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ultralytics · Oct 23, 2021 · 84c4072 · 84c4072
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diff --git a/.github/dependabot.yml b/.github/dependabot.yml
@@ -10,3 +10,14 @@ updates:
       - glenn-jocher
     labels:
       - dependencies
+
+  - package-ecosystem: github-actions
+    directory: "/"
+    schedule:
+      interval: weekly
+      time: "04:00"
+    open-pull-requests-limit: 5
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies
diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml
@@ -20,7 +20,7 @@ jobs:
       matrix:
         os: [ ubuntu-latest, macos-latest, windows-latest ]
         python-version: [ 3.8 ]
-        model: [ 'yolov5s' ]  # models to test
+        model: [ 'yolov5n' ]  # models to test
 
     # Timeout: https://stackoverflow.com/a/59076067/4521646
     timeout-minutes: 50
@@ -39,7 +39,7 @@ jobs:
           python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
 
       - name: Cache pip
-        uses: actions/cache@v1
+        uses: actions/cache@v2.1.6
         with:
           path: ${{ steps.pip-cache.outputs.dir }}
           key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}

diff --git a/.github/workflows/rebase.yml b/.github/workflows/rebase.yml
@@ -1,10 +1,9 @@
-name: Automatic Rebase
 # https://github.com/marketplace/actions/automatic-rebase
 
+name: Automatic Rebase
 on:
   issue_comment:
     types: [created]
-
 jobs:
   rebase:
     name: Rebase
@@ -14,8 +13,9 @@ jobs:
       - name: Checkout the latest code
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
+          token: ${{ secrets.ACTIONS_TOKEN }}
+          fetch-depth: 0 # otherwise, you will fail to push refs to dest repo
       - name: Automatic Rebase
-        uses: cirrus-actions/rebase@1.3.1
+        uses: cirrus-actions/rebase@1.5
         env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GITHUB_TOKEN: ${{ secrets.ACTIONS_TOKEN }}
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
@@ -9,7 +9,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@v3
+      - uses: actions/stale@v4
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           stale-issue-message: |
@@ -21,7 +21,7 @@ jobs:
             - **Docs** – https://docs.ultralytics.com
 
             Access additional [Ultralytics](https://ultralytics.com) ⚡ resources:
-            - **Ultralytics HUB** – https://ultralytics.com
+            - **Ultralytics HUB** – https://ultralytics.com/hub
             - **Vision API** – https://ultralytics.com/yolov5
             - **About Us** – https://ultralytics.com/about
             - **Join Our Team** – https://ultralytics.com/work
@@ -31,7 +31,7 @@ jobs:
 
             Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!
 
-          stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
+          stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
           days-before-stale: 30
           days-before-close: 5
           exempt-issue-labels: 'documentation,tutorial'

diff --git a/Dockerfile b/Dockerfile
@@ -21,8 +21,11 @@ WORKDIR /usr/src/app
 # Copy contents
 COPY . /usr/src/app
 
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/
+
 # Set environment variables
-ENV HOME=/usr/src/app
+# ENV HOME=/usr/src/app
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------

diff --git a/README.md b/README.md
@@ -191,7 +191,7 @@ Get started in seconds with our verified environments. Click each icon below for
     </a>
 </div>
 
-|Weights and Biases|Roboflow - ⭐ NEW|
+|Weights and Biases|Roboflow ⭐ NEW|
 |:-:|:-:|
 |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and automatically export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
 
@@ -207,66 +207,60 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 
 ## <div align="center">Why YOLOv5</div>
 
-<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"></p>
 <details>
   <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
 
-<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"></p>
 </details>
 <details>
   <summary>Figure Notes (click to expand)</summary>
 
-* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size
-  32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
-* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
-* **Reproduce** by
-  `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-
+* **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
+* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
+* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
+* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
 </details>
 
 ### Pretrained Checkpoints
 
 [assets]: https://github.com/ultralytics/yolov5/releases
-
-|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPs<br><sup>640 (B)
-|---                    |---  |---      |---      |---      |---     |---|---   |---
-|[YOLOv5s][assets]      |640  |36.7     |36.7     |55.4     |**2.0** |   |7.3   |17.0
-|[YOLOv5m][assets]      |640  |44.5     |44.5     |63.1     |2.7     |   |21.4  |51.3
-|[YOLOv5l][assets]      |640  |48.2     |48.2     |66.9     |3.8     |   |47.0  |115.4
-|[YOLOv5x][assets]      |640  |**50.4** |**50.4** |**68.8** |6.1     |   |87.7  |218.8
-|                       |     |         |         |         |        |   |      |
-|[YOLOv5s6][assets]     |1280 |43.3     |43.3     |61.9     |**4.3** |   |12.7  |17.4
-|[YOLOv5m6][assets]     |1280 |50.5     |50.5     |68.7     |8.4     |   |35.9  |52.4
-|[YOLOv5l6][assets]     |1280 |53.4     |53.4     |71.1     |12.3    |   |77.2  |117.7
-|[YOLOv5x6][assets]     |1280 |**54.4** |**54.4** |**72.0** |22.4    |   |141.8 |222.9
-|                       |     |         |         |         |        |   |      |
-|[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8    |   |-     |-
+[TTA]: https://github.com/ultralytics/yolov5/issues/303
+
+|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
+|---                    |---  |---    |---    |---    |---    |---    |---    |---
+|[YOLOv5n][assets]      |640  |28.4   |46.0   |**45** |**6.3**|**0.6**|**1.9**|**4.5**
+|[YOLOv5s][assets]      |640  |37.2   |56.0   |98     |6.4    |0.9    |7.2    |16.5
+|[YOLOv5m][assets]      |640  |45.2   |63.9   |224    |8.2    |1.7    |21.2   |49.0
+|[YOLOv5l][assets]      |640  |48.8   |67.2   |430    |10.1   |2.7    |46.5   |109.1
+|[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7
+|                       |     |       |       |       |       |       |       |
+|[YOLOv5n6][assets]     |1280 |34.0   |50.7   |153    |8.1    |2.1    |3.2    |4.6
+|[YOLOv5s6][assets]     |1280 |44.5   |63.0   |385    |8.2    |3.6    |16.8   |12.6
+|[YOLOv5m6][assets]     |1280 |51.0   |69.0   |887    |11.1   |6.8    |35.7   |50.0
+|[YOLOv5l6][assets]     |1280 |53.6   |71.6   |1784   |15.8   |10.5   |76.8   |111.4
+|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>- 
 
 <details>
   <summary>Table Notes (click to expand)</summary>
 
 * All checkpoints are trained to 300 epochs with default settings and hyperparameters.
-* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results
-  denote val2017 accuracy.
-* **mAP** values are for single-model single-scale unless otherwise noted.<br>**Reproduce** by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-* **Speed** averaged over 5000 COCO val2017 images using a
-  GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and
-  includes FP16 inference, postprocessing and NMS.<br>**Reproduce**
-  by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
-* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale.<br>**Reproduce** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+* **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
+* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 
 </details>
 
 ## <div align="center">Contribute</div>
 
-We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see
-our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out
-the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to provide 
-thoughts and feedback on your experience with YOLOv5. Thank you!
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
+
 
 ## <div align="center">Contact</div>
 
-For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or
+For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
 professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
 
 <br>

diff --git a/data/Objects365.yaml b/data/Objects365.yaml
@@ -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/Objects365  # dataset root dir
 train: images/train  # train images (relative to 'path') 1742289 images
-val: images/val # val images (relative to 'path') 5570 images
+val: images/val # val images (relative to 'path') 80000 images
 test:  # test images (optional)
 
 # Classes
@@ -62,43 +62,51 @@ names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Gla
 download: |
   from pycocotools.coco import COCO
   from tqdm import tqdm
-
-  from utils.general import download, Path
-
+  
+  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)
-
-  # Download
-  url = "https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/"
-  download([url + 'zhiyuan_objv2_train.tar.gz'], dir=dir, delete=False)  # annotations json
-  download([url + f for f in [f'patch{i}.tar.gz' for i in range(51)]], dir=dir / 'images' / 'train',
-           curl=True, delete=False, threads=8)
-
-  # Move
-  train = dir / 'images' / 'train'
-  for f in tqdm(train.rglob('*.jpg'), desc=f'Moving images'):
-      f.rename(train / f.name)  # move to /images/train
-
-  # Labels
-  coco = COCO(dir / 'zhiyuan_objv2_train.json')
-  names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
-  for cid, cat in enumerate(names):
-      catIds = coco.getCatIds(catNms=[cat])
-      imgIds = coco.getImgIds(catIds=catIds)
-      for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
-          width, height = im["width"], im["height"]
-          path = Path(im["file_name"])  # image filename
-          try:
-              with open(dir / 'labels' / 'train' / path.with_suffix('.txt').name, 'a') as file:
-                  annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
-                  for a in coco.loadAnns(annIds):
-                      x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
-                      x, y = x + w / 2, y + h / 2  # xy to center
-                      file.write(f"{cid} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
-
-          except Exception as e:
-              print(e)
+  
+  # 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':
+          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
+          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+      elif split == 'val':
+          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())]
+      for cid, cat in enumerate(names):
+          catIds = coco.getCatIds(catNms=[cat])
+          imgIds = coco.getImgIds(catIds=catIds)
+          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+              width, height = im["width"], im["height"]
+              path = Path(im["file_name"])  # image filename
+              try:
+                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
+                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+                      for a in coco.loadAnns(annIds):
+                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+              except Exception as e:
+                  print(e)
diff --git a/data/hyps/hyp.scratch-p6.yaml → data/hyps/hyp.scratch-high.yaml b/data/hyps/hyp.scratch-p6.yaml → data/hyps/hyp.scratch-high.yaml
@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for COCO training from scratch
+# Hyperparameters for high-augmentation COCO training from scratch
 # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
 
@@ -30,5 +30,5 @@ perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
 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)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.1  # segment copy-paste (probability)
diff --git a/data/hyps/hyp.scratch-low.yaml b/data/hyps/hyp.scratch-low.yaml
@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01  # 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)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.5  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 1.0  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+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)