update to ultralytics 16.09.22

.github/workflows/ci.yml

@@ -19,62 +19,75 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v2
+
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+
       - name: Restore Ubuntu cache
         uses: actions/cache@v1
         if: matrix.operating-system == 'ubuntu-latest'
         with:
           path: ~/.cache/pip
           key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
           restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
       - name: Restore MacOS cache
         uses: actions/cache@v1
         if: matrix.operating-system == 'macos-latest'
         with:
           path: ~/Library/Caches/pip
           key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
           restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
       - name: Restore Windows cache
         uses: actions/cache@v1
         if: matrix.operating-system == 'windows-latest'
         with:
           path: ~\AppData\Local\pip\Cache
           key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
           restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
       - name: Update pip
         run: python -m pip install --upgrade pip
-      - name: Install  numpy and matplotlib for Python 3.6
-        if: matrix.python-version == 3.6
-        run: >
-          pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
+
       - name: Install dependencies
         run: >
           pip install -r requirements.txt
+
       - name: Test with unittest
         run: |
           python -m unittest
+
       - name: Test scripts
         run: |
           pip install -e .
-          di=cpu # inference devices  # define device
           # train
-          python yolov5/train.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device $di
-          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device $di
-          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device $di --evolve 2
+          python yolov5/train.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu
+          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --freeze 10
+          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --evolve 2
           # detect
-          python yolov5/detect.py --weights yolov5/weights/yolov5n.pt --device $di
-          yolov5 detect --weights yolov5/weights/yolov5n.pt --device $di
-          python yolov5/detect.py --weights runs/train/exp/weights/last.pt --device $di
-          yolov5 detect --weights runs/train/exp/weights/last.pt --device $di
+          python yolov5/detect.py --weights yolov5/weights/yolov5n.pt --device cpu
+          yolov5 detect --weights yolov5/weights/yolov5n.pt --device cpu
+          python yolov5/detect.py --weights runs/train/exp/weights/last.pt --device cpu
+          yolov5 detect --weights runs/train/exp/weights/last.pt --device cpu
           # val
-          python yolov5/val.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device $di
-          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device $di
-          python yolov5/val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
-          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
+          python yolov5/val.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu
+          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu
+          python yolov5/val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu
+          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu
           # export
           pip install onnx onnx-simplifier tensorflowjs
-          python yolov5/export.py --weights yolov5/weights/yolov5n.pt --device $di --include 'torchscript,onnx,tflite'
-          yolov5 export --weights yolov5/weights/yolov5n.pt --device $di --simplify --include torchscript,onnx,saved_model,pb,tfjs
+          python yolov5/export.py --weights yolov5/weights/yolov5n.pt --device cpu --include torchscript,onnx,tflite
+          yolov5 export --weights yolov5/weights/yolov5n.pt --device cpu --simplify --include torchscript,onnx,saved_model,pb,tfjs
+          # benckmarks
+          yolov5 benchmarks --weights yolov5s.pt --img 128 --pt-only --device cpu
+          # classify
+          yolov5 classify train --img 32 --data mnist2560 --weights yolov5s-cls.pt --epochs 1 --device cpu
+          yolov5 classify val --img 32 --data datasets/mnist2560 --weights yolov5s-cls.pt --device cpu
+          yolov5 classify predict --img 32 --weights yolov5s-cls.pt --device cpu  
+          # segment
+          yolov5 segment train --img 128 --weights yolov5s-seg.pt --epochs 1 --device cpu
+          # yolov5 segment val --img 128 --weights yolov5s-seg.pt --device cpu
+          yolov5 segment predict --img 128 --weights yolov5s-seg.pt --device cpu

