feat: Add model name signature and several more updates

scripts/README.md

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+### `download_model.py`
+
+# Download all models
+
+```bash
+python scripts/download_model.py
+```
+
+# Download just RESNET18
+
+```bash
+python scripts/download_model.py --model RESNET18
+```
+
+### `run_inference.py`
+```bash
+python scripts/run_inference.py --image assets/test.jpg --model MNET_V2 --iterations 10
+```

scripts/download_model.py

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+import argparse
+from uniface.constants import RetinaFaceWeights
+from uniface.model_store import verify_model_weights
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Download and verify RetinaFace model weights.")
+    parser.add_argument(
+        "--model",
+        type=str,
+        choices=[m.name for m in RetinaFaceWeights],
+        help="Model to download (e.g. MNET_V2). If not specified, all models will be downloaded.",
+    )
+    args = parser.parse_args()
+
+    if args.model:
+        weight = RetinaFaceWeights[args.model]
+        print(f"📥 Downloading model: {weight.value}")
+        verify_model_weights(weight.value)
+    else:
+        print("📥 Downloading all models...")
+        for weight in RetinaFaceWeights:
+            verify_model_weights(weight.value)
+
+    print("✅ All requested weights are ready and verified.")
+
+
+if __name__ == "__main__":
+    main()
+
+

scripts/run_inference.py

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+import os
+import cv2
+import time
+import argparse
+import numpy as np
+
+from uniface import RetinaFace, draw_detections
+from uniface.constants import RetinaFaceWeights
+
+
+def run_inference(model, image_path, vis_threshold=0.6, save_dir="outputs"):
+    """
+    Run face detection on a single image.
+
+    Args:
+        model (RetinaFace): Initialized RetinaFace model.
+        image_path (str): Path to input image.
+        vis_threshold (float): Threshold for drawing detections.
+        save_dir (str): Directory to save output image.
+    """
+    image = cv2.imread(image_path)
+    if image is None:
+        print(f"❌ Error: Failed to load image from '{image_path}'")
+        return
+
+    boxes, landmarks = model.detect(image)
+    draw_detections(image, (boxes, landmarks), vis_threshold)
+
+    os.makedirs(save_dir, exist_ok=True)
+    output_path = os.path.join(save_dir, f"{os.path.splitext(os.path.basename(image_path))[0]}_out.jpg")
+    cv2.imwrite(output_path, image)
+    print(f"✅ Output saved at: {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run RetinaFace inference on an image.")
+    parser.add_argument("--image", type=str, required=True, help="Path to the input image")
+    parser.add_argument("--model", type=str, default="MNET_V2", choices=[m.name for m in RetinaFaceWeights], help="Model variant to use")
+    parser.add_argument("--threshold", type=float, default=0.6, help="Visualization confidence threshold")
+    parser.add_argument("--iterations", type=int, default=1, help="Number of inference runs for benchmarking")
+    parser.add_argument("--save_dir", type=str, default="outputs", help="Directory to save output images")
+
+    args = parser.parse_args()
+
+    model_name = RetinaFaceWeights[args.model]
+    model = RetinaFace(model_name=model_name)
+
+    avg_time = 0
+    for i in range(args.iterations):
+        start = time.time()
+        run_inference(model, args.image, args.threshold, args.save_dir)
+        elapsed = time.time() - start
+        print(f"[{i + 1}/{args.iterations}] ⏱️ Inference time: {elapsed:.4f} seconds")
+        avg_time += elapsed
+
+    if args.iterations > 1:
+        print(f"\n🔥 Average inference time over {args.iterations} runs: {avg_time / args.iterations:.4f} seconds")
+
+
+if __name__ == "__main__":
+    main()