add some new python demos

examples/clip/README.md

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+## Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/clip/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android_arm64-v8a/clip_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android_arm64-v8a/clip_demo /data/local/tmp/
+adb push model/vision_model_int8_A311D2.adla /data/local/tmp/
+adb push clip_datasets/ /data/local/tmp/
+adb push test_hat_0.jpg /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x clip_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./clip_demo <model_path> [base_dir] [json_filename]
+./clip_demo vision_model_int8_A311D2.adla ./clip_datasets/ clip_text_res.json
+```
+
+**Note:** 
+- Replace `vision_model_int8_A311D2.adla` with your actual model file path.
+- The `base_dir` and `json_filename` parameters are optional. You can also use environment variables `CLIP_BASE_DIR` and `CLIP_JSON_FILENAME`.
+- The program will prompt you to enter image paths interactively. Enter "exit" to quit.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `Pillow`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy Pillow amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+# Basic usage (process current directory)
+python clip.py --model-path ./vision_model_int8_A311D2.adla
+
+# Specify image directory or file
+python clip.py --model-path ./vision_model_int8_A311D2.adla --image-dir ./
+
+# Specify base directory and JSON filename
+python clip.py --model-path ./vision_model_int8_A311D2.adla --base-dir ./clip_datasets/ --json-filename clip_text_res.json
+```
+
+The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the specified directory or process a single image file, and display the best matching dataset for each image.
+
+5. Results
+
+The program will print the best matching dataset path for each processed image. The program searches through all dataset folders in the base directory and finds the text feature with the highest similarity to the input image.
+
+**Example output:**
+```
+# python demo result
+Model initialized successfully.
+
+Found 2 image file(s) to process
+Searching in base directory: ./clip_datasets/
+
+Processing image: test_jacket_0.jpg
+  Best matching dataset: ./clip_datasets/shirt10_jacket7
+Searching in base directory: ./clip_datasets/
+
+Processing image: test_hat_0.jpg
+  Best matching dataset: ./clip_datasets/hat1_jd
+
+Total results: 2
+Index[0]: ./clip_datasets/shirt10_jacket7
+Index[1]: ./clip_datasets/hat1_jd
+
+Done.
+```
+
+The program returns the dataset folder path that contains the text feature with the highest similarity to the input image. Each result represents the best matching dataset for the corresponding input image.

