feat: Add yolov5n, update docs and ruff code format

* ref: Remove type conversion and update face class

* fix: change the type to float32

* chore: Update all examples, testing with latest version

* docs: Update docs reflecting the recent changes

CONTRIBUTING.md

@@ -56,3 +56,5 @@ Example notebooks demonstrating library usage:
 
 Open an issue or start a discussion on GitHub.
 
+
+

MODELS.md

@@ -80,10 +80,11 @@ detector = SCRFD(
 
 YOLOv5-Face models provide excellent detection accuracy with 5-point facial landmarks, optimized for real-time applications.
 
-| Model Name     | Params | Size | Easy   | Medium | Hard   | FLOPs (G) | Use Case                       |
-| -------------- | ------ | ---- | ------ | ------ | ------ | --------- | ------------------------------ |
-| `YOLOV5S` ⭐ | 7.1M   | 28MB | 94.33% | 92.61% | 83.15% | 5.751     | **Real-time + accuracy** |
-| `YOLOV5M`    | 21.1M  | 84MB | 95.30% | 93.76% | 85.28% | 18.146    | High accuracy                  |
+| Model Name     | Size | Easy   | Medium | Hard   | Use Case                       |
+| -------------- | ---- | ------ | ------ | ------ | ------------------------------ |
+| `YOLOV5N`    | 11MB | 93.61% | 91.52% | 80.53% | Lightweight/Mobile             |
+| `YOLOV5S` ⭐ | 28MB | 94.33% | 92.61% | 83.15% | **Real-time + accuracy** |
+| `YOLOV5M`    | 82MB | 95.30% | 93.76% | 85.28% | High accuracy                  |
 
 **Accuracy**: WIDER FACE validation set - from [YOLOv5-Face paper](https://arxiv.org/abs/2105.12931)
 **Speed**: Benchmark on your own hardware using `scripts/run_detection.py --iterations 100`
@@ -95,6 +96,13 @@ YOLOv5-Face models provide excellent detection accuracy with 5-point facial land
 from uniface import YOLOv5Face
 from uniface.constants import YOLOv5FaceWeights
 
+# Lightweight/Mobile
+detector = YOLOv5Face(
+    model_name=YOLOv5FaceWeights.YOLOV5N,
+    conf_thresh=0.6,
+    nms_thresh=0.5
+)
+
 # Real-time detection (recommended)
 detector = YOLOv5Face(
     model_name=YOLOv5FaceWeights.YOLOV5S,
@@ -251,9 +259,9 @@ landmarks = landmarker.get_landmarks(image, bbox)
 from uniface import AgeGender
 
 predictor = AgeGender()
-gender_id, age = predictor.predict(image, bbox)
-# Returns: (gender_id, age_in_years)
-# gender_id: 0 for Female, 1 for Male
+gender, age = predictor.predict(image, bbox)
+# Returns: (gender, age_in_years)
+# gender: 0 for Female, 1 for Male
 ```
 
 ---

QUICKSTART.md

@@ -199,9 +199,9 @@ faces = detector.detect(image)
 
 # Predict attributes
 for i, face in enumerate(faces):
-    gender_id, age = age_gender.predict(image, face['bbox'])
-    gender = 'Female' if gender_id == 0 else 'Male'
-    print(f"Face {i+1}: {gender}, {age} years old")
+    gender, age = age_gender.predict(image, face['bbox'])
+    gender_str = 'Female' if gender == 0 else 'Male'
+    print(f"Face {i+1}: {gender_str}, {age} years old")
 ```
 
 **Output:**

README.md

@@ -147,9 +147,9 @@ detector = RetinaFace()
 age_gender = AgeGender()
 
 faces = detector.detect(image)
-gender_id, age = age_gender.predict(image, faces[0]['bbox'])
-gender = 'Female' if gender_id == 0 else 'Male'
-print(f"{gender}, {age} years old")
+gender, age = age_gender.predict(image, faces[0]['bbox'])
+gender_str = 'Female' if gender == 0 else 'Male'
+print(f"{gender_str}, {age} years old")
 ```
 
 ---
@@ -171,15 +171,18 @@ from uniface.detection import RetinaFace, SCRFD
 from uniface.recognition import ArcFace
 from uniface.landmark import Landmark106
 
+from uniface.constants import SCRFDWeights
+
 # Create detector with default settings
 detector = RetinaFace()
 
 # Create with custom config
 detector = SCRFD(
-    model_name='scrfd_10g_kps',
-    conf_thresh=0.8,
+    model_name=SCRFDWeights.SCRFD_10G_KPS, # SCRFDWeights.SCRFD_500M_KPS
+    conf_thresh=0.4,
     input_size=(640, 640)
 )
+# Or with defaults settings: detector = SCRFD()
 
 # Recognition and landmarks
 recognizer = ArcFace()
@@ -198,6 +201,7 @@ detector = RetinaFace(
     conf_thresh=0.5,
     nms_thresh=0.4
 )
+# Or detector = RetinaFace()
 
 # YOLOv5-Face detection
 detector = YOLOv5Face(
@@ -205,6 +209,7 @@ detector = YOLOv5Face(
     conf_thresh=0.6,
     nms_thresh=0.5
 )
+# Or detector = YOLOv5Face
 
 # Recognition
 recognizer = ArcFace()  # Uses default weights
@@ -229,7 +234,7 @@ faces = detect_faces(image, method='retinaface', conf_thresh=0.8)  # methods: re
 | -------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------- |
 | `RetinaFace` | `model_name=RetinaFaceWeights.MNET_V2`, `conf_thresh=0.5`, `nms_thresh=0.4`, `input_size=(640, 640)`, `dynamic_size=False` | Supports 5-point landmarks                     |
 | `SCRFD`      | `model_name=SCRFDWeights.SCRFD_10G_KPS`, `conf_thresh=0.5`, `nms_thresh=0.4`, `input_size=(640, 640)`                        | Supports 5-point landmarks                     |
-| `YOLOv5Face` | `model_name=YOLOv5FaceWeights.YOLOV5S`, `conf_thresh=0.6`, `nms_thresh=0.5`, `input_size=640` (fixed)                        | Landmarks supported;`input_size` must be 640 |
+| `YOLOv5Face` | `model_name=YOLOv5FaceWeights.YOLOV5S`, `conf_thresh=0.6`, `nms_thresh=0.5`, `input_size=640` (fixed)                        | Supports 5-point landmarks; models: YOLOV5N/S/M; `input_size` must be 640 |
 
 **Recognition**
 
@@ -260,6 +265,7 @@ faces = detect_faces(image, method='retinaface', conf_thresh=0.8)  # methods: re
 | retinaface_r34     | 94.16% | 93.12% | 88.90% | High accuracy          |
 | scrfd_500m         | 90.57% | 88.12% | 68.51% | Real-time applications |
 | scrfd_10g          | 95.16% | 93.87% | 83.05% | Best accuracy/speed    |
+| yolov5n_face       | 93.61% | 91.52% | 80.53% | Lightweight/Mobile     |
 | yolov5s_face       | 94.33% | 92.61% | 83.15% | Real-time + accuracy   |
 | yolov5m_face       | 95.30% | 93.76% | 85.28% | High accuracy          |
 

examples/face_analyzer.ipynb

@@ -13,9 +13,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install -q uniface"
    ]
@@ -29,14 +37,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.3.0\n"
+      "1.3.1\n"
      ]
     }
    ],
@@ -65,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -95,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -136,7 +144,7 @@
     "\n",
     "    # Print face attributes\n",
     "    for i, face in enumerate(faces, 1):\n",
-    "        print(f'  Face {i}: {face.gender}, {face.age}y')\n",
+    "        print(f'  Face {i}: {face.sex}, {face.age}y')\n",
     "\n",
     "    # Prepare visualization (without text overlay)\n",
     "    vis_image = image.copy()\n",
@@ -159,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -186,7 +194,7 @@
     "    axes[1, idx].axis('off')\n",
     "    info_text = f'{len(faces)} face(s)\\n'\n",
     "    for i, face in enumerate(faces, 1):\n",
-    "        info_text += f'Face {i}: {face.gender}, {face.age}y\\n'\n",
+    "        info_text += f'Face {i}: {face.sex}, {face.age}y\\n'\n",
     "\n",
     "    axes[1, idx].text(0.5, 0.5, info_text,\n",
     "                      ha='center', va='center',\n",
@@ -207,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -236,7 +244,7 @@
     "    print(f'  - Confidence: {face.confidence:.3f}')\n",
     "    print(f'  - Landmarks shape: {face.landmarks.shape}')\n",
     "    print(f'  - Age: {face.age} years')\n",
-    "    print(f'  - Gender: {face.gender}')\n",
+    "    print(f'  - Gender: {face.sex}')\n",
     "    print(f'  - Embedding shape: {face.embedding.shape}')\n",
     "    print(f'  - Embedding dimension: {face.embedding.shape[1]}D')"
    ]
@@ -252,14 +260,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Similarity between faces: 0.1201\n",
+      "Similarity between faces: 0.1135\n",
       "Same person: No (threshold=0.6)\n"
      ]
     }
@@ -283,7 +291,7 @@
     "\n",
     "- `analyzer.analyze()` performs detection, recognition, and attribute prediction in one call\n",
     "- Each `Face` object contains: `bbox`, `confidence`, `landmarks`, `embedding`, `age`, `gender`\n",
-    "- Gender is available as both ID (0=Female, 1=Male) and string via `face.gender` property\n",
+    "- Gender is available as both ID (0=Female, 1=Male) and string via `face.sex` property\n",
     "- Face embeddings are L2-normalized (norm ≈ 1.0) for similarity computation\n",
     "- Use `face.compute_similarity(other_face)` to compare faces (returns cosine similarity)\n",
     "- Typical similarity threshold: 0.6 (same person if similarity > 0.6)"
@@ -297,7 +305,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -311,7 +319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,

examples/face_detection.ipynb

@@ -13,9 +13,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install -q uniface"
    ]
@@ -29,14 +37,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.3.0\n"
+      "1.3.1\n"
      ]
     }
    ],
@@ -61,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -99,7 +107,7 @@
        "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x624>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -175,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {

examples/face_verification.ipynb

@@ -11,15 +11,6 @@
     "## 1. Install UniFace"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -q uniface"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -29,7 +20,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.3.0\n"
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -q uniface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.3.1\n"
      ]
     }
    ],
@@ -56,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -72,12 +80,12 @@
     "analyzer = FaceAnalyzer(\n",
     "    detector=RetinaFace(conf_thresh=0.5),\n",
     "    recognizer=ArcFace()\n",
-    ")\n"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -99,12 +107,12 @@
     "faces1 = analyzer.analyze(image1)\n",
     "faces2 = analyzer.analyze(image2)\n",
     "\n",
-    "print(f'Detected {len(faces1)} and {len(faces2)} faces')\n"
+    "print(f'Detected {len(faces1)} and {len(faces2)} faces')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -130,31 +138,7 @@
     "axes[1].axis('off')\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Similarity: 0.1201\n"
-     ]
-    }
-   ],
-   "source": [
-    "if faces1 and faces2:\n",
-    "    face1 = faces1[0]\n",
-    "    face2 = faces2[0]\n",
-    "\n",
-    "    similarity = face1.compute_similarity(face2)\n",
-    "    print(f'Similarity: {similarity:.4f}')\n",
-    "else:\n",
-    "    print('Error: Could not detect faces')\n"
+    "plt.show()"
    ]
   },
   {
@@ -166,7 +150,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Similarity: 0.1201\n",
+      "Similarity: 0.1135\n"
+     ]
+    }
+   ],
+   "source": [
+    "if faces1 and faces2:\n",
+    "    face1 = faces1[0]\n",
+    "    face2 = faces2[0]\n",
+    "\n",
+    "    similarity = face1.compute_similarity(face2)\n",
+    "    print(f'Similarity: {similarity:.4f}')\n",
+    "else:\n",
+    "    print('Error: Could not detect faces')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Similarity: 0.1135\n",
       "Threshold: 0.6\n",
       "Result: Different people\n"
      ]
@@ -180,12 +188,12 @@
     "\n",
     "    print(f'Similarity: {similarity:.4f}')\n",
     "    print(f'Threshold: {THRESHOLD}')\n",
-    "    print(f'Result: {\"Same person\" if is_same_person else \"Different people\"}')\n"
+    "    print(f'Result: {\"Same person\" if is_same_person else \"Different people\"}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -193,9 +201,9 @@
