pull updates from original repo (release v5) + reimplement UI

.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

@@ -66,14 +66,15 @@ 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

@@ -11,7 +11,7 @@ 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
@@ -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

@@ -74,7 +74,7 @@ YOLOv5 is one of the best available detectors. And we are proud to announce its
 
 # For Developers
 - you can use sources of [Serve YOLOv5 app](https://github.com/supervisely-ecosystem/yolov5/tree/master/supervisely/serve) as example of how to prepare weights, initialize model and apply it to a folder with images (or to images URLs)
-- This apps collection is based on the original YOLOv5 [release v4.0](https://github.com/ultralytics/yolov5/releases/tag/v4.0). Once a next official release is available, all apps will be synchronized with it and also released with the new versions. Before running any app you can choose what version to use. Also Supervisely Team will pull updates from original master branch from time to time.
+- This apps collection is based on the original YOLOv5 [release v5.0](https://github.com/ultralytics/yolov5/releases/tag/v5.0). Once a next official release is available, all apps will be synchronized with it and also released with the new versions. Before running any app you can choose what version to use. Also Supervisely Team will pull updates from original master branch from time to time.
 
 # Contact & Questions & Suggestions
 

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/scripts/get_argoverse_hd.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
+# Download command: bash data/scripts/get_argoverse_hd.sh
+# Train command: python train.py --data argoverse_hd.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /argoverse
+#     /yolov5
+
+# Download/unzip images
+d='../argoverse/' # unzip directory
+mkdir $d
+url=https://argoverse-hd.s3.us-east-2.amazonaws.com/
+f=Argoverse-HD-Full.zip
+curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background
+wait                                              # finish background tasks
+
+cd ../argoverse/Argoverse-1.1/
+ln -s tracking images
+
+cd ../Argoverse-HD/annotations/
+
+python3 - "$@" <<END
+import json
+from pathlib import Path
+
+annotation_files = ["train.json", "val.json"]
+print("Converting annotations to YOLOv5 format...")
+
+for val in annotation_files:
+    a = json.load(open(val, "rb"))
+
+    label_dict = {}
+    for annot in a['annotations']:
+        img_id = annot['image_id']
+        img_name = a['images'][img_id]['name']
+        img_label_name = img_name[:-3] + "txt"
+
+        obj_class = annot['category_id']
+        x_center, y_center, width, height = annot['bbox']
+        x_center = (x_center + width / 2) / 1920.  # offset and scale
+        y_center = (y_center + height / 2) / 1200.  # offset and scale
+        width /= 1920.  # scale
+        height /= 1200.  # scale
+
+        img_dir = "./labels/" + a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+
+        Path(img_dir).mkdir(parents=True, exist_ok=True)
+
+        if img_dir + "/" + img_label_name not in label_dict:
+            label_dict[img_dir + "/" + img_label_name] = []
+
+        label_dict[img_dir + "/" + img_label_name].append(f"{obj_class} {x_center} {y_center} {width} {height}\n")
+
+    for filename in label_dict:
+        with open(filename, "w") as file:
+            for string in label_dict[filename]:
+                file.write(string)
+
+END
+
+mv ./labels ../../Argoverse-1.1/

data/scripts/get_coco.sh

@@ -2,16 +2,17 @@
 # COCO 2017 dataset http://cocodataset.org
 # Download command: bash data/scripts/get_coco.sh
 # 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
 
 # Download/unzip labels
 d='../' # unzip directory
 url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels.zip'                                                                 # 68 MB
-echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
+echo 'Downloading' $url$f ' ...'
+curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
 
 # Download/unzip images
 d='../coco/images' # unzip directory
@@ -20,5 +21,7 @@ f1='train2017.zip' # 19G, 118k images
 f2='val2017.zip'   # 1G, 5k images
 f3='test2017.zip'  # 7G, 41k images (optional)
 for f in $f1 $f2; do
-  echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
 done
+wait # finish background tasks