examples/clip/py/clip.py

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+import numpy as np
+import os
+import argparse
+import json
+import re
+from PIL import Image
+from amlnnlite.api import AMLNNLite
+
+
+def preprocess_image(image_path: str, target_size: int = 224) -> np.ndarray:
+    """
+    Preprocess image for CLIP model.
+    
+    Steps:
+        1. Load image and convert to RGB
+        2. Scale the shorter side to target_size
+        3. Center crop to target_size x target_size
+        4. Normalize with CLIP mean and std
+    
+    Args:
+        image_path (str): Path to input image
+        target_size (int): Target image size (default: 224)
+    
+    Returns:
+        np.ndarray: Preprocessed image data with shape (target_size, target_size, 3)
+    """
+    # Load image
+    img = Image.open(image_path).convert("RGB")
+    width, height = img.size
+    
+    # Scale the shorter side
+    scale = target_size / min(width, height)
+    new_w = int(round(width * scale))
+    new_h = int(round(height * scale))
+    
+    # Resize
+    img = img.resize((new_w, new_h), Image.BILINEAR)
+    
+    # Center crop
+    left = (new_w - target_size) // 2
+    top = (new_h - target_size) // 2
+    img = img.crop((left, top, left + target_size, top + target_size))
+    
+    # Convert to numpy array and normalize to [0, 1]
+    img_array = np.array(img, dtype=np.float32) / 255.0
+    
+    # CLIP normalization
+    mean = np.array([0.48145466, 0.4578275, 0.40821073], dtype=np.float32)
+    std = np.array([0.26862954, 0.26130258, 0.27577711], dtype=np.float32)
+    
+    # Normalize: (x - mean) / std
+    img_array = (img_array - mean) / std
+    
+    # Return in NHWC format
+    return img_array
+
+
+def post_process(
+    image_features: np.ndarray,
+    text_features: np.ndarray,
+    scale: float = 100.00000762939453,
+    use_cosine: bool = True,
+    apply_scale: bool = True,
+) -> float:
+    """
+    Calculate similarity between image and text features.
+    
+    Args:
+        image_features (np.ndarray): Image feature vector
+        text_features (np.ndarray): Text feature vector
+        scale (float): Scale factor for similarity calculation
+        use_cosine (bool): If True, L2-normalize both vectors before dot product (cosine similarity)
+        apply_scale (bool): If True, multiply by scale after dot product
+    
+    Returns:
+        float: Similarity score
+    """
+    img_vec = image_features.flatten().astype(np.float32)
+    txt_vec = np.array(text_features, dtype=np.float32).flatten()
+    
+    if len(img_vec) != len(txt_vec):
+        raise ValueError(f"Feature dimension mismatch: image={len(img_vec)}, text={len(txt_vec)}")
+    
+    if use_cosine:
+        img_norm = np.linalg.norm(img_vec) + 1e-8
+        txt_norm = np.linalg.norm(txt_vec) + 1e-8
+        img_vec = img_vec / img_norm
+        txt_vec = txt_vec / txt_norm
+    
+    dot_product = np.dot(img_vec, txt_vec)
+    
+    similarity = dot_product * scale if apply_scale else dot_product
+    
+    return float(similarity)
+
+
+def extract_index(filename: str) -> int:
+    """
+    Extract index from filename pattern: test_xxx_index.jpg
+    
+    Args:
+        filename (str): Filename to extract index from
+    
+    Returns:
+        int: Extracted index, or -1 if pattern doesn't match
+    """
+    pattern = r"test_\w+_(\d+)\.jpg"
+    match = re.match(pattern, filename)
+    if match:
+        return int(match.group(1))
+    return -1
+
+
+def process_image_dir(
+    amlnn: AMLNNLite,
+    image_dir_path: str,
+    base_dir: str = "",
+    json_filename: str = ""
+) -> list:
+    """
+    Process image directory and find best matching text dataset.
+    
+    Args:
+        amlnn: AMLNNLite instance
+        image_dir_path (str): Path to directory containing test images
+        base_dir (str): Base directory for clip datasets (optional, can use CLIP_BASE_DIR env var)
+        json_filename (str): JSON filename in each dataset folder (optional, can use CLIP_JSON_FILENAME env var)
+    
+    Returns:
+        list: List of best matching dataset paths
+    """
+    results = []
+    file_pattern = re.compile(r"test_(\w+)_\d+\.jpg")
+    image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.JPG', '.JPEG', '.PNG', '.BMP'}
+    
+    if not base_dir:
+        base_dir = os.getenv("CLIP_BASE_DIR", "./clip_datasets/")
+    
+    if not json_filename:
+        json_filename = os.getenv("CLIP_JSON_FILENAME", "clip_text_res.json")
+    
+    matched_files = []
+    if os.path.isdir(image_dir_path):
+        for filename in os.listdir(image_dir_path):
+            filepath = os.path.join(image_dir_path, filename)
+            if os.path.isfile(filepath):
+                if file_pattern.match(filename):
+                    matched_files.append((filename, filepath, True))  
+                elif any(filename.lower().endswith(ext) for ext in image_extensions):
+                    matched_files.append((filename, filepath, False))  
+    elif os.path.isfile(image_dir_path):
+        filename = os.path.basename(image_dir_path)
+        if any(filename.lower().endswith(ext) for ext in image_extensions):
+            has_pattern = bool(file_pattern.match(filename))
+            matched_files.append((filename, image_dir_path, has_pattern))
+        else:
+            print(f"Error: {image_dir_path} is not a valid image file")
+            return results
+    else:
+        print(f"Error: {image_dir_path} is not a valid directory or file")
+        return results
+    
+    if not matched_files:
+        print(f"Warning: No image files found in {image_dir_path}")
+        return results
+    
+    print(f"Found {len(matched_files)} image file(s) to process")
+    
+    matched_files.sort(key=lambda x: extract_index(x[0]) if x[2] else 999999)
+    
+    # Process each image
+    for filename, filepath, has_pattern in matched_files:
+        if has_pattern:
+            match = file_pattern.match(filename)
+            if match:
+                name = match.group(1)
+            else:
+                name = ""  
+        else:
+            name = ""
+        
+        # Preprocess image
+        try:
+            input_data = preprocess_image(filepath)
+            input_data = np.expand_dims(input_data, axis=0)
+        except Exception as e:
+            print(f"Error preprocessing image {filename}: {e}")
+            continue
+        
+        # Run inference
+        try:
+            outputs = amlnn.inference(inputs=[input_data])
+            model_output = outputs[0]  
+            if isinstance(model_output, np.ndarray):
+                model_output = model_output.astype(np.float32)
+            else:
+                model_output = np.array(model_output, dtype=np.float32)
+            model_output = model_output.flatten()
+        except Exception as e:
+            print(f"Error running inference on {filename}: {e}")
+            continue
+        
+        max_sim = float('-inf')
+        best_key = ""
+        best_id = ""
+        
+        if not os.path.isdir(base_dir):
+            print(f"Error: Base directory does not exist: {base_dir}")
+            continue
+        
+        print(f"Searching in base directory: {base_dir}")
+        folder_count = 0
+        for folder_name in os.listdir(base_dir):
+            folder_path = os.path.join(base_dir, folder_name)
+            if not os.path.isdir(folder_path):
+                continue
+            
+            if has_pattern and name and name not in folder_name:
+                continue
+            
+            folder_count += 1
+            
+            vit_res_path = os.path.join(folder_path, json_filename)
+            if not os.path.isfile(vit_res_path):
+                print(f"Warning: JSON file not found: {vit_res_path}")
+                continue
+            
+            try:
+                with open(vit_res_path, 'r', encoding='utf-8') as f:
+                    vit_json = json.load(f)
+                
+                    for key, text_vec in vit_json.items():
+                        if isinstance(text_vec, list):
+                            text_features = np.array(text_vec, dtype=np.float32)
+                            sim_scaled = post_process(
+                                model_output,
+                                text_features,
+                                use_cosine=True,
+                                apply_scale=True,
+                            )
+                            
+                            if sim_scaled > max_sim:
+                                max_sim = sim_scaled
+                                best_key = key
+                                best_id = folder_name
+            except Exception as e:
+                print(f"Error loading JSON file {vit_res_path}: {e}")
+                continue
+        
+        if best_key and best_id:
+            best_path = os.path.join(base_dir, best_id)
+            results.append(best_path)
+            print(f"\nProcessing image: {filename}")
+            print(f"  Best matching dataset: {best_path}")
+        else:
+            print(f"\nProcessing image: {filename}")
+            print(f"  No matching dataset found (searched {folder_count} folder(s))")
+    
+    return results
+
+
+def main():
+    parser = argparse.ArgumentParser(description='CLIP Image-Text Matching Demo')
+    parser.add_argument('--model-path', required=True, help='Path to the CLIP model file')
+    parser.add_argument('--base-dir', default='./clip_datasets/', help='Base directory for clip datasets (can also use CLIP_BASE_DIR env var)')
+    parser.add_argument('--json-filename', default='clip_text_res.json', help='JSON filename in each dataset folder (can also use CLIP_JSON_FILENAME env var, default: clip_text_res.json)')
+    parser.add_argument('--image-dir', default='./', help='Image directory or single image file to process (optional, will prompt if not provided)')
+    args = parser.parse_args()
+    
+    # Initialize AMLNNLite
+    print("Initializing model...")
+    amlnn = AMLNNLite()
+    amlnn.config(model_path=args.model_path)
+    amlnn.init()
+    print("Model initialized successfully.\n")
+    
+    # Process images
+    if args.image_dir:
+        results = process_image_dir(amlnn, args.image_dir, args.base_dir, args.json_filename)
+        print(f"\nTotal results: {len(results)}")
+        for i, result in enumerate(results):
+            print(f"Index[{i}]: {result}")
+    else:
+        while True:
+            image_path = input("\nPlease enter the JPG image path or directory (enter 'exit' to quit):\n").strip()
+            
+            if image_path.lower() == 'exit':
+                break
+            
+            if not image_path:
+                print("The path cannot be empty.")
+                continue
+            
+            results = process_image_dir(amlnn, image_path, args.base_dir, args.json_filename)
+            
+            for i, result in enumerate(results):
+                print(f"Index[{i}]: {result}")
+    
+    amlnn.uninit()
+    print("\nDone.")
+
+
+if __name__ == "__main__":
+    main()

examples/mobilenet/README.md

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+
+##  Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/mobilenet/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android/mobilenet_v2_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android/mobilenet_v2_demo /data/local/tmp/
+adb push model/mobilenet_v2_1.0_224_quant_A311D2.adla /data/local/tmp/
+adb push model/cat_224x224.jpg /data/local/tmp/
+adb push model/labels.txt /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x mobilenet_v2_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./mobilenet_v2_demo <model_path> <image_path> <labels_path>
+./mobilenet_v2_demo mobilenet_v2_1.0_224_quant_A311D2.adla cat_224x224.jpg labels.txt
+```
+
+**Note:** Replace `mobilenet_v2_1.0_224_quant_A311D2.adla` with your actual model file path.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `Pillow`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy Pillow amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+# Basic usage
+python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla
+
+# Run with performance testing (100 cycles)
+python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla --run-cycles 100
+```
+
+The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and display top-5 classification results for each image.
+
+## Results
+
+The program will print the top-5 classification results with probabilities for each processed image. 
+
+**Example output:**
+```
+# python demo result
+============================================================
+Processing image 1/3: dog_224x224.jpg
+============================================================
+
+Top-5 Classification Results:
+  1. Shih-Tzu                  (probability: 0.9239)
+  2. Pekinese                  (probability: 0.0476)
+  3. Lhasa                     (probability: 0.0263)
+  4. Brabancon griffon         (probability: 0.0004)
+  5. Dandie Dinmont            (probability: 0.0003)
+
+============================================================
+Processing image 2/3: cat_224x224.jpg
+============================================================
+
+Top-5 Classification Results:
+  1. tiger cat                 (probability: 0.4774)
+  2. tabby                     (probability: 0.4324)
+  3. Egyptian cat              (probability: 0.0542)
+  4. lynx                      (probability: 0.0150)
+  5. Persian cat               (probability: 0.0025)
+
+============================================================
+Processing image 3/3: fish_224x224.jpeg
+============================================================
+
+Top-5 Classification Results:
+  1. goldfish                  (probability: 0.9998)
+  2. conch                     (probability: 0.0001)
+  3. trifle                    (probability: 0.0000)
+  4. axolotl                   (probability: 0.0000)
+  5. American lobster          (probability: 0.0000)
+```
+
+The classification results show the model's confidence scores (probabilities) for each detected class, with the highest probability indicating the most likely classification.
+