      "output_type": "stream",
      "text": [
       "Comparing multiple pairs:\n",
-      "image0.jpg vs image1.jpg: 0.1201\n",
-      "image0.jpg vs image2.jpg: 0.0951\n",
-      "image1.jpg vs image2.jpg: -0.0047\n"
+      "image0.jpg vs image1.jpg: 0.1135\n",
+      "image0.jpg vs image2.jpg: 0.0833\n",
+      "image1.jpg vs image2.jpg: -0.0082\n"
      ]
     }
    ],
@@ -220,7 +228,7 @@
     "        img1_name = img1_path.split('/')[-1]\n",
     "        img2_name = img2_path.split('/')[-1]\n",
     "\n",
-    "        print(f'{img1_name} vs {img2_name}: {sim:.4f}')\n"
+    "        print(f'{img1_name} vs {img2_name}: {sim:.4f}')"
    ]
   },
   {

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "uniface"
-version = "1.3.0"
+version = "1.3.2"
 description = "UniFace: A Comprehensive Library for Face Detection, Recognition, Landmark Analysis, Age, and Gender Detection"
 readme = "README.md"
 license = { text = "MIT" }

scripts/batch_process.py

@@ -31,7 +31,9 @@ def process_image(detector, image_path: Path, output_path: Path, threshold: floa
     bboxes = [f['bbox'] for f in faces]
     scores = [f['confidence'] for f in faces]
     landmarks = [f['landmarks'] for f in faces]
-    draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)
+    draw_detections(
+        image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True
+    )
 
     cv2.putText(
         image,

scripts/run_age_gender.py

@@ -43,7 +43,9 @@ def process_image(
     bboxes = [f['bbox'] for f in faces]
     scores = [f['confidence'] for f in faces]
     landmarks = [f['landmarks'] for f in faces]
-    draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)
+    draw_detections(
+        image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True
+    )
 
     for i, face in enumerate(faces):
         gender_id, age = age_gender.predict(image, face['bbox'])

scripts/run_detection.py

@@ -51,7 +51,15 @@ def run_webcam(detector, threshold: float = 0.6):
         bboxes = [f['bbox'] for f in faces]
         scores = [f['confidence'] for f in faces]
         landmarks = [f['landmarks'] for f in faces]
-        draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold, draw_score=True, fancy_bbox=True)
+        draw_detections(
+            image=frame,
+            bboxes=bboxes,
+            scores=scores,
+            landmarks=landmarks,
+            vis_threshold=threshold,
+            draw_score=True,
+            fancy_bbox=True,
+        )
 
         cv2.putText(
             frame,
@@ -90,7 +98,7 @@ def main():
     else:
         from uniface.constants import YOLOv5FaceWeights
 
-        detector = YOLOv5Face(model_name=YOLOv5FaceWeights.YOLOV5M)
+        detector = YOLOv5Face(model_name=YOLOv5FaceWeights.YOLOV5N)
 
     if args.webcam:
         run_webcam(detector, args.threshold)

scripts/run_emotion.py

@@ -42,7 +42,9 @@ def process_image(
     bboxes = [f['bbox'] for f in faces]
     scores = [f['confidence'] for f in faces]
     landmarks = [f['landmarks'] for f in faces]
-    draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)
+    draw_detections(
+        image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True
+    )
 
     for i, face in enumerate(faces):
         emotion, confidence = emotion_predictor.predict(image, face['landmarks'])

scripts/run_face_analyzer.py

@@ -16,8 +16,8 @@ def draw_face_info(image, face, face_id):
     """Draw face ID and attributes above bounding box."""
     x1, y1, x2, y2 = map(int, face.bbox)
     lines = [f'ID: {face_id}', f'Conf: {face.confidence:.2f}']
-    if face.age and face.gender:
-        lines.append(f'{face.gender}, {face.age}y')
+    if face.age and face.sex:
+        lines.append(f'{face.sex}, {face.age}y')
 
     for i, line in enumerate(lines):
         y_pos = y1 - 10 - (len(lines) - 1 - i) * 25
@@ -41,7 +41,7 @@ def process_image(analyzer, image_path: str, save_dir: str = 'outputs', show_sim
         return
 
     for i, face in enumerate(faces, 1):
-        info = f'  Face {i}: {face.gender}, {face.age}y' if face.age and face.gender else f'  Face {i}'
+        info = f'  Face {i}: {face.sex}, {face.age}y' if face.age and face.sex else f'  Face {i}'
         if face.embedding is not None:
             info += f' (embedding: {face.embedding.shape})'
         print(info)
@@ -82,7 +82,7 @@ def process_image(analyzer, image_path: str, save_dir: str = 'outputs', show_sim
     bboxes = [f.bbox for f in faces]
     scores = [f.confidence for f in faces]
     landmarks = [f.landmarks for f in faces]
-    draw_detections(image, bboxes, scores, landmarks)
+    draw_detections(image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, fancy_bbox=True)
 
     for i, face in enumerate(faces, 1):
         draw_face_info(image, face, i)

scripts/run_video_detection.py

@@ -55,7 +55,9 @@ def process_video(
         bboxes = [f['bbox'] for f in faces]
         scores = [f['confidence'] for f in faces]
         landmarks = [f['landmarks'] for f in faces]
-        draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold)
+        draw_detections(
+            image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True
+        )
 
         cv2.putText(
             frame,

uniface/__init__.py

@@ -13,7 +13,7 @@
 
 __license__ = 'MIT'
 __author__ = 'Yakhyokhuja Valikhujaev'
-__version__ = '1.3.0'
+__version__ = '1.3.2'
 
 
 from uniface.face_utils import compute_similarity, face_alignment

uniface/analyzer.py

@@ -53,12 +53,11 @@ class FaceAnalyzer:
                 except Exception as e:
                     Logger.warning(f'  Face {idx + 1}: Failed to extract embedding: {e}')
 
-            age, gender_id = None, None
+            age, gender = None, None
             if self.age_gender is not None:
                 try:
-                    gender_id, age = self.age_gender.predict(image, bbox)
-                    gender_str = 'Female' if gender_id == 0 else 'Male'
-                    Logger.debug(f'  Face {idx + 1}: Age={age}, Gender={gender_str}')
+                    gender, age = self.age_gender.predict(image, bbox)
+                    Logger.debug(f'  Face {idx + 1}: Age={age}, Gender={gender}')
                 except Exception as e:
                     Logger.warning(f'  Face {idx + 1}: Failed to predict age/gender: {e}')
 
@@ -68,7 +67,7 @@ class FaceAnalyzer:
                 landmarks=landmarks,
                 embedding=embedding,
                 age=age,
-                gender_id=gender_id,
+                gender=gender,
             )
             faces.append(face)
 

uniface/constants.py

@@ -62,11 +62,13 @@ class YOLOv5FaceWeights(str, Enum):
     Exported to ONNX from: https://github.com/yakhyo/yolov5-face-onnx-inference
 
     Model Performance (WIDER FACE):
-    - YOLOV5S: 7.1M params, 28MB, 94.33% Easy / 92.61% Medium / 83.15% Hard
-    - YOLOV5M: 21.1M params, 84MB, 95.30% Easy / 93.76% Medium / 85.28% Hard
+    - YOLOV5N: 11MB, 93.61% Easy / 91.52% Medium / 80.53% Hard
+    - YOLOV5S: 28MB, 94.33% Easy / 92.61% Medium / 83.15% Hard
+    - YOLOV5M: 82MB, 95.30% Easy / 93.76% Medium / 85.28% Hard
     """
-    YOLOV5S = "yolov5s_face"
-    YOLOV5M = "yolov5m_face"
+    YOLOV5N = "yolov5n"
+    YOLOV5S = "yolov5s"
+    YOLOV5M = "yolov5m"
 
 
 class DDAMFNWeights(str, Enum):
@@ -117,6 +119,7 @@ MODEL_URLS: Dict[Enum, str] = {
     SCRFDWeights.SCRFD_10G_KPS:      'https://github.com/yakhyo/uniface/releases/download/weights/scrfd_10g_kps.onnx',
     SCRFDWeights.SCRFD_500M_KPS:     'https://github.com/yakhyo/uniface/releases/download/weights/scrfd_500m_kps.onnx',
     # YOLOv5-Face
+    YOLOv5FaceWeights.YOLOV5N:       'https://github.com/yakhyo/yolov5-face-onnx-inference/releases/download/weights/yolov5n_face.onnx',
     YOLOv5FaceWeights.YOLOV5S:       'https://github.com/yakhyo/yolov5-face-onnx-inference/releases/download/weights/yolov5s_face.onnx',
     YOLOv5FaceWeights.YOLOV5M:       'https://github.com/yakhyo/yolov5-face-onnx-inference/releases/download/weights/yolov5m_face.onnx',
     # DDAFM
@@ -151,6 +154,7 @@ MODEL_SHA256: Dict[Enum, str] = {
     SCRFDWeights.SCRFD_10G_KPS:      '5838f7fe053675b1c7a08b633df49e7af5495cee0493c7dcf6697200b85b5b91',
     SCRFDWeights.SCRFD_500M_KPS:     '5e4447f50245bbd7966bd6c0fa52938c61474a04ec7def48753668a9d8b4ea3a',
     # YOLOv5-Face
+    YOLOv5FaceWeights.YOLOV5N:       'eb244a06e36999db732b317c2b30fa113cd6cfc1a397eaf738f2d6f33c01f640',
