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@@ -3,6 +3,9 @@ license: apache-2.0
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  datasets:
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  - DamarJati/Face-Mask-Detection
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  ---
 
 
 
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  ```py
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  Classification Report:
@@ -17,3 +20,77 @@ Face_Mask Not_Found 0.9568 0.9667 0.9617 5909
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  ```
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  ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/sHVD-hwWEVZT2lQMmlq9_.png)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  datasets:
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  - DamarJati/Face-Mask-Detection
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  ---
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+ # **Face-Mask-Detection**
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+
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+ > **Face-Mask-Detection** is a binary image classification model based on `google/siglip2-base-patch16-224`, trained to detect whether a person is **wearing a face mask** or **not**. This model can be used in **public health monitoring**, **access control systems**, and **workplace compliance enforcement**.
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  ```py
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  Classification Report:
 
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  ```
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  ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/sHVD-hwWEVZT2lQMmlq9_.png)
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+
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+ ---
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+
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+ ## **Label Classes**
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+
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+ The model distinguishes between the following face mask statuses:
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+
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+ ```
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+ 0: Face_Mask Found
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+ 1: Face_Mask Not_Found
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+ ```
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+
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+ ---
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+
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+ ## **Installation**
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+
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+ ```bash
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+ pip install transformers torch pillow gradio
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+ ```
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+
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+ ---
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+
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+ ## **Example Inference Code**
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+
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+ ```python
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+ import gradio as gr
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+ from transformers import AutoImageProcessor, SiglipForImageClassification
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+ from PIL import Image
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+ import torch
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+
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+ # Load model and processor
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+ model_name = "prithivMLmods/Face-Mask-Detection"
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+ model = SiglipForImageClassification.from_pretrained(model_name)
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+ processor = AutoImageProcessor.from_pretrained(model_name)
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+
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+ # ID to label mapping
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+ id2label = {
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+ "0": "Face_Mask Found",
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+ "1": "Face_Mask Not_Found"
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+ }
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+
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+ def detect_face_mask(image):
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+ image = Image.fromarray(image).convert("RGB")
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+ inputs = processor(images=image, return_tensors="pt")
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+
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+ with torch.no_grad():
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+ outputs = model(**inputs)
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+ logits = outputs.logits
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+ probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
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+
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+ prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
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+ return prediction
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+
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+ # Gradio Interface
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+ iface = gr.Interface(
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+ fn=detect_face_mask,
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+ inputs=gr.Image(type="numpy"),
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+ outputs=gr.Label(num_top_classes=2, label="Mask Status"),
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+ title="Face-Mask-Detection",
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+ description="Upload an image to check if a person is wearing a face mask or not."
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+ )
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+
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+ if __name__ == "__main__":
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+ iface.launch()
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+ ```
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+
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+ ---
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+
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+ ## **Applications**
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+
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+ * **COVID-19 Compliