add support PPYOLO DAMOYOLO bmodel on 4093c

Signed-off-by: Onuralp SEZER <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <[email protected]> Clean and bump dvc callback: settings, stacked images (ultralytics#4343) Co-authored-by: Glenn Jocher <[email protected]> Improve tests coverage and speed (ultralytics#4340) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> `ultralytics 8.0.154` add `freeze` training argument (ultralytics#4329) Omit `ultralytics/utils/callbacks` from coverage (ultralytics#4345) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Fix PIL Image `exif_size()` function (ultralytics#4355) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Add `data/utils.py` to tests (ultralytics#4363)
pistonly · Aug 15, 2023 · 364f569 · 364f569
1 parent 28893d9
commit 364f569
Show file tree

Hide file tree

Showing 91 changed files with 2,589 additions and 1,232 deletions.
diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
@@ -165,9 +165,9 @@ jobs:
         run: |  # CoreML must be installed before export due to protobuf error from AutoInstall
           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 pytest "coremltools>=7.0.b1" --extra-index-url https://download.pytorch.org/whl/cpu
+              pip install -e . torch==1.8.0 torchvision==0.9.0 pytest-cov "coremltools>=7.0.b1" --extra-index-url https://download.pytorch.org/whl/cpu
           else
-              pip install -e . pytest "coremltools>=7.0.b1" --extra-index-url https://download.pytorch.org/whl/cpu
+              pip install -e . pytest-cov "coremltools>=7.0.b1" --extra-index-url https://download.pytorch.org/whl/cpu
           fi
       - name: Check environment
         run: |
@@ -182,7 +182,12 @@ jobs:
           pip list
       - name: Pytest tests
         shell: bash  # for Windows compatibility
-        run: pytest tests
+        run: pytest --cov=ultralytics/ --cov-report xml tests/
+      - name: Upload coverage reports to Codecov
+        if: github.repository == 'ultralytics/ultralytics' && matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
+        uses: codecov/codecov-action@v3
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
   Summary:
     runs-on: ubuntu-latest

diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml
@@ -28,7 +28,7 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
+          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
 
       - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
         if: github.event_name == 'workflow_dispatch'
@@ -37,4 +37,4 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
+          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
diff --git a/.gitignore b/.gitignore
@@ -158,3 +158,4 @@ weights/
 
 # Autogenerated files for tests
 /ultralytics/assets/
+*.~*~
diff --git a/README.md b/README.md
@@ -9,6 +9,7 @@
 
 <div>
     <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
     <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
     <br>

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -9,6 +9,7 @@
 
 <div>
     <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
     <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
     <br>

diff --git a/docs/datasets/index.md b/docs/datasets/index.md
@@ -52,6 +52,12 @@ Image classification is a computer vision task that involves categorizing an ima
 - [Imagewoof](classify/imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
 - [MNIST](classify/mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
 
+## [Oriented Bounding Boxes (OBB)](obb/index.md)
+
+Oriented Bounding Boxes (OBB) is a method in computer vision for detecting angled objects in images using rotated bounding boxes, often applied to aerial and satellite imagery.
+
+- [DOTAv2](obb/dota-v2.md): A popular OBB aerial imagery dataset with 1.7 million instances and 11,268 images.
+
 ## [Multi-Object Tracking](track/index.md)
 
 Multi-object tracking is a computer vision technique that involves detecting and tracking multiple objects over time in a video sequence.

diff --git a/docs/datasets/obb/index.md b/docs/datasets/obb/index.md
@@ -1,12 +1,12 @@
 ---
 comments: true
-description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with the Ultralytics YOLO model. Grasp the nuances of using and converting datasets to this format.
+description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with Ultralytics YOLO models. Grasp the nuances of using and converting datasets to this format.
 keywords: Ultralytics, YOLO, oriented bounding boxes, OBB, dataset formats, label formats, DOTA v2, data conversion
 ---
 
-# Oriented Bounding Box Datasets Overview
+# Oriented Bounding Box (OBB) Datasets Overview
 
-Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide elucidates the various OBB dataset formats compatible with the Ultralytics YOLO model, offering insights into their structure, application, and methods for format conversions.
+Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
 
 ## Supported OBB Dataset Formats
 

diff --git a/docs/help/CI.md b/docs/help/CI.md
@@ -8,14 +8,18 @@ keywords: continuous integration, software development, CI tests, Ultralytics re
 
 Continuous Integration (CI) is an essential aspect of software development which involves integrating changes and testing them automatically. CI allows us to maintain high-quality code by catching issues early and often in the development process. At Ultralytics, we use various CI tests to ensure the quality and integrity of our codebase.
 