examples/mobilenet/py/mobilenetv2.py

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+import numpy as np
+import os
+import glob
+import argparse
+import time
+from PIL import Image
+from amlnnlite.api import AMLNNLite
+
+
+def preprocess(image_path: str) -> np.ndarray:
+    """
+    Preprocess the input image for MobileNetV2 quantized model.
+
+    Steps:
+        1. Load image and convert to RGB
+        2. Resize to 224x224
+        3. Normalize to [-1, 1]
+        4. Quantize to uint8 with zero-point = 128, scale = 0.0078125
+
+    Args:
+        image_path (str): Path to input image
+
+    Returns:
+        np.ndarray: Preprocessed image data with shape (1, 224, 224, 3)
+    """
+    img = Image.open(image_path).convert("RGB").resize((224, 224))
+    img = np.array(img, dtype=np.float32)
+
+    # Normalize to [-1, 1]
+    img = img / 127.5 - 1.0
+
+    # Expand batch dimension
+    data = np.expand_dims(img, axis=0)
+
+    # Quantization (uint8)
+    data = data / 0.0078125 + 128
+    data = np.clip(data, 0, 255).astype(np.uint8)
+
+    return data
+
+
+def postprocess_topk(predictions: np.ndarray, labels_path: str, k: int = 5, use_softmax: bool = True) -> None:
+    """
+    Postprocess model output and print top-K classification results.
+
+    Args:
+        predictions (np.ndarray): Raw model output (logits)
+        labels_path (str): Path to labels.txt
+        k (int): Number of top results to display
+        use_softmax (bool): If True, apply softmax to convert logits to probabilities
+    """
+    predictions = predictions.reshape(-1).astype(np.float32)
+
+    if use_softmax:
+        exp_predictions = np.exp(predictions - np.max(predictions))
+        probabilities = exp_predictions / np.sum(exp_predictions)
+        scores = probabilities
+        score_label = "probability"
+    else:
+        scores = predictions
+        score_label = "score"
+
+    # Load labels
+    with open(labels_path, "r", encoding="utf-8") as f:
+        labels = [line.strip() for line in f.readlines()]
+
+    # Get top-k indices based on scores
+    top_indices = np.argsort(scores)[::-1][:k]
+
+    print(f"\nTop-{k} Classification Results:")
+    for rank, idx in enumerate(top_indices, start=1):
+        label = labels[idx] if idx < len(labels) else f"Class {idx}"
+        score = scores[idx]
+        if use_softmax:
+            print(f"  {rank}. {label:<25} ({score_label}: {score:.4f})")
+        else:
+            print(f"  {rank}. {label:<25} ({score_label}: {score:.6f})")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model-path', default='./mobilenet_v2_1.0_224_quant.tflite')
+    parser.add_argument('--run-cycles', type=int, default=1, help='Number of inference cycles to run (for performance testing)')
+    args = parser.parse_args()
+    
+    # Initialize AMLNNLite
+    amlnn = AMLNNLite()
+    amlnn.config(
+        model_path=args.model_path           # Model file path, Support ADLD and quantized TFlite models
+    )
+    amlnn.init()
+
+    # Find all image files in the 01_export_model directory
+    image_dir = "./"
+    image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp"]
+    image_files = []
+    for ext in image_extensions:
+        image_files.extend(glob.glob(os.path.join(image_dir, ext)))
+        image_files.extend(glob.glob(os.path.join(image_dir, ext.upper())))
+    
+    if not image_files:
+        print("No image files found in", image_dir)
+        amlnn.uninit()
+        return
+    
+    print(f"Found {len(image_files)} image files to process:")
+    for img_file in image_files:
+        print(f"  - {os.path.basename(img_file)}")
+    print()
+
+    # Process each image
+    for i, image_path in enumerate(image_files, 1):
+        print(f"=" * 60)
+        print(f"Processing image {i}/{len(image_files)}: {os.path.basename(image_path)}")
+        print(f"=" * 60)
+        
+        try:
+            # Preprocess input
+            input_data = preprocess(image_path)
+
+            # Run inference for specified cycles
+            inference_times = []
+            outputs = None
+            for cycle in range(args.run_cycles):
+                start_time = time.time()
+                outputs = amlnn.inference(
+                    inputs=[input_data]
+                )
+                inference_time = (time.time() - start_time) * 1000  # Convert to milliseconds
+                inference_times.append(inference_time)
+            
+            # Print performance statistics if running multiple cycles
+            if args.run_cycles > 1:
+                avg_time = np.mean(inference_times)
+                min_time = np.min(inference_times)
+                max_time = np.max(inference_times)
+                print(f"\nInference Performance ({args.run_cycles} cycles):")
+                print(f"  Average: {avg_time:.2f} ms")
+                print(f"  Min: {min_time:.2f} ms")
+                print(f"  Max: {max_time:.2f} ms")
+            
+            # Postprocess results (only show results from last inference)
+            postprocess_topk(outputs[0], "./labels.txt", k=5, use_softmax=True)
+            
+        except Exception as e:
+            print(f"Error processing {os.path.basename(image_path)}: {e}")
+        
+        print()
+
+    # Optional visualization
+    amlnn.visualize()
+
+    # Release resources
+    amlnn.uninit()
+
+
+if __name__ == "__main__":
+    main()

examples/resnet/README.md

@@ -0,0 +1,165 @@
+# resnet
+
+## 1.Overview
+
+
+
+## 2.Model Download
+
+- **Open Source model**
+
+  - **Open Source projects:** 
+
+  - **Export Model Step:**	
+
+    - **Install ultralytics**
+
+        pip install torch==2.4.1
+
+        pip install torchvision==0.19.1
+
+        pip install ultralytics==8.3.0
+
+    - **Download weights**
+
+      
+
+    - **Export Model**
+
+      ```
+      
+      ```
+
+
+- **Exported Model**
+
+  ​	link to amlogic server( **onnx model   or quantized tflite**)
+
+  
+
+## 3. Model Conversion
+
+```
+cd model
+Usage:   ./adla_covnert.sh model_path adla_tookkit_path target_platform
+
+example
+
+```
+
+| Parameter         | Discription                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| model_path        | onnx model path                                              |
+| adla_tookkit_path | path to adla_toolkit                                         |
+| target_platform   | Specify target platform. for A311D2 : PRODUCT_PID0XA003. for S905X5:  PRODUCT_PID0XA005 |
+
+
+
+## 4. Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/resnet/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android/resnet_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android/resnet_demo /data/local/tmp/
+adb push model/res2net50_int8_A311D2.adla /data/local/tmp/
+adb push imgs /data/local/tmp/
+adb push labels.txt /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x resnet_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./resnet_demo <model_path> <image_dir> <labels.txt>
+./resnet_demo res2net50_int8_A311D2.adla imgs/ labels.txt
+```
+
+**Note:** Replace `res2net50_int8_A311D2.adla` with your actual model file path.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `opencv-python`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+python resnet.py \
+    --model-path ./res2net50_int8_A311D2.adla \
+    --image-dir ./imgs \
+    --labels labels.txt \
+    --run-cycles 1 \
+    --loglevel INFO
+```
+Argument Descriptions:
+| Argument         | Description                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| --board-work-path       | Work path on board, default is /data/local/tmp    |
+| --model-path | path to .adla model  |
+| --image-dir   | Directory containing test images |
+| --labels   | Path to synset_words.txt or labels.txt |
+| --run-cycles   | Number of inference cycles, default is 1 |
+| --loglevel   | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
+
+The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and save results to a `{model_name}_result` folder.
+
+## 5.Results
+**Performance Feedback**
+
+By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
+- Hardware Information: System and ADLA library versions.
+- Model Overview: Basic input/output configurations.
+- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
+
+**Classification Output**
+
+For each image, the program prints the Top-5 classification results with their respective scores:
+```bash
+============================================================
+Processing image 1/1: dog.jpg
+============================================================                                                                                        Top-5 Results:                                                                                                          
+1: Pekinese                     score=9.851644                                                                                 
+2: West Highland white terrier  score=5.055449                                                                          
+3: Maltese dog                  score=4.796195                                                                                 
+4: basenji                      score=3.111045                                                                                 
+5: Scotch terrier               score=2.786978                                                                                 ============================================================ 
+```
+**Profiling Visualization**
+
+After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
+- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
+- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
+- `pie_charts_distribution.html`: Overall resource allocation.
+
+You can pull the result folder back to view it:
+```bash
+adb pull /data/local/tmp/res2net50_int8_A311D2
+```
+
+Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.
+
+![alt text](Visualization.png)

examples/resnet/py/resnet.py

@@ -52,8 +52,8 @@ def postprocess_topk(logits, labels, k=5):
 def main():
     parser = argparse.ArgumentParser(description="Classification AMLNNLite Demo")
 
-    parser.add_argument('--board-work-path', default='/data/nn', help='Work path on board')
+    parser.add_argument('--board-work-path', default='/data/local/tmp', help='Work path on board')
-    parser.add_argument('--model-path', required=True, help='Path to .adla or .tflite model')
+    parser.add_argument('--model-path', required=True, help='Path to .adla model')
     parser.add_argument('--image-dir', required=True, help='Directory containing test images')
     parser.add_argument('--labels', required=True, help='Path to synset_words.txt or labels.txt')
     parser.add_argument('--run-cycles', type=int, default=1, help='Number of inference cycles')

examples/retinaface/README.md

@@ -0,0 +1,160 @@
+# retinaface
+
+## 1.Overview
+
+
+## 2.Model Download
+
+- **Open Source model**
+
+  - **Open Source projects:** 
+
+  - **Export Model Step:**	
+
+    - **Install ultralytics**
+
+        pip install torch==2.4.1
+
+        pip install torchvision==0.19.1
+
+        pip install ultralytics==8.3.0
+
+    - **Download weights**
+
+      
+
+    - **Export Model**
+
+    
+
+
+- **Exported Model**
+
+  ​	link to amlogic server( **onnx model   or quantized tflite**)
+
+  
+
+## 3. Model Conversion
+
+```
+cd model
+Usage:   ./adla_covnert.sh model_path adla_tookkit_path target_platform
+
+example
+ 
+```
+
+| Parameter         | Discription                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| model_path        | onnx model path                                              |
+| adla_tookkit_path | path to adla_toolkit                                         |
+| target_platform   | Specify target platform. for A311D2 : PRODUCT_PID0XA003. for S905X5:  PRODUCT_PID0XA005 |
+
+
+
+## 4. Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/retinaface/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android/retinaface_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android/retinaface_demo /data/local/tmp/
+adb push model/RetinaFace_int8_A311D2.adla /data/local/tmp/
+adb push imgs /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x retinaface_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./retinaface_demo <model_path> <image_dir>
+./retinaface_demo RetinaFace_int8_A311D2.adla ./imgs
+```
+
+**Note:** Replace `RetinaFace_int8_A311D2.adla` with your actual model file path.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `opencv-python`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+python RetinaFace.py \
+    --model-path ./RetinaFace_int8_A311D2.adla \
+    --image-dir ./imgs \
+    --run-cycles 1 \
+    --loglevel INFO
+```
+
+Argument Descriptions:
+| Argument         | Description                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| --board-work-path       | Work path on board, default is /data/local/tmp    |
+| --model-path | path to .adla model  |
+| --image-dir   | Directory containing test images |
+| --run-cycles   | Number of inference cycles, default is 1 |
+| --loglevel   | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
+
+The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and save results to a `{model_name}_result` folder.
+
+## 5.Results
+
+**Performance Feedback**
+
+By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
+- Hardware Information: System and ADLA library versions.
+- Model Overview: Basic input/output configurations.
+- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
+
+**Detection Output**
+
+The program will print the detection count. The output images, featuring bounding boxes and five facial landmarks (eyes, nose, and mouth corners), will be saved to the `{model_name}_result` folder.
+
+
+You can pull the result folder back to view it:
+```bash
+adb pull /data/local/tmp/RetinaFace_int8_A311D2_result
+```
+![alt text](result.jpg)
+
+
+**Profiling Visualization**
+
+After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
+- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
+- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
+- `pie_charts_distribution.html`: Overall resource allocation.
+
+You can pull the result folder back to view it:
+```bash
+adb pull /data/local/tmp/RetinaFace_int8_A311D2
+```
+
+Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.
+
+![alt text](Visualization.png)

