YOLOv3 general updates, improvements and fixes (#2011)

* YOLOv3 updates

* Add missing files

* Reformat

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Reformat

* Reformat

* Reformat

* Reformat

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

data/scripts/download_weights.sh

@@ -1,18 +1,22 @@
 #!/bin/bash
 # YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Download latest models from https://github.com/ultralytics/yolov3/releases
-# Example usage: bash path/to/download_weights.sh
+# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Example usage: bash data/scripts/download_weights.sh
 # parent
-# └── yolov3
-#     ├── yolov3.pt  ← downloads here
-#     ├── yolov3-spp.pt
+# └── yolov5
+#     ├── yolov5s.pt  ← downloads here
+#     ├── yolov5m.pt
 #     └── ...
 
 python - <<EOF
 from utils.downloads import attempt_download
 
-models = ['yolov3', 'yolov3-spp', 'yolov3-tiny']
-for x in models:
-    attempt_download(f'{x}.pt')
+p5 = list('nsmlx')  # P5 models
+p6 = [f'{x}6' for x in p5]  # P6 models
+cls = [f'{x}-cls' for x in p5]  # classification models
+seg = [f'{x}-seg' for x in p5]  # classification models
+
+for x in p5 + p6 + cls + seg:
+    attempt_download(f'weights/yolov5{x}.pt')
 
 EOF

data/scripts/get_coco.sh

@@ -3,25 +3,54 @@
 # Download COCO 2017 dataset http://cocodataset.org
 # Example usage: bash data/scripts/get_coco.sh
 # parent
-# ├── yolov3
+# ├── yolov5
 # └── datasets
 #     └── coco  ← downloads here
 
+# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    --test) test=true ;;
+    --segments) segments=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+  test=false
+  segments=false
+fi
+
 # Download/unzip labels
 d='../datasets' # unzip directory
 url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
+if [ "$segments" == "true" ]; then
+  f='coco2017labels-segments.zip' # 168 MB
+else
+  f='coco2017labels.zip' # 46 MB
+fi
 echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
 
 # Download/unzip images
 d='../datasets/coco/images' # unzip directory
 url=http://images.cocodataset.org/zips/
-f1='train2017.zip' # 19G, 118k images
-f2='val2017.zip'   # 1G, 5k images
-f3='test2017.zip'  # 7G, 41k images (optional)
-for f in $f1 $f2; do
+if [ "$train" == "true" ]; then
+  f='train2017.zip' # 19G, 118k images
   echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
-done
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$val" == "true" ]; then
+  f='val2017.zip' # 1G, 5k images
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$test" == "true" ]; then
+  f='test2017.zip' # 7G, 41k images (optional)
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
 wait # finish background tasks

data/scripts/get_coco128.sh

@@ -3,7 +3,7 @@
 # Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
 # Example usage: bash data/scripts/get_coco128.sh
 # parent
-# ├── yolov3
+# ├── yolov5
 # └── datasets
 #     └── coco128  ← downloads here
 
@@ -12,6 +12,6 @@ d='../datasets' # unzip directory
 url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
 f='coco128.zip' # or 'coco128-segments.zip', 68 MB
 echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
 
 wait # finish background tasks

data/scripts/get_imagenet.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
+# Download ILSVRC2012 ImageNet dataset https://image-net.org
+# Example usage: bash data/scripts/get_imagenet.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+fi
+
+# Make dir
+d='../datasets/imagenet' # unzip directory
+mkdir -p $d && cd $d
+
+# Download/unzip train
+if [ "$train" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
+  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
+  tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
+  find . -name "*.tar" | while read NAME; do
+    mkdir -p "${NAME%.tar}"
+    tar -xf "${NAME}" -C "${NAME%.tar}"
+    rm -f "${NAME}"
+  done
+  cd ..
+fi
+
+# Download/unzip val
+if [ "$val" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
+  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
+  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
+fi
+
+# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
+# rm train/n04266014/n04266014_10835.JPEG
+
+# TFRecords (optional)
+# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt