diff --git a/.github/workflows/rust-ci.yml b/.github/workflows/rust-ci.yml
index 823b2d9..75cc98c 100644
--- a/.github/workflows/rust-ci.yml
+++ b/.github/workflows/rust-ci.yml
@@ -1,17 +1,17 @@
-name: ci
+name: Rust-CI
 
 on:
   push:
-    branches: [ main, dev ]
+    branches: [ "main", "dev", "x", "xy" , "xyz" ]
   pull_request:
-    branches: [ main ]
+    branches: [ "main" ]
 
 env:
   CARGO_TERM_COLOR: always
 
-jobs:
 
-  build-linux:
+jobs:
+  build-on-linux:
     name: build / linux / ffmpeg ${{ matrix.ffmpeg_version }}
     runs-on: ubuntu-latest
     container: jrottenberg/ffmpeg:${{ matrix.ffmpeg_version }}-ubuntu
@@ -35,16 +35,10 @@ jobs:
         with:
           toolchain: stable
     
-      - name: Set up Rust
-        uses: actions-rs/toolchain@v1
-        with:
-          toolchain: stable
-          override: true
-
       - name: Build
         run: cargo build
 
-  build-macos:
+  build-on-macos:
     name: build / macos / latest ffmpeg
     runs-on: macos-latest
 
@@ -64,7 +58,8 @@ jobs:
       - name: Build
         run: cargo build
 
-  build-windows:
+
+  build-on-windows:
     name: build / windows / latest ffmpeg
     runs-on: windows-latest
 
@@ -94,9 +89,16 @@ jobs:
       - name: Build
         run: cargo build
 
-  test:
+
+  test-on-linux:
+    name: test / linux / ffmpeg ${{ matrix.ffmpeg_version }}
     runs-on: ubuntu-latest
-    container: jrottenberg/ffmpeg:6-ubuntu
+    container: jrottenberg/ffmpeg:${{ matrix.ffmpeg_version }}-ubuntu
+
+    strategy:
+      matrix:
+        ffmpeg_version: ["4.3", "4.4", "5.0", "5.1", "6.0", "6.1", "7.0"]
+      fail-fast: false
 
     steps:
       - name: Checkout
@@ -107,14 +109,97 @@ jobs:
           apt update
           apt install -y --no-install-recommends clang curl pkg-config
 
+      - name: Setup Rust
+        uses: dtolnay/rust-toolchain@v1
+        with:
+          toolchain: stable
+
+      - name: Run Tests with All Features
+        run: cargo test --all-features
+
+      - name: Run Tests in Release Mode
+        run: cargo test --release
+
+      - name: Run Tests with No Default Features
+        run: cargo test --no-default-features
+
+      - name: Run Tests on All Targets
+        run: cargo test --all-targets
+
+
+
+  test-on-macos:
+    name: test / macos / latest ffmpeg
+    runs-on: macos-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Install dependencies
+        run: |
+          brew install ffmpeg pkg-config
+
       - name: Setup Rust
         uses: dtolnay/rust-toolchain@v1
         with:
           toolchain: stable
     
-      - name: Test
+      - name: Run Tests with All Features
         run: cargo test --all-features
 
+      - name: Run Tests in Release Mode
+        run: cargo test --release
+
+      - name: Run Tests with No Default Features
+        run: cargo test --no-default-features
+
+      - name: Run Tests on All Targets
+        run: cargo test --all-targets
+
+
+
+  test-on-windows:
+    name: test / windows / latest ffmpeg
+    runs-on: windows-latest
+
+    env:
+      FFMPEG_DOWNLOAD_URL: https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-full-shared.7z
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Install dependencies
+        run: |
+          $VCINSTALLDIR = $(& "${env:ProgramFiles(x86)}\Microsoft Visual Studio\Installer\vswhere.exe" -latest -property installationPath)
+          Add-Content $env:GITHUB_ENV "LIBCLANG_PATH=${VCINSTALLDIR}\VC\Tools\LLVM\x64\bin`n"
+          Invoke-WebRequest "${env:FFMPEG_DOWNLOAD_URL}" -OutFile ffmpeg-release-full-shared.7z
+          7z x ffmpeg-release-full-shared.7z
+          mkdir ffmpeg
+          mv ffmpeg-*/* ffmpeg/
+          Add-Content $env:GITHUB_ENV "FFMPEG_DIR=${pwd}\ffmpeg`n"
+          Add-Content $env:GITHUB_PATH "${pwd}\ffmpeg\bin`n"
+
+      - name: Setup Rust
+        uses: dtolnay/rust-toolchain@v1
+        with:
+          toolchain: stable
+    
+      - name: Run Tests with All Features
+        run: cargo test --all-features
+
+      - name: Run Tests in Release Mode
+        run: cargo test --release
+
+      - name: Run Tests with No Default Features
+        run: cargo test --no-default-features
+
+      - name: Run Tests on All Targets
+        run: cargo test --all-targets
+
+
+
   lints:
     runs-on: ubuntu-latest
     container: jrottenberg/ffmpeg:6-ubuntu
@@ -138,4 +223,4 @@ jobs:
         run: cargo fmt --all -- --check
 
       - name: Clippy
-        run: cargo clippy --all --all-features -- -D warnings
+        run: cargo clippy --all --all-targets --all-features -- -D warnings
diff --git a/benches/yolo.rs b/benches/yolo.rs
index 5a9e806..82ecb3a 100644
--- a/benches/yolo.rs
+++ b/benches/yolo.rs
@@ -24,6 +24,7 @@ fn yolo_stage_bench(
         let t0 = std::time::Instant::now();
         let xs = model.preprocess(x).unwrap();
         t_pre += t0.elapsed();
+        let a = 1;
 
         let t = std::time::Instant::now();
         let xs = model.inference(xs).unwrap();
diff --git a/src/lib.rs b/src/lib.rs
index cc17c6e..fd63333 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -1,27 +1,28 @@
-//! **`usls`** is a Rust library integrated with **ONNXRuntime** that provides a collection of state-of-the-art models for **Computer Vision** and **Vision-Language** tasks, including:
-
+//! **usls** is a Rust library integrated with **ONNXRuntime** that provides a collection of state-of-the-art models for **Computer Vision** and **Vision-Language** tasks, including:
+//!
 //! - **YOLO Models**: [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv6](https://github.com/meituan/YOLOv6), [YOLOv7](https://github.com/WongKinYiu/yolov7), [YOLOv8](https://github.com/ultralytics/ultralytics), [YOLOv9](https://github.com/WongKinYiu/yolov9), [YOLOv10](https://github.com/THU-MIG/yolov10)
 //! - **SAM Models**: [SAM](https://github.com/facebookresearch/segment-anything), [SAM2](https://github.com/facebookresearch/segment-anything-2), [MobileSAM](https://github.com/ChaoningZhang/MobileSAM), [EdgeSAM](https://github.com/chongzhou96/EdgeSAM), [SAM-HQ](https://github.com/SysCV/sam-hq), [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM)
 //! - **Vision Models**: [RTDETR](https://arxiv.org/abs/2304.08069), [RTMO](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo), [DB](https://arxiv.org/abs/1911.08947), [SVTR](https://arxiv.org/abs/2205.00159), [Depth-Anything-v1-v2](https://github.com/LiheYoung/Depth-Anything), [DINOv2](https://github.com/facebookresearch/dinov2), [MODNet](https://github.com/ZHKKKe/MODNet), [Sapiens](https://arxiv.org/abs/2408.12569)
 //! - **Vision-Language Models**: [CLIP](https://github.com/openai/CLIP), [BLIP](https://arxiv.org/abs/2201.12086), [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO), [YOLO-World](https://github.com/AILab-CVC/YOLO-World)
 //!
+//! # Examples
 //!
+//! Refer to [All Runnable Demos](https://github.com/jamjamjon/usls/tree/main/examples)
 //!
 //! # Quick Start
 //!
-//! The following demo shows how to build model to run and annotate the results.
+//! The following demo shows how to build a `YOLO` with [`Options`], load `image(s)`, `video` and `stream` with [`DataLoader`], and annotate the model's inference results with [`Annotator`].
 //!
-//! ```rust, no_run
+//! ```ignore
 //! use usls::{models::YOLO, Annotator, DataLoader, Options, Vision, YOLOTask, YOLOVersion};
 //!
-//!
 //! fn main() -> anyhow::Result<()> {
 //!     // Build model with Options
 //!     let options = Options::new()
 //!         .with_trt(0)
 //!         .with_model("yolo/v8-m-dyn.onnx")?
-//!         .with_yolo_version(YOLOVersion::V8)     // YOLOVersion: V5, V6, V7, V8, V9, V10, RTDETR
-//!         .with_yolo_task(YOLOTask::Detect)       // YOLOTask: Classify, Detect, Pose, Segment, Obb
+//!         .with_yolo_version(YOLOVersion::V8) // YOLOVersion: V5, V6, V7, V8, V9, V10, RTDETR
+//!         .with_yolo_task(YOLOTask::Detect) // YOLOTask: Classify, Detect, Pose, Segment, Obb
 //!         .with_i00((1, 1, 4).into())
 //!         .with_i02((0, 640, 640).into())
 //!         .with_i03((0, 640, 640).into())
@@ -41,7 +42,7 @@
 //!     .build()?;
 //!
 //!     // Build annotator
-//!     let annotator = Annotator::new().with_saveout("YOLO-DataLoader");
+//!     let annotator = Annotator::new().with_saveout("YOLO-Demo");
 //!
 //!     // Run and Annotate images
 //!     for (xs, _) in dl {
@@ -53,21 +54,18 @@
 //! }
 //! ```
 //!
-//! Refer to [All Demos Here](https://github.com/jamjamjon/usls/tree/main/examples)
-//!
-//!
-//!
-//! # How to use Provided Models for Inference
+
+//! # What's More
 //!
-//! #### 1. Build Model
+//! This guide covers the process of using provided models for inference, including how to build a model, load data, annotate results, and retrieve the outputs. Click the sections below to expand for detailed instructions.
 //!
 //! <details>
-//! <summary>Click to expand</summary>
+//! <summary>Build the Model</summary>
 //!
-//! Using provided [`models`] with [`Options`]
+//! To build a model, you can use the provided [models] with [Options]:
 //!
-//! ```rust, no_run
-//! use usls::{ models::YOLO, Annotator, DataLoader, Options, Vision};
+//! ```ignore
+//! use usls::{models::YOLO, Annotator, DataLoader, Options, Vision};
 //!
 //! let options = Options::default()
 //!     .with_yolo_version(YOLOVersion::V8)  // YOLOVersion: V5, V6, V7, V8, V9, V10, RTDETR
@@ -76,9 +74,12 @@
 //! let mut model = YOLO::new(options)?;
 //! ```
 //!
-//! - Choose Execute Provider: `CUDA`(by default), `TensorRT`, or `CoreML`
+//! **And there're many options provided by [Options]**
 //!
-//! ```rust, no_run
+//! - **Choose Execution Provider:**  
+//!   Select `CUDA` (default), `TensorRT`, or `CoreML`:
+//!
+//! ```ignore
 //! let options = Options::default()
 //!     .with_cuda(0)
 //!     // .with_trt(0)
@@ -86,157 +87,136 @@
 //!     // .with_cpu();
 //! ```
 //!
-//! - Dynamic Input Shapes
-//! If your model has dynamic shapes, you need pre-specified it with [`MinOptMax`].
-//!
-//! `with_ixy()` means the y-th axis of the x-th input. e.g., `i00` is the first axis of the 1st input, batch usually
+//! - **Dynamic Input Shapes:**  
+//!   Specify dynamic shapes with [MinOptMax]:
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let options = Options::default()
 //!     .with_i00((1, 2, 4).into()) // batch(min=1, opt=2, max=4)
 //!     .with_i02((416, 640, 800).into()) // height(min=416, opt=640, max=800)
 //!     .with_i03((416, 640, 800).into()); // width(min=416, opt=640, max=800)
 //! ```
 //!
-//! - Set Confidence Thresholds for Each Category
+//! - **Set Confidence Thresholds:**  
+//!   Adjust thresholds for each category:
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let options = Options::default()
 //!     .with_confs(&[0.4, 0.15]); // class_0: 0.4, others: 0.15
 //! ```
 //!
-//! - [Optional] Set Class Names
+//! - **Set Class Names:**  
+//!   Provide class names if needed:
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let options = Options::default()
 //!     .with_names(&COCO_CLASS_NAMES_80);
 //! ```
 //!
-//! More options can be found in the [`Options`] documentation.
+//! **More options are detailed in the [Options] documentation.**  
+//!   
 //!
 //! </details>
 //!
-//!
-//! #### 2. Use [`DataLoader`] to load `Image(s)`, `Video` and `Stream`
-//!
 //! <details>
-//! <summary>Click to expand</summary>
-//!
-//! - Use [`DataLoader::try_read`] to laod single image
+//! <summary>Load Images, Video and Stream</summary>
 //!
-//! You can now load image from local file or remote(Github Release Page)
+//! - **Load a Single Image**  
+//! Use [DataLoader::try_read] to load an image from a local file or remote source:
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let x = DataLoader::try_read("./assets/bus.jpg")?; // from local
 //! let x = DataLoader::try_read("images/bus.jpg")?; // from remote
 //! ```
 //!
-//! Of course You can directly use [`image::ImageReader`]
+//! Alternatively, use [image::ImageReader] directly:
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let x = image::ImageReader::open("myimage.png")?.decode()?;
 //! ```
 //!
-//! - Use [`DataLoader] to load image(s), video, stream
-//!
-//! ```rust, no_run
-//!     // Build DataLoader
-//!     let dl = DataLoader::new(
-//!         "./assets/bus.jpg", // local image
-//!         // "images/bus.jpg",  // remote image
-//!         // "../set-negs",  // local images (from folder)
-//!         // "../hall.mp4",  // local video
-//!         // "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4",  // remote video
-//!         // "rtsp://admin:kkasd1234@192.168.2.217:554/h264/ch1/",  // stream
-//!     )?
-//!     .with_batch(3)  // iterate with batch_size = 3
-//!     .build()?;
-//!
-//!     // iterate
-//!     for (xs, _) in dl {}
+//! - **Load Multiple Images, Videos, or Streams**  
+//! Create a [DataLoader] instance for batch processing:
+//!
+//! ```ignore
+//! let dl = DataLoader::new(
+//!     "./assets/bus.jpg", // local image
+//!     // "images/bus.jpg",  // remote image
+//!     // "../set-negs",  // local images (from folder)
+//!     // "../hall.mp4",  // local video
+//!     // "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4",  // remote video
+//!     // "rtsp://admin:kkasd1234@192.168.2.217:554/h264/ch1/",  // stream
+//! )?
+//! .with_batch(3)  // iterate with batch_size = 3