examples/retinaface/cpp/src/main.cpp

@@ -55,7 +55,8 @@ int main(int argc, char** argv) {
     size_t num_priors = priors.size();
     std::vector<float> chw_buffer(kInputW * kInputH * 3);
 
-    const std::string out_dir = "retinaface_result";
+    std::string model_stem = fs::path(argv[1]).stem().string();
+    const std::string out_dir = model_stem + "_result";
     fs::create_directory(out_dir);
 
     std::vector<fs::path> image_paths;

examples/retinaface/py/RetinaFace.py

@@ -107,7 +107,7 @@ def preprocess(img_path, input_size=(320, 320)):
 
 def main():
     parser = argparse.ArgumentParser(description="RetinaFace AMLNNLite Demo")
-    parser.add_argument('--board-work-path', type=str, default='/data/nn')
+    parser.add_argument('--board-work-path', type=str, default='/data/local/tmp')
     parser.add_argument('--model-path', required=True, help='Path to .adla model')
     parser.add_argument('--image-dir', required=True, help='Directory of test images')
     parser.add_argument('--run-cycles', type=int, default=1, help='Inference cycles')
@@ -122,13 +122,18 @@ def main():
     amlnn.init()
 
     priors = PriorBox((320, 320)).forward()
-    image_files = sorted(glob.glob(os.path.join(args.image_dir, "*.[jp][pn][g]")))
+    image_files = []
+    for ext in ["*.jpg", "*.jpeg", "*.png", "*.bmp"]:
+        image_files.extend(glob.glob(os.path.join(args.image_dir, ext)))
+        image_files.extend(glob.glob(os.path.join(args.image_dir, ext.upper())))
+    image_files.sort()
     
     if not image_files:
         print(f"No images found in {args.image_dir}")
         amlnn.uninit(); return
 
-    res_dir = "retinaface_result"
+    model_stem = Path(args.model_path).stem
+    res_dir = f"{model_stem}_result"
     os.makedirs(res_dir, exist_ok=True)
 
     for idx, img_path in enumerate(image_files, start=1):

