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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <[email protected]>
Co-authored-by: Bogdan Gheorghe <[email protected]>
Co-authored-by: Ayush Chaurasia <[email protected]>
Co-authored-by: Jaap van de Loosdrecht <[email protected]>
Co-authored-by: Noobtoss <[email protected]>
Co-authored-by: nerdyespresso <[email protected]>
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@Laughing-q @bogdan-galileo @AyushExel @VdLMV @Noobtoss @nerdyespresso
8 people authored Feb 17, 2023
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64 changes: 60 additions & 4 deletions .github/workflows/ci.yaml
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Expand Up @@ -12,6 +12,64 @@ on:
- cron: '0 0 * * *' # runs at 00:00 UTC every day

jobs:
Benchmarks:
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest]
python-version: ['3.10'] # requires python<=3.9
model: [yolov8n]
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
#- name: Cache pip
# uses: actions/cache@v3
# with:
# path: ~/.cache/pip
# key: ${{ runner.os }}-Benchmarks-${{ hashFiles('requirements.txt') }}
# restore-keys: ${{ runner.os }}-Benchmarks-
- name: Install requirements
run: |
python -m pip install --upgrade pip wheel
pip install -e '.[export]' --extra-index-url https://download.pytorch.org/whl/cpu
- name: Check environment
run: |
echo "RUNNER_OS is ${{ runner.os }}"
echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
echo "GITHUB_ACTOR is ${{ github.actor }}"
echo "GITHUB_REPOSITORY is ${{ github.repository }}"
echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}"
python --version
pip --version
pip list
- name: TF Lite export
run: |
yolo export model=${{ matrix.model }}.pt format=tflite
yolo task=detect mode=predict model=yolov8n_saved_model/yolov8n_float16.tflite imgsz=640
- name: TF *.pb export
run: |
yolo export model=${{ matrix.model }}.pt format=pb
yolo task=detect mode=predict model=yolov8n.pb imgsz=640
- name: TF Lite Edge TPU export
run: |
yolo export model=${{ matrix.model }}.pt format=edgetpu
- name: TF.js export
run: |
yolo export model=${{ matrix.model }}.pt format=tfjs
- name: Benchmark DetectionModel
run: |
# yolo benchmark model=${{ matrix.model }}.pt imgsz=320 min_metric=0.29
- name: Benchmark SegmentationModel
run: |
# yolo benchmark model=${{ matrix.model }}-seg.pt imgsz=320 min_metric=0.29
- name: Benchmark ClassificationModel
run: |
# yolo benchmark model=${{ matrix.model }}-cls.pt imgsz=224 min_metric=0.29
Tests:
timeout-minutes: 60
runs-on: ${{ matrix.os }}
Expand Down Expand Up @@ -49,15 +107,13 @@ jobs:
run: |
python -m pip install --upgrade pip wheel
if [ "${{ matrix.torch }}" == "1.8.0" ]; then
pip install -e . torch==1.8.0 torchvision==0.9.0 onnx openvino-dev>=2022.3 pytest --extra-index-url https://download.pytorch.org/whl/cpu
pip install -e '.[export]' torch==1.8.0 torchvision==0.9.0 pytest --extra-index-url https://download.pytorch.org/whl/cpu
else
pip install -e . onnx openvino-dev>=2022.3 pytest --extra-index-url https://download.pytorch.org/whl/cpu
pip install -e '.[export]' pytest --extra-index-url https://download.pytorch.org/whl/cpu
fi
# pip install ultralytics (production)
shell: bash # for Windows compatibility
- name: Check environment
run: |
# python -c "import utils; utils.notebook_init()"
echo "RUNNER_OS is ${{ runner.os }}"
echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
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10 changes: 5 additions & 5 deletions .pre-commit-config.yaml
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Expand Up @@ -31,11 +31,11 @@ repos:
name: Upgrade code
args: [--py37-plus]

# - repo: https://github.com/PyCQA/isort
# rev: 5.11.4
# hooks:
# - id: isort
# name: Sort imports
- repo: https://github.com/PyCQA/isort
rev: 5.12.0
hooks:
- id: isort
name: Sort imports

- repo: https://github.com/google/yapf
rev: v0.32.0
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34 changes: 20 additions & 14 deletions README.md
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Expand Up @@ -108,6 +108,12 @@ success = model.export(format="onnx") # export the model to ONNX format
Ultralytics [release](https://github.com/ultralytics/assets/releases). See
YOLOv8 [Python Docs](https://docs.ultralytics.com/python) for more examples.

#### Model Architectures

⭐ **NEW** YOLOv5u anchor free models are now available.

All supported model architectures can be found in the [Models](./ultralytics/models/) section.

#### Known Issues / TODOs

We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
Expand Down Expand Up @@ -152,13 +158,13 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage ex

See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for usage examples with these models.

| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo val segment data=coco.yaml device=0`
Expand All @@ -172,13 +178,13 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for us

See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for usage examples with these models.

| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| ---------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |

- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
<br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
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