+//! .build()?;
+//!
+//! // Iterate through the data
+//! for (xs, _) in dl {}
 //! ```
 //!
-//! - Use [`DataLoader::is2v`] to convert images into a video
+//! - **Convert Images to Video**  
+//! Use [DataLoader::is2v] to create a video from a sequence of images:
 //!
-//! ```rust, no_run
-//!     let fps = 24;
-//!     let image_folder = "runs/YOLO-DataLoader";
-//!     let saveout = ["runs", "is2v"];
-//!     let fps = 24;
-//!     DataLoader::is2v(image_folder, &saveout, 24)?;
+//! ```ignore
+//! let fps = 24;
+//! let image_folder = "runs/YOLO-DataLoader";
+//! let saveout = ["runs", "is2v"];
+//! DataLoader::is2v(image_folder, &saveout, fps)?;
 //! ```
 //!
-//!
 //! </details>
 //!
-//!
-//!
-//! #### 3. Use [`Annotator`] to annotate images  
-//!
-//!
 //! <details>
-//! <summary>Click to expand</summary>
-//!
+//! <summary>Annotate Inference Results</summary>
 //!
+//! - **Create an Annotator Instance**
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let annotator = Annotator::default();
 //! ```
 //!
-//! - Set Saveout Name
+//! - **Set Saveout Name:**
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let annotator = Annotator::default()
 //!     .with_saveout("YOLOs");
 //! ```
-//!     
-//! - Set Bboxes Line Width
 //!
-//! ```rust, no_run
+//! - **Set Bounding Box Line Width:**
+//!
+//! ```ignore
 //! let annotator = Annotator::default()
 //!     .with_bboxes_thickness(4);
 //! ```
-//!  
-//! - Disable Mask Plotting
-//!  
-//! ```rust, no_run
+//!
+//! - **Disable Mask Plotting**
+//!
+//! ```ignore
 //! let annotator = Annotator::default()
 //!     .without_masks(true);
 //! ```
-//!   
-//! More options can be found in the [`Annotator`] documentation.
-//!
-//!
-//! </details>    
 //!
+//! - **Perform Inference and nnotate the results**  
 //!
-//!
-//! #### 4. Run and Annotate
-//!
-//! <details>
-//! <summary>Click to expand</summary>
-//!
-//!
-//! ```rust, no_run
+//! ```ignore
 //! for (xs, _paths) in dl {
 //!     let ys = model.run(&xs)?;
 //!     annotator.annotate(&xs, &ys);
 //! }
 //! ```
 //!
-//! </details>    
-//!
+//! More options are detailed in the [Annotator] documentation.
 //!
-//! #### 5. Get Results
+//! </details>
 //!
 //! <details>
-//! <summary>Click to expand</summary>
+//! <summary>Retrieve Model's Inference Results</summary>
 //!
-//! The inference outputs of provided models will be saved to a [`Vec<Y>`].
+//! Retrieve the inference outputs, which are saved in a [`Vec<Y>`]:
 //!
-//! - For Example, Get Detection Bboxes with `y.bboxes()`
+//! - **Get Detection Bounding Boxes**
 //!
-//! ```rust, no_run
+//! ```ignore
 //! let ys = model.run(&xs)?;
 //! for y in ys {
 //!     // bboxes
@@ -254,16 +234,18 @@
 //!         }
 //!     }
 //! }
+//! ```
 //!
-//! </details>    
+//! </details>
 //!
-//! ```
+//! <details>
+//! <summary>Custom Model Implementation</summary>
 //!
-//! # Also, You Can Implement Your Own Model with [`OrtEngine`] and [`Options`]
+//! You can also implement your own model using [OrtEngine] and [Options]. [OrtEngine] supports ONNX model loading, metadata parsing, dry_run, inference, and other functions, with execution providers such as CUDA, TensorRT, CoreML, etc.
 //!
-//! [`OrtEngine`] provides ONNX model loading, metadata parsing, dry_run, inference, and other functions, supporting EPs such as CUDA, TensorRT, CoreML, etc. You can use it as the ONNXRuntime engine for building models.
+//! For more details, refer to the [Demo: Depth-Anything](https://github.com/jamjamjon/usls/blob/main/src/models/depth_anything.rs).
 //!
-//! Refer to [Demo: Depth-Anything](https://github.com/jamjamjon/usls/blob/main/src/models/depth_anything.rs) for more details.
+//! </details>
 
 mod core;
 pub mod models;
diff --git a/src/ys/y.rs b/src/ys/y.rs
index c116a58..d7b8827 100644
--- a/src/ys/y.rs
+++ b/src/ys/y.rs
@@ -95,13 +95,6 @@ impl Y {
     /// # Returns
     ///
     /// * `Self` - The updated struct instance with the new probabilities set.
-    ///
-    /// # Examples
-    ///
-    /// ```
-    /// let probs = Prob::default();
-    /// let y = Y::default().with_probs(&probs);
-    /// ```
     pub fn with_probs(mut self, probs: &Prob) -> Self {
         self.probs = Some(probs.clone());
         self