-Here's a brief description of our CI tests:
+## CI Actions
+
+Here's a brief description of our CI actions:
 
 - **CI:** This is our primary CI test that involves running unit tests, linting checks, and sometimes more comprehensive tests depending on the repository.
 - **Docker Deployment:** This test checks the deployment of the project using Docker to ensure the Dockerfile and related scripts are working correctly.
 - **Broken Links:** This test scans the codebase for any broken or dead links in our markdown or HTML files.
 - **CodeQL:** CodeQL is a tool from GitHub that performs semantic analysis on our code, helping to find potential security vulnerabilities and maintain high-quality code.
 - **PyPi Publishing:** This test checks if the project can be packaged and published to PyPi without any errors.
 
+### CI Results
+
 Below is the table showing the status of these CI tests for our main repositories:
 
 | Repository                                                | CI                                                                                                                                                                        | Docker Deployment                                                                                                                                                                        | Broken Links                                                                                                                                                                      | CodeQL                                                                                                                                                                          | PyPi and Docs Publishing                                                                                                                                                                                      |
@@ -32,4 +36,27 @@ If you notice a test failing, it would be a great help if you could report it th
 
 Remember, a successful CI test does not mean that everything is perfect. It is always recommended to manually review the code before deployment or merging changes.
 
-Happy coding!
+## Code Coverage
+
+Code coverage is a metric that represents the percentage of your codebase that is executed when your tests run. It provides insight into how well your tests exercise your code and can be crucial in identifying untested parts of your application. A high code coverage percentage is often associated with a lower likelihood of bugs. However, it's essential to understand that code coverage doesn't guarantee the absence of defects. It merely indicates which parts of the code have been executed by the tests.
+
+### Integration with [codecov.io](https://codecov.io/)
+
+At Ultralytics, we have integrated our repositories with [codecov.io](https://codecov.io/), a popular online platform for measuring and visualizing code coverage. Codecov provides detailed insights, coverage comparisons between commits, and visual overlays directly on your code, indicating which lines were covered.
+
+By integrating with Codecov, we aim to maintain and improve the quality of our code by focusing on areas that might be prone to errors or need further testing.
+
+### Coverage Results
+
+To quickly get a glimpse of the code coverage status of the `ultralytics` python package, we have included a badge and and sunburst visual of the `ultralytics` coverage results. These images show the percentage of code covered by our tests, offering an at-a-glance metric of our testing efforts. For full details please see https://codecov.io/github/ultralytics/ultralytics.
+
+| Repository                                                | Code Coverage                                                        |
+|-----------------------------------------------------------|----------------------------------------------------------------------|
+| [ultralytics](https://github.com/ultralytics/ultralytics) | [![codecov](https://codecov.io/gh/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY)](https://codecov.io/gh/ultralytics/ultralytics) |
+
+In the sunburst graphic below, the inner-most circle is the entire project, moving away from the center are folders then, finally, a single file. The size and color of each slice is representing the number of statements and the coverage, respectively.
+
+<a href="https://codecov.io/github/ultralytics/ultralytics">
+    <img src="https://codecov.io/gh/ultralytics/ultralytics/branch/main/graphs/sunburst.svg?token=HHW7IIVFVY" alt="Ultralytics Codecov Image">
+</a>
+
diff --git a/docs/index.md b/docs/index.md
@@ -10,6 +10,7 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine lea
     <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
   </p>
   <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+  <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
   <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
   <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
   <br>

diff --git a/docs/models/fast-sam.md b/docs/models/fast-sam.md
@@ -40,58 +40,71 @@ The FastSAM models are easy to integrate into your Python applications. Ultralyt
 
 To perform object detection on an image, use the `predict` method as shown below:
 
