Merge branch 'tf-only-export' into tf-android

.github/FUNDING.yml

@@ -0,0 +1,5 @@
+# These are supported funding model platforms
+
+github: glenn-jocher
+patreon: ultralytics
+open_collective: ultralytics

.github/workflows/ci-testing.yml

@@ -2,12 +2,10 @@ name: CI CPU testing
 
 on:  # https://help.github.com/en/actions/reference/events-that-trigger-workflows
   push:
-    branches: [ master ]
+    branches: [ master, develop ]
   pull_request:
     # The branches below must be a subset of the branches above
-    branches: [ master ]
-  schedule:
-    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+    branches: [ master, develop ]
 
 jobs:
   cpu-tests:
@@ -66,14 +64,15 @@ jobs:
           di=cpu # inference devices  # define device
 
           # train
-          python train.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di
+          python train.py --img 128 --batch 16 --weights weights/${{ 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/train/exp/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/train/exp/weights/last.pt --device $di
+          python test.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --device $di
+          python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
 
+          python hubconf.py  # hub
           python models/yolo.py --cfg models/${{ matrix.model }}.yaml  # inspect
-          python models/export.py --img 256 --batch 1 --weights weights/${{ matrix.model }}.pt  # export
+          python models/export.py --img 128 --batch 1 --weights weights/${{ matrix.model }}.pt  # export
         shell: bash

.github/workflows/greetings.yml

@@ -11,12 +11,12 @@ jobs:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           pr-message: |
             👋 Hello @${{ github.actor }}, thank you for submitting a 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
-            - ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master update by running the following, replacing 'feature' with the name of your local branch:
+            - ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' with the name of your local branch:
             ```bash
             git remote add upstream https://github.com/ultralytics/yolov5.git
             git fetch upstream
             git checkout feature  # <----- replace 'feature' with local branch name
-            git rebase upstream/master
+            git rebase upstream/develop
             git push -u origin -f
             ```
             - ✅ Verify all Continuous Integration (CI) **checks are passing**.
@@ -42,10 +42,11 @@ jobs:
             
             YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
             
-            - **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-            - **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)
-            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) 
-            - **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)
+            - **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
+            - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
+            - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+            
             
             ## Status
             

Dockerfile

@@ -1,14 +1,14 @@
 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:20.12-py3
+FROM nvcr.io/nvidia/pytorch:21.03-py3
 
 # Install linux packages
-RUN apt update && apt install -y screen libgl1-mesa-glx
+RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
 
 # Install python dependencies
-RUN pip install --upgrade pip
 COPY requirements.txt .
-RUN pip install -r requirements.txt
-RUN pip install gsutil
+RUN python -m pip install --upgrade pip
+RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
+RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook
 
 # Create working directory
 RUN mkdir -p /usr/src/app
@@ -17,11 +17,8 @@ WORKDIR /usr/src/app
 # Copy contents
 COPY . /usr/src/app
 
-# Copy weights
-#RUN python3 -c "from models import *; \
-#attempt_download('weights/yolov5s.pt'); \
-#attempt_download('weights/yolov5m.pt'); \
-#attempt_download('weights/yolov5l.pt')"
+# Set environment variables
+ENV HOME=/usr/src/app
 
 
 # ---------------------------------------------------  Extras Below  ---------------------------------------------------
@@ -40,13 +37,13 @@ COPY . /usr/src/app
 # sudo docker kill $(sudo docker ps -q)
 
 # Kill all image-based
-# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest)
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
 
 # Bash into running container
-# sudo docker container exec -it ba65811811ab bash
+# sudo docker exec -it 5a9b5863d93d bash
 
 # Bash into stopped container
-# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume
+# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
 
 # Send weights to GCP
 # python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt

README.md

@@ -23,7 +23,7 @@ https://github.com/ultralytics/yolov5.git
 ### 2. Install requirements
 ```
 pip install -r requirements.txt
-pip install tensorflow==2.4.0
+pip install tensorflow==2.4.1
 ```
 
