Merge branch 'master' into freeze

.github/ISSUE_TEMPLATE/feature-request.md

@@ -8,6 +8,7 @@ assignees: ''
 ---
 
 ## 🚀 Feature
+
 <!-- A clear and concise description of the feature proposal -->
 
 ## Motivation

.github/dependabot.yml

@@ -1,12 +1,12 @@
 version: 2
 updates:
-- package-ecosystem: pip
-  directory: "/"
-  schedule:
-    interval: weekly
-    time: "04:00"
-  open-pull-requests-limit: 10
-  reviewers:
-  - glenn-jocher
-  labels:
-  - dependencies
+  - package-ecosystem: pip
+    directory: "/"
+    schedule:
+      interval: weekly
+      time: "04:00"
+    open-pull-requests-limit: 10
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies

.github/workflows/ci-testing.yml

@@ -1,6 +1,6 @@
 name: CI CPU testing
 
-on:  # https://help.github.com/en/actions/reference/events-that-trigger-workflows
+on: # https://help.github.com/en/actions/reference/events-that-trigger-workflows
   push:
     branches: [ master, develop ]
   pull_request:
@@ -14,9 +14,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.8]
-        model: ['yolov5s']  # models to test
+        os: [ ubuntu-latest, macos-latest, windows-latest ]
+        python-version: [ 3.8 ]
+        model: [ 'yolov5s' ]  # models to test
 
     # Timeout: https://stackoverflow.com/a/59076067/4521646
     timeout-minutes: 50
@@ -74,5 +74,5 @@ jobs:
 
           python hubconf.py  # hub
           python models/yolo.py --cfg ${{ matrix.model }}.yaml  # inspect
-          python models/export.py --img 128 --batch 1 --weights ${{ matrix.model }}.pt  # export
+          python export.py --img 128 --batch 1 --weights ${{ matrix.model }}.pt  # export
         shell: bash

.github/workflows/codeql-analysis.yml

@@ -21,34 +21,34 @@ jobs:
         # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
 
     steps:
-    - name: Checkout repository
-      uses: actions/checkout@v2
-
-    # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
-      uses: github/codeql-action/init@v1
-      with:
-        languages: ${{ matrix.language }}
-        # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
-        # queries: ./path/to/local/query, your-org/your-repo/queries@main
-
-    # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
-    # If this step fails, then you should remove it and run the build manually (see below)
-    - name: Autobuild
-      uses: github/codeql-action/autobuild@v1
-
-    # ℹ️ Command-line programs to run using the OS shell.
-    # 📚 https://git.io/JvXDl
-
-    # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
-    #    and modify them (or add more) to build your code if your project
-    #    uses a compiled language
-
-    #- run: |
-    #   make bootstrap
-    #   make release
-
-    - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v1
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      # Initializes the CodeQL tools for scanning.
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@v1
+        with:
+          languages: ${{ matrix.language }}
+          # If you wish to specify custom queries, you can do so here or in a config file.
+          # By default, queries listed here will override any specified in a config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
+          # queries: ./path/to/local/query, your-org/your-repo/queries@main
+
+      # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
+      # If this step fails, then you should remove it and run the build manually (see below)
+      - name: Autobuild
+        uses: github/codeql-action/autobuild@v1
+
+      # ℹ️ Command-line programs to run using the OS shell.
+      # 📚 https://git.io/JvXDl
+
+      # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
+      #    and modify them (or add more) to build your code if your project
+      #    uses a compiled language
+
+      #- run: |
+      #   make bootstrap
+      #   make release
+
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@v1

.github/workflows/greetings.yml

@@ -1,6 +1,6 @@
 name: Greetings
 
-on: [pull_request_target, issues]
+on: [ pull_request_target, issues ]
 
 jobs:
   greeting:
@@ -39,18 +39,18 @@ jobs:
             ```
 
             ## Environments
-            
+
             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 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
-            
+
             ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
-            
-            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/models/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
+
+            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
             

.github/workflows/rebase.yml

@@ -3,7 +3,7 @@ name: Automatic Rebase
 
 on:
   issue_comment:
-    types: [created]
+    types: [ created ]
 
 jobs:
   rebase:

.gitignore

@@ -19,26 +19,18 @@
 *.avi
 *.data
 *.json
-
 *.cfg
 !cfg/yolov3*.cfg
 
 storage.googleapis.com
 runs/*
 data/*
+!data/hyps/*
 !data/images/zidane.jpg
 !data/images/bus.jpg
-!data/coco.names
-!data/coco_paper.names
-!data/coco.data
-!data/coco_*.data
-!data/coco_*.txt
-!data/trainvalno5k.shapes
 !data/*.sh
 
-pycocotools/*
 results*.txt
-gcp_test*.sh
 
 # Datasets -------------------------------------------------------------------------------------------------------------
 coco/

Dockerfile

@@ -9,7 +9,8 @@ COPY requirements.txt .
 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
-RUN pip install --no-cache -U torch torchvision
+RUN pip install --no-cache -U torch torchvision numpy
+# RUN pip install --no-cache torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
 
 # Create working directory
 RUN mkdir -p /usr/src/app
@@ -22,17 +23,16 @@ COPY . /usr/src/app
 ENV HOME=/usr/src/app
 
 
-# ---------------------------------------------------  Extras Below  ---------------------------------------------------
+# Usage Examples -------------------------------------------------------------------------------------------------------
 
 # Build and Push
 # t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t
-# for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done
 
 # Pull and Run
 # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
 
 # Pull and Run with local directory access
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
 
 # Kill all
 # sudo docker kill $(sudo docker ps -q)

README.md

@@ -62,9 +62,7 @@ See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on tr
 
 
 <details open>
-<summary>
-Install
-</summary>
+<summary>Install</summary>
 
 Python >= 3.6.0 required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed:
 <!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
@@ -180,11 +178,10 @@ Get started in seconds with our verified environments and integrations, includin
 
 We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!  
 
-<div align="center">
-<a href="https://github.com/ultralytics/yolov5/discussions/3213">
-    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"/>
-</a>
-</div>
+<p align="center">
+  <a href="https://github.com/ultralytics/yolov5/discussions/3213">
+  <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
+</p>
 
 
 ## <div align="center">Why YOLOv5</div>

data/Argoverse_HD.yaml

@@ -0,0 +1,66 @@
+# 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
+#     /datasets/Argoverse
+#     /yolov5
+
+
+# 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/Argoverse  # dataset root dir
+train: Argoverse-1.1/images/train/  # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/  # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/  # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+nc: 8  # number of classes
+names: [ 'person',  'bicycle',  'car',  'motorcycle',  'bus',  'truck',  'traffic_light',  'stop_sign' ]  # class names
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+
+  from tqdm import tqdm
+  from utils.general import download, Path
+
+
+  def argoverse2yolo(set):
+      labels = {}
+      a = json.load(open(set, "rb"))
+      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"
+
+          cls = annot['category_id']  # instance class id
+          x_center, y_center, width, height = annot['bbox']
+          x_center = (x_center + width / 2) / 1920.0  # offset and scale
+          y_center = (y_center + height / 2) / 1200.0  # offset and scale
+          width /= 1920.0  # scale
+          height /= 1200.0  # scale
+
+          img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+          if not img_dir.exists():
+              img_dir.mkdir(parents=True, exist_ok=True)
+
+          k = str(img_dir / img_label_name)
+          if k not in labels:
+              labels[k] = []
+          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+      for k in labels:
+          with open(k, "w") as f:
+              f.writelines(labels[k])
+
+
+  # Download
+  dir = Path('../datasets/Argoverse')  # dataset root dir
+  urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
+  download(urls, dir=dir, delete=False)
+
+  # Convert
+  annotations_dir = 'Argoverse-HD/annotations/'
+  (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images')  # rename 'tracking' to 'images'
+  for d in "train.json", "val.json":
+      argoverse2yolo(dir / annotations_dir / d)  # convert VisDrone annotations to YOLO labels

data/GlobalWheat2020.yaml

@@ -1,43 +1,40 @@
 # 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
+#   /parent
 #     /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) --------------------------------------------------------------------------------------
+# 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/GlobalWheat2020  # dataset root dir
+train: # train images (relative to 'path') 3422 images
+  - images/arvalis_1
+  - images/arvalis_2
+  - images/arvalis_3
+  - images/ethz_1
+  - images/rres_1
+  - images/inrae_1
+  - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+  - images/ethz_1
+test: # test images (optional) 1276 images
+  - images/utokyo_1
+  - images/utokyo_2
+  - images/nau_1
+  - images/uq_1
+
+# Classes
+nc: 1  # number of classes
+names: [ 'wheat_head' ]  # class names
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from utils.general import download, Path
 
   # Download
-  dir = Path('../datasets/GlobalWheat2020')  # dataset directory
+  dir = Path(yaml['path'])  # dataset root dir
   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)