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.gitattributes

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+confusion-matrix.png filter=lfs diff=lfs merge=lfs -text
+Precision-confidence.png filter=lfs diff=lfs merge=lfs -text
+Precision-recall.png filter=lfs diff=lfs merge=lfs -text
+result.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model:
+- Ultralytics/YOLO11
+pipeline_tag: object-detection
+tags:
+- pytorch
+---
+
+## YOLOv11n-Face-Detection
+
+A lightweight face detection model based on YOLO architecture ([YOLOv11 nano](https://huggingface.co/Ultralytics/YOLO11)), trained for 225 epochs on the WIDERFACE dataset.
+
+It achieves the following results on the evaluation set:
+
+```
+==================== Results ====================
+Easy   Val AP: 0.9420471677096086
+Medium Val AP: 0.9210357271019756
+Hard   Val AP: 0.8099848364072022
+=================================================
+```
+
+YOLO results:
+
+![Yolov11n results](https://huggingface.co/AdamCodd/YOLOv11-face-detection/resolve/main/result.png)
+
+[Confusion matrix](https://huggingface.co/AdamCodd/YOLOv11-face-detection/blob/main/confusion-matrix.png):
+
+[[23577 2878]
+
+[16098 0]]
+
+### Usage
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+model_path = hf_hub_download(repo_id="AdamCodd/YOLOv11n-face-detection", filename="model.pt")
+model = YOLO(model_path)
+
+results = model.predict("/path/to/your/image", save=True) # saves the result in runs/detect/predict
+```
+
+### Limitations
+
+- Performance may vary in extreme lighting conditions
+- Best suited for frontal and slightly angled faces
+- Optimal performance for faces occupying >20 pixels

config.json

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+{
+  "model": {
+    "type": "YOLO11n",
+    "pretrained": true,
+    "num_classes": 1,
+    "input_size": 640
+  },
+  "dataset": {
+    "name": "WIDER FACE",
+    "train": {
+      "images": "WIDER_train/images",
+      "annotations": "WIDER_train/annotations"
+    },
+    "val": {
+      "images": "WIDER_val/images",
+      "annotations": "WIDER_val/annotations"
+    }
+  },
+  "training": {
+    "epochs": 225,
+    "batch_size": 16,
+    "learning_rate": 0.001,
+    "optimizer": "Adam",
+    "momentum": 0.9,
+    "weight_decay": 0.0005,
+    "scheduler": {
+      "type": "StepLR",
+      "step_size": 30,
+      "gamma": 0.1
+    }
+  },
+  "augmentation": {
+    "horizontal_flip": true,
+    "vertical_flip": false,
+    "rotation": 10,
+    "scale": [0.8, 1.2],
+    "shear": 2
+  },
+  "evaluation": {
+    "interval": 1,
+    "metrics": ["mAP", "precision", "recall"]
+  },
+  "save": {
+    "checkpoint_dir": "checkpoints/",
+    "best_model": "model.pth"
+  }
+}

model.onnx

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model.pt

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