.github/workflows/package_testing.yml

@@ -17,62 +17,76 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v2
+
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+
       - name: Restore Ubuntu cache
         uses: actions/cache@v1
         if: matrix.operating-system == 'ubuntu-latest'
         with:
           path: ~/.cache/pip
           key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
           restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
       - name: Restore MacOS cache
         uses: actions/cache@v1
         if: matrix.operating-system == 'macos-latest'
         with:
           path: ~/Library/Caches/pip
           key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
           restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
       - name: Restore Windows cache
         uses: actions/cache@v1
         if: matrix.operating-system == 'windows-latest'
         with:
           path: ~\AppData\Local\pip\Cache
           key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
           restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
       - name: Update pip
         run: python -m pip install --upgrade pip
-      - name: Install numpy and matplotlib for Python 3.6
-        if: matrix.python-version == 3.6
-        run: >
-          pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
+
+
       - name: Install latest YOLOv5 package
         run: >
           pip install --upgrade --force-reinstall yolov5
+
       - name: Test with unittest
         run: |
           python -m unittest
+
       - name: Test scripts
         run: |
           pip install -e .
-          di=cpu # inference devices  # define device
           # train
-          python yolov5/train.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device $di
-          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device $di
-          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device $di --evolve 2
+          python yolov5/train.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu
+          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --freeze 10
+          yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --evolve 2
           # detect
-          python yolov5/detect.py --weights yolov5/weights/yolov5n.pt --device $di
-          yolov5 detect --weights yolov5/weights/yolov5n.pt --device $di
-          python yolov5/detect.py --weights runs/train/exp/weights/last.pt --device $di
-          yolov5 detect --weights runs/train/exp/weights/last.pt --device $di
+          python yolov5/detect.py --weights yolov5/weights/yolov5n.pt --device cpu
+          yolov5 detect --weights yolov5/weights/yolov5n.pt --device cpu
+          python yolov5/detect.py --weights runs/train/exp/weights/last.pt --device cpu
+          yolov5 detect --weights runs/train/exp/weights/last.pt --device cpu
           # val
-          python yolov5/val.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device $di
-          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device $di
-          python yolov5/val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
-          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
+          python yolov5/val.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu
+          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu
+          python yolov5/val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu
+          yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu
           # export
           pip install onnx onnx-simplifier tensorflowjs
-          python yolov5/export.py --weights yolov5/weights/yolov5n.pt --device $di --include 'torchscript,onnx,tflite'
-          yolov5 export --weights yolov5/weights/yolov5n.pt --device $di --simplify --include torchscript,onnx,saved_model,pb,tfjs
+          python yolov5/export.py --weights yolov5/weights/yolov5n.pt --device cpu --include torchscript,onnx,tflite
+          yolov5 export --weights yolov5/weights/yolov5n.pt --device cpu --simplify --include torchscript,onnx,saved_model,pb,tfjs
+          # benckmarks
+          yolov5 benchmarks --weights yolov5s.pt --img 128 --pt-only --device cpu
+          # classify
+          yolov5 classify train --img 224 --weights yolov5s-cls.pt --epochs 1 --device cpu
+          yolov5 classify val --img 224 --weights yolov5s-cls.pt --device cpu
+          yolov5 classify predict --img 224 --weights yolov5s-cls.pt --device cpu  
+          # segment
+          yolov5 segment train --img 128 --weights yolov5s-seg.pt --epochs 1 --device cpu
+          # yolov5 segment val --img 128 --weights yolov5s-seg.pt --device cpu
+          yolov5 segment predict --img 128 --weights yolov5s-seg.pt --device cpu

.gitignore

@@ -44,6 +44,11 @@ pycocotools/*
 results*.txt
 gcp_test*.sh
 .neptune
+*.bin
+*.pb
+*saved_model
+*.zip
+datasets/
 
 # Datasets -------------------------------------------------------------------------------------------------------------
 coco/

README.md

@@ -25,7 +25,7 @@ You can finally install <a href="https://github.com/ultralytics/yolov5">YOLOv5 o
 </div>
 
 <br>
-This yolov5 package contains everything from ultralytics/yolov5 <a href="https://github.com/ultralytics/yolov5/tree/84e7748564f83ba04601770f17a38cc55e6be661">at this commit</a> plus:
+This yolov5 package contains everything from ultralytics/yolov5 <a href="https://github.com/ultralytics/yolov5/tree/03f2ca8eff8918b98169256d055353a1f15b8e32">at this commit</a> plus:
 <br>
 1. Easy installation via pip: `pip install yolov5`
 <br>

data_coco.yml

@@ -0,0 +1,4 @@
+train_json_path: "/home/fatihakyon/data/witwiser/banned_objects_with_negative_samples/train.json"
+train_image_dir: "/home/fatihakyon/data/witwiser/banned_objects_with_negative_samples"
+val_json_path: "/home/fatihakyon/data/witwiser/banned_objects_with_negative_samples/val.json"
+val_image_dir: "/home/fatihakyon/data/witwiser/banned_objects_with_negative_samples"

data_wandb.yaml

@@ -0,0 +1,13 @@
+names:
+- earphone
+- cable
+- cellphone
+- Headphone
+- calculator
+nc: 5
+train: wandb-artifact://YOLOv5/train
+train_image_dir: /home/fatihakyon/data/witwiser/banned_objects_with_negative_samples
+train_json_path: /home/fatihakyon/data/witwiser/banned_objects_with_negative_samples/train.json
+val: wandb-artifact://YOLOv5/val
+val_image_dir: /home/fatihakyon/data/witwiser/banned_objects_with_negative_samples
+val_json_path: /home/fatihakyon/data/witwiser/banned_objects_with_negative_samples/val.json

data_yolo.yml

@@ -0,0 +1,5 @@
+names: ['earphone', 'cable', 'cellphone', 'Headphone', 'calculator']
+nc: 5
+train: /home/fatihakyon/dev/fcakyon/yolov5-pip/runs/train/yolo_upload_dataset_test/data/train
+val: /home/fatihakyon/dev/fcakyon/yolov5-pip/runs/train/yolo_upload_dataset_test/data/val
+yolo_s3_data_dir: s3://obss-ml/computer-vision/model/yolov5/yolo_upload_dataset_test2/data/

requirements.txt

@@ -12,11 +12,12 @@ scipy>=1.4.1
 torch>=1.7.0
 torchvision>=0.8.1
 tqdm>=4.64.0
-protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
+# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
 
 # Logging -------------------------------------
 tensorboard>=2.4.1
 # wandb
+# clearml
 
 # Plotting ------------------------------------
 pandas>=1.1.4
@@ -46,4 +47,4 @@ fire
 # AWS
 boto3>=1.19.1
 # coco to yolov5 conversion
-sahi>=0.9.1
+sahi>=0.10.5

yolov5/__init__.py

@@ -1,4 +1,4 @@
 from yolov5.helpers import YOLOv5
 from yolov5.helpers import load_model as load
 
-__version__ = "6.1.9"
+__version__ = "6.2.0"