 ### 3. Convert and verify

data/GlobalWheat2020.yaml

@@ -0,0 +1,55 @@
+# Global Wheat 2020 dataset http://www.global-wheat.com/
+# Train command: python train.py --data GlobalWheat2020.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /datasets/GlobalWheat2020
+#     /yolov5
+
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: # 3422 images
+  - ../datasets/GlobalWheat2020/images/arvalis_1
+  - ../datasets/GlobalWheat2020/images/arvalis_2
+  - ../datasets/GlobalWheat2020/images/arvalis_3
+  - ../datasets/GlobalWheat2020/images/ethz_1
+  - ../datasets/GlobalWheat2020/images/rres_1
+  - ../datasets/GlobalWheat2020/images/inrae_1
+  - ../datasets/GlobalWheat2020/images/usask_1
+
+val: # 748 images (WARNING: train set contains ethz_1)
+  - ../datasets/GlobalWheat2020/images/ethz_1
+
+test: # 1276 images
+  - ../datasets/GlobalWheat2020/images/utokyo_1
+  - ../datasets/GlobalWheat2020/images/utokyo_2
+  - ../datasets/GlobalWheat2020/images/nau_1
+  - ../datasets/GlobalWheat2020/images/uq_1
+
+# number of classes
+nc: 1
+
+# class names
+names: [ 'wheat_head' ]
+
+
+# download command/URL (optional) --------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, Path
+
+  # Download
+  dir = Path('../datasets/GlobalWheat2020')  # dataset directory
+  urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
+  download(urls, dir=dir)
+
+  # Make Directories
+  for p in 'annotations', 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+
+  # Move
+  for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+           'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+      (dir / p).rename(dir / 'images' / p)  # move to /images
+      f = (dir / p).with_suffix('.json')  # json file
+      if f.exists():
+          f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations

data/SKU-110K.yaml

@@ -0,0 +1,52 @@
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
+# Train command: python train.py --data SKU-110K.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /datasets/SKU-110K
+#     /yolov5
+
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../datasets/SKU-110K/train.txt  # 8219 images
+val: ../datasets/SKU-110K/val.txt  # 588 images
+test: ../datasets/SKU-110K/test.txt  # 2936 images
+
+# number of classes
+nc: 1
+
+# class names
+names: [ 'object' ]
+
+
+# download command/URL (optional) --------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from tqdm import tqdm
+  from utils.general import np, pd, Path, download, xyxy2xywh
+
+  # Download
+  datasets = Path('../datasets')  # download directory
+  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+  download(urls, dir=datasets, delete=False)
+
+  # Rename directories
+  dir = (datasets / 'SKU-110K')
+  if dir.exists():
+      shutil.rmtree(dir)
+  (datasets / 'SKU110K_fixed').rename(dir)  # rename dir
+  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names
+  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+          f.writelines(f'./images/{s}\n' for s in unique_images)
+      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+          cls = 0  # single-class dataset
+          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label

data/VisDrone.yaml

@@ -0,0 +1,61 @@
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
+# Train command: python train.py --data VisDrone.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /VisDrone
+#     /yolov5
+
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../VisDrone/VisDrone2019-DET-train/images  # 6471 images
+val: ../VisDrone/VisDrone2019-DET-val/images  # 548 images
+test: ../VisDrone/VisDrone2019-DET-test-dev/images  # 1610 images
+
+# number of classes
+nc: 10
+
+# class names
+names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
+
+
+# download command/URL (optional) --------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, os, Path
+
+  def visdrone2yolo(dir):
+      from PIL import Image
+      from tqdm import tqdm
+
+      def convert_box(size, box):
+          # Convert VisDrone box to YOLO xywh box
+          dw = 1. / size[0]
+          dh = 1. / size[1]
+          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
+      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+      for f in pbar:
+          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+          lines = []
+          with open(f, 'r') as file:  # read annotation.txt
+              for row in [x.split(',') for x in file.read().strip().splitlines()]:
+                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
+                      continue
+                  cls = int(row[5]) - 1
+                  box = convert_box(img_size, tuple(map(int, row[:4])))
+                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+                      fl.writelines(lines)  # write label.txt
+
+
+  # Download
+  dir = Path('../VisDrone')  # dataset directory
+  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
+          '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)
+
+  # Convert
+  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

data/argoverse_hd.yaml

@@ -0,0 +1,21 @@
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
+# Train command: python train.py --data argoverse_hd.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /argoverse
+#     /yolov5
+
+
+# download command/URL (optional)
+download: bash data/scripts/get_argoverse_hd.sh
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../argoverse/Argoverse-1.1/images/train/  # 39384 images
+val: ../argoverse/Argoverse-1.1/images/val/  # 15062 iamges
+test: ../argoverse/Argoverse-1.1/images/test/  # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview
+
+# number of classes
+nc: 8
+
+# class names
+names: [ 'person',  'bicycle',  'car',  'motorcycle',  'bus',  'truck',  'traffic_light',  'stop_sign' ]

data/coco.yaml

@@ -1,6 +1,6 @@
 # COCO 2017 dataset http://cocodataset.org
 # Train command: python train.py --data coco.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /coco
 #     /yolov5
@@ -30,6 +30,6 @@ names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', '
 
 # Print classes
 # with open('data/coco.yaml') as f:
-#   d = yaml.load(f, Loader=yaml.FullLoader)  # dict
+#   d = yaml.safe_load(f)  # dict
 #   for i, x in enumerate(d['names']):
 #     print(i, x)

data/coco128.yaml

@@ -1,6 +1,6 @@
 # COCO 2017 dataset http://cocodataset.org - first 128 training images
 # Train command: python train.py --data coco128.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /coco128
 #     /yolov5

data/hyp.finetune_objects365.yaml

@@ -0,0 +1,28 @@
+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