examples/yolov11/README.md

@@ -0,0 +1,170 @@
+# yolov11
+
+## 1.Overview
+
+​		YOLOv11 was released by Ultralytics on October 2, 2024. It introduces significant architectural refinements and efficiency optimizations, delivering superior accuracy-speed trade-offs compared to previous generations. With enhanced feature extraction capabilities, YOLOv11 is designed for high-performance real-time applications—including object detection, instance segmentation, and pose estimation—to handle demanding tasks in a wide range of applications. 
+
+## 2.Model Download
+
+- **Open Source model**
+
+  - **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.3.0
+
+  - **Export Model Step:**	
+
+    - **Install ultralytics**
+
+        pip install torch==2.4.1
+
+        pip install torchvision==0.19.1
+
+        pip install ultralytics==8.3.0
+
+    - **Download weights**
+
+      wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt
+
+      wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt
+
+      wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt
+
+    - **Export Model**
+
+      ```
+      from ultralytics import YOLO
+      
+      model = YOLO("yolo11n.pt")
+      model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
+      ```
+
+
+- **Exported Model**
+
+  ​	link to amlogic server( **onnx model   or quantized tflite**)
+
+  
+
+## 3. Model Conversion
+
+```
+cd model
+Usage:   ./adla_covnert.sh model_path adla_tookkit_path target_platform
+
+example
+ ./adla_covnert.sh yolov11m.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
+ ./adla_covnert.sh yolov11s.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
+ ./adla_covnert.sh yolov11n.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
+```
+
+| Parameter         | Discription                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| model_path        | onnx model path                                              |
+| adla_tookkit_path | path to adla_toolkit                                         |
+| target_platform   | Specify target platform. for A311D2 : PRODUCT_PID0XA003. for S905X5:  PRODUCT_PID0XA005 |
+
+
+
+## 4. Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/yolov11/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android/yolo11_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android/yolo11_demo /data/local/tmp/
+adb push model/yolov11n_int8_A311D2.adla /data/local/tmp/
+adb push imgs /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x yolo11_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./yolo11_demo <model_path> <image_dir>
+./yolo11_demo yolov11n_int8_A311D2.adla ./imgs
+```
+
+**Note:** Replace `yolov11n_int8_A311D2.adla` with your actual model file path.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `opencv-python`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+python yolov11.py \
+    --model-path ./yolov11n_int8_A311D2.adla \
+    --image-dir ./imgs \
+    --run-cycles 1 \
+    --loglevel INFO
+```
+
+Argument Descriptions:
+| Argument         | Description                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| --board-work-path       | Work path on board, default is /data/local/tmp    |
+| --model-path | path to .adla model  |
+| --image-dir   | Directory containing test images |
+| --run-cycles   | Number of inference cycles, default is 1 |
+| --loglevel   | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
+
+The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and save results to a `{model_name}_result` folder.
+
+## 5.Results
+
+**Performance Feedback**
+
+By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
+- Hardware Information: System and ADLA library versions.
+- Model Overview: Basic input/output configurations.
+- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
+
+**Detection Output**
+
+The program will print the detection count. The result image with bounding boxes will be saved to the specified output path (`{model_name}_result`). 
+
+You can pull the result folder back to view it:
+```bash
+adb pull /data/local/tmp/yolov11n_int8_A311D2_result
+```
+![alt text](result.jpg)
+
+**Profiling Visualization**
+
+After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
+- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
+- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
+- `pie_charts_distribution.html`: Overall resource allocation.
+
+You can pull the result folder back to view it:
+```bash
+adb pull /data/local/tmp/yolov11n_int8_A311D2
+```
+
+Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.
+
+![alt text](Visualization.png)

examples/yolov11/py/yolov11.py

@@ -93,8 +93,8 @@ def postprocess(outputs, scale, pad, strides=[32,16,8], conf_threshold=0.25, iou
 
 def main():
     parser = argparse.ArgumentParser(description="YOLOV11 AMLNNLite Demo")
-    parser.add_argument('--board-work-path', default='/data/nn', help='Work path on board')
+    parser.add_argument('--board-work-path', default='/data/local/tmp', help='Work path on board')
-    parser.add_argument('-m', '--model-path', required=True, help='Path to .adla or .tflite model')
+    parser.add_argument('-m', '--model-path', required=True, help='Path to .adla model')
     parser.add_argument('--image-dir', required=True, help='Directory containing test images')
     parser.add_argument('--run-cycles', type=int, default=1, help='Inference cycles for profiling')
     parser.add_argument('--loglevel', default='WARNING', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'], help='Log level')
@@ -110,7 +110,7 @@ def main():
     amlnn.init()
 
     image_files = []
-    for ext in ["*.jpg", "*.jpeg", "*.png"]:
+    for ext in ["*.jpg", "*.jpeg", "*.png", "*.bmp"]:
         image_files.extend(glob.glob(os.path.join(args.image_dir, ext)))
         image_files.extend(glob.glob(os.path.join(args.image_dir, ext.upper())))
     image_files.sort()

examples/yoloworld/py/yoloworld.py

@@ -390,7 +390,6 @@ Examples:
         traceback.print_exc()
         return 1
     
-    # Use try-finally to ensure resources are released
     try:
         # Find all image files in the specified directory
         image_dir = args.image_dir
@@ -493,7 +492,6 @@ Examples:
             amlnn.visualize()
             print("Visualization charts saved.")
     finally:
-        # Always release resources
         if amlnn is not None:
             print("\nReleasing resources...")
             amlnn.uninit()