-```python
-from ultralytics import FastSAM
-from ultralytics.models.fastsam import FastSAMPrompt
-
-# Define image path and inference device
-IMAGE_PATH = 'ultralytics/assets/bus.jpg'
-DEVICE = 'cpu'
-
-# Create a FastSAM model
-model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
-
-# Run inference on an image
-everything_results = model(IMAGE_PATH,
-                           device=DEVICE,
-                           retina_masks=True,
-                           imgsz=1024,
-                           conf=0.4,
-                           iou=0.9)
-
-prompt_process = FastSAMPrompt(IMAGE_PATH, everything_results, device=DEVICE)
-
-# Everything prompt
-ann = prompt_process.everything_prompt()
-
-# Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
-ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
-
-# Text prompt
-ann = prompt_process.text_prompt(text='a photo of a dog')
-
-# Point prompt
-# points default [[0,0]] [[x1,y1],[x2,y2]]
-# point_label default [0] [1,0] 0:background, 1:foreground
-ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
-prompt_process.plot(annotations=ann, output='./')
-```
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import FastSAM
+        from ultralytics.models.fastsam import FastSAMPrompt
+
+        # Define an inference source
+        source = 'path/to/bus.jpg'
+
+        # Create a FastSAM model
+        model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
+
+        # Run inference on an image
+        everything_results = model(source, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
+      
+        # Prepare a Prompt Process object
+        prompt_process = FastSAMPrompt(source, everything_results, device='cpu')
+
+        # Everything prompt
+        ann = prompt_process.everything_prompt()
+
+        # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
+        ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
+
+        # Text prompt
+        ann = prompt_process.text_prompt(text='a photo of a dog')
+
+        # Point prompt
+        # points default [[0,0]] [[x1,y1],[x2,y2]]
+        # point_label default [0] [1,0] 0:background, 1:foreground
+        ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
+        prompt_process.plot(annotations=ann, output='./')
+        ```
+      
+    === "CLI"
+        ```bash
+        # Load a FastSAM model and segment everything with it
+        yolo segment predict model=FastSAM-s.pt source=path/to/bus.jpg imgsz=640
+        ```
 
 This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
 
 #### Val Usage
 
 Validation of the model on a dataset can be done as follows:
 
-```python
-from ultralytics import FastSAM
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import FastSAM
+
+        # Create a FastSAM model
+        model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
 
-# Create a FastSAM model
-model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
+        # Validate the model
+        results = model.val(data='coco8-seg.yaml')
+        ```
 
-# Validate the model
-results = model.val(data='coco8-seg.yaml')
-```
+    === "CLI"
+        ```bash
+        # Load a FastSAM model and validate it on the COCO8 example dataset at image size 640
+        yolo segment val model=FastSAM-s.pt data=coco8.yaml imgsz=640
+        ```
 
 Please note that FastSAM only supports detection and segmentation of a single class of object. This means it will recognize and segment all objects as the same class. Therefore, when preparing the dataset, you need to convert all object category IDs to 0.
 

diff --git a/docs/models/index.md b/docs/models/index.md
@@ -26,7 +26,7 @@ You can use many of these models directly in the Command Line Interface (CLI) or
 
 ## Usage
 
-    This example provides simple inference code for YOLO, SAM and RTDETR models. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using models with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+This example provides simple inference code for YOLO, SAM and RTDETR models. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using models with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
 !!! example ""
 
@@ -44,7 +44,7 @@ You can use many of these models directly in the Command Line Interface (CLI) or
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the YOLOv8n model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')

diff --git a/docs/models/mobile-sam.md b/docs/models/mobile-sam.md
@@ -61,27 +61,33 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
 
 ### Point Prompt
 
-```python
-from ultralytics import SAM
-
-# Load the model
-model = SAM('mobile_sam.pt')
-
-# Predict a segment based on a point prompt
-model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
-```
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import SAM
+        
+        # Load the model
+        model = SAM('mobile_sam.pt')
+        
+        # Predict a segment based on a point prompt
+        model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
+        ```
 
 ### Box Prompt
 
-```python
-from ultralytics import SAM
-
-# Load the model
-model = SAM('mobile_sam.pt')
-
-# Predict a segment based on a box prompt
-model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
-```
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import SAM
+        
+        # Load the model
+        model = SAM('mobile_sam.pt')
+        
+        # Predict a segment based on a box prompt
+        model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
+        ```
 
 We have implemented `MobileSAM` and `SAM` using the same API. For more usage information, please see the [SAM page](./sam.md).
 

diff --git a/docs/models/rtdetr.md b/docs/models/rtdetr.md
@@ -46,7 +46,7 @@ You can use RT-DETR for object detection tasks using the `ultralytics` pip packa
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the RT-DETR-l model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')
Original file line number	Diff line number	Diff line change
Expand Up		@@ -158,3 +158,4 @@ weights/

		# Autogenerated files for tests
		/ultralytics/assets/
		.~~