feat: Add yolov5n, update docs and ruff code format

CONTRIBUTING.md

@@ -57,3 +57,4 @@ 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                       |
+| Model Name     | Size | Easy   | Medium | Hard   | Use Case                       |
-| -------------- | ------ | ---- | ------ | ------ | ------ | --------- | ------------------------------ |
+| -------------- | ---- | ------ | ------ | ------ | ------------------------------ |
-| `YOLOV5S` ⭐ | 7.1M   | 28MB | 94.33% | 92.61% | 83.15% | 5.751     | **Real-time + accuracy** |
+| `YOLOV5N`    | 11MB | 93.61% | 91.52% | 80.53% | Lightweight/Mobile             |
-| `YOLOV5M`    | 21.1M  | 84MB | 95.30% | 93.76% | 85.28% | 18.146    | High accuracy                  |
+| `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,

README.md

@@ -234,7 +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**
 
@@ -265,6 +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          |
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "uniface"
-version = "1.3.1"
+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=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True)
+    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=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True)
+    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(image=frame, bboxes=bboxes, scores=scores, landmarks=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=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True)
+    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

@@ -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=image, bboxes=bboxes, scores=scores, landmarks=landmarks,fancy_bbox=True)
+    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(image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True)
+        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__ = '1.3.1'
+__version__ = '1.3.2'
 
 
 from uniface.face_utils import compute_similarity, face_alignment

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
+    - YOLOV5N: 11MB, 93.61% Easy / 91.52% Medium / 80.53% Hard
-    - YOLOV5M: 21.1M params, 84MB, 95.30% Easy / 93.76% Medium / 85.28% 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"
+    YOLOV5N = "yolov5n"
-    YOLOV5M = "yolov5m_face"
+    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/face.py

@@ -17,6 +17,7 @@ class Face:
     """
     Detected face with analysis results.
     """
+
     # Required attributes
     bbox: np.ndarray
     confidence: float