOC.yaml
 # parent
 # ├── yolov5
@@ -62,7 +62,7 @@ download: |
   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)
+  download(urls, dir=dir / 'images', delete=False, threads=3)
 
   # Convert
   path = dir / f'images/VOCdevkit'

diff --git a/yolov5/data/VisDrone.yaml b/yolov5/data/VisDrone.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
 # Example usage: python train.py --data VisDrone.yaml
 # parent
 # ├── yolov5
@@ -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)
+  download(urls, dir=dir, threads=4)
 
   # Convert
   for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':

diff --git a/yolov5/data/coco.yaml b/yolov5/data/coco.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org
+# COCO 2017 dataset http://cocodataset.org by Microsoft
 # Example usage: python train.py --data coco.yaml
 # parent
 # ├── yolov5
@@ -10,7 +10,7 @@
 # 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, ..]
 path: ../datasets/coco  # dataset root dir
 train: train2017.txt  # train images (relative to 'path') 118287 images
-val: val2017.txt  # train images (relative to 'path') 5000 images
+val: val2017.txt  # val images (relative to 'path') 5000 images
 test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
 
 # Classes
@@ -30,6 +30,7 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
 download: |
   from utils.general import download, Path
 
+
   # Download labels
   segments = False  # segment or box labels
   dir = Path(yaml['path'])  # dataset root dir

diff --git a/yolov5/data/coco128.yaml b/yolov5/data/coco128.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: python train.py --data coco128.yaml
 # parent
 # ├── yolov5
@@ -27,4 +27,4 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
 
 
 # Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
+download: https://ultralytics.com/assets/coco128.zip
diff --git a/yolov5/data/hyps/hyp.Objects365.yaml b/yolov5/data/hyps/hyp.Objects365.yaml
@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for Objects365 training
+# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.00258
+lrf: 0.17
+momentum: 0.779
+weight_decay: 0.00058
+warmup_epochs: 1.33
+warmup_momentum: 0.86
+warmup_bias_lr: 0.0711
+box: 0.0539
+cls: 0.299
+cls_pw: 0.825
+obj: 0.632
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 3.44
+anchors: 3.2
+fl_gamma: 0.0
+hsv_h: 0.0188
+hsv_s: 0.704
+hsv_v: 0.36
+degrees: 0.0
+translate: 0.0902
+scale: 0.491
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
diff --git a/yolov5/data/hyps/hyp.VOC.yaml b/yolov5/data/hyps/hyp.VOC.yaml
@@ -0,0 +1,40 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for VOC training
+# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+# YOLOv5 Hyperparameter Evolution Results
+# Best generation: 467
+# Last generation: 996
+#    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss
+#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865
+
+lr0: 0.00334
+lrf: 0.15135
+momentum: 0.74832
+weight_decay: 0.00025
+warmup_epochs: 3.3835
+warmup_momentum: 0.59462
+warmup_bias_lr: 0.18657
+box: 0.02
+cls: 0.21638
+cls_pw: 0.5
+obj: 0.51728
+obj_pw: 0.67198
+iou_t: 0.2
+anchor_t: 3.3744
+fl_gamma: 0.0
+hsv_h: 0.01041
+hsv_s: 0.54703
+hsv_v: 0.27739
+degrees: 0.0
+translate: 0.04591
+scale: 0.75544
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 0.85834
+mixup: 0.04266
+copy_paste: 0.0
+anchors: 3.412
diff --git a/yolov5/data/hyps/hyp.scratch-high.yaml b/yolov5/data/hyps/hyp.scratch-high.yaml
@@ -4,7 +4,7 @@
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
 
 lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
 momentum: 0.937  # SGD momentum/Adam beta1
 weight_decay: 0.0005  # optimizer weight decay 5e-4
 warmup_epochs: 3.0  # warmup epochs (fractions ok)
@@ -31,4 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.1  # image mixup (probability)
-copy_paste: 0.1  # segment copy-paste (probability)
+copy_paste: 0.1  # segment copy-paste (probability)
diff --git a/yolov5/data/hyps/hyp.scratch-low.yaml b/yolov5/data/hyps/hyp.scratch-low.yaml
@@ -31,4 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
+copy_paste: 0.0  # segment copy-paste (probability)
diff --git a/yolov5/data/scripts/download_weights.sh b/yolov5/data/scripts/download_weights.sh
@@ -11,7 +11,10 @@
 python - <<EOF
 from utils.downloads import attempt_download
 
-for x in ['s', 'm', 'l', 'x']:
+models = ['n', 's', 'm', 'l', 'x']
+models.extend([x + '6' for x in models])  # add P6 models
+
+for x in models:
     attempt_download(f'yolov5{x}.pt')
 
 EOF
diff --git a/yolov5/data/xView.yaml b/yolov5/data/xView.yaml
@@ -1,6 +1,6 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# xView 2018 dataset https://challenge.xviewdataset.org
-# --------  DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command!  --------
+# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
+# --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
 # Example usage: python train.py --data xView.yaml
 # parent
 # ├── yolov5