     YOLOv5FaceWeights.YOLOV5S:       'fc682801cd5880e1e296184a14aea0035486b5146ec1a1389d2e7149cb134bb2',
     YOLOv5FaceWeights.YOLOV5M:       '04302ce27a15bde3e20945691b688e2dd018a10e92dd8932146bede6a49207b2',
     # DDAFM

uniface/detection/retinaface.py

@@ -230,9 +230,9 @@ class RetinaFace(BaseDetector):
         faces = []
         for i in range(detections.shape[0]):
             face_dict = {
-                'bbox': detections[i, :4].astype(np.float32),
+                'bbox': detections[i, :4],
                 'confidence': float(detections[i, 4]),
-                'landmarks': landmarks[i].astype(np.float32),
+                'landmarks': landmarks[i],
             }
             faces.append(face_dict)
 
@@ -293,7 +293,7 @@ class RetinaFace(BaseDetector):
             landmarks[: self.post_nms_topk],
         )
 
-        landmarks = landmarks.reshape(-1, 5, 2).astype(np.int32)
+        landmarks = landmarks.reshape(-1, 5, 2).astype(np.float32)
 
         return detections, landmarks
 

uniface/detection/scrfd.py

@@ -251,7 +251,7 @@ class SCRFD(BaseDetector):
 
         detections = pre_det[keep, :]
         landmarks = landmarks[order, :, :]
-        landmarks = landmarks[keep, :, :].astype(np.int32)
+        landmarks = landmarks[keep, :, :].astype(np.float32)
 
         if 0 < max_num < detections.shape[0]:
             # Calculate area of detections
@@ -281,9 +281,9 @@ class SCRFD(BaseDetector):
         faces = []
         for i in range(detections.shape[0]):
             face_dict = {
-                'bbox': detections[i, :4].astype(np.float32),
+                'bbox': detections[i, :4],
                 'confidence': float(detections[i, 4]),
-                'landmarks': landmarks[i].astype(np.float32),
+                'landmarks': landmarks[i],
             }
             faces.append(face_dict)
 

uniface/detection/yolov5.py

@@ -331,9 +331,9 @@ class YOLOv5Face(BaseDetector):
         faces = []
         for i in range(detections.shape[0]):
             face_dict = {
-                'bbox': detections[i, :4].astype(np.float32),
+                'bbox': detections[i, :4],
                 'confidence': float(detections[i, 4]),
-                'landmarks': landmarks[i].astype(np.float32),
+                'landmarks': landmarks[i],
             }
             faces.append(face_dict)
 

uniface/face.py

@@ -14,14 +14,19 @@ __all__ = ['Face']
 
 @dataclass
 class Face:
-    """Detected face with analysis results."""
+    """
+    Detected face with analysis results.
+    """
 
+    # Required attributes
     bbox: np.ndarray
     confidence: float
     landmarks: np.ndarray
+
+    # Optional attributes
     embedding: Optional[np.ndarray] = None
     age: Optional[int] = None
-    gender_id: Optional[int] = None  # 0: Female, 1: Male
+    gender: Optional[int] = None  # 0 or 1
 
     def compute_similarity(self, other: 'Face') -> float:
         """Compute cosine similarity with another face."""
@@ -34,18 +39,28 @@ class Face:
         return asdict(self)
 
     @property
-    def gender(self) -> str:
+    def sex(self) -> str:
         """Get gender as a string label (Female or Male)."""
-        if self.gender_id is None:
+        if self.gender is None:
             return None
-        return 'Female' if self.gender_id == 0 else 'Male'
+        return 'Female' if self.gender == 0 else 'Male'
+
+    @property
+    def bbox_xyxy(self) -> np.ndarray:
+        """Get bounding box coordinates in (x1, y1, x2, y2) format."""
+        return self.bbox.copy()
+
+    @property
+    def bbox_xywh(self) -> np.ndarray:
+        """Get bounding box coordinates in (x1, y1, w, h) format."""
+        return np.array([self.bbox[0], self.bbox[1], self.bbox[2] - self.bbox[0], self.bbox[3] - self.bbox[1]])
 
     def __repr__(self) -> str:
         parts = [f'Face(confidence={self.confidence:.3f}']
         if self.age is not None:
             parts.append(f'age={self.age}')
-        if self.gender_id is not None:
-            parts.append(f'gender={self.gender}')
+        if self.gender is not None:
+            parts.append(f'sex={self.sex}')
         if self.embedding is not None:
             parts.append(f'embedding_dim={self.embedding.shape[0]}')
         return ', '.join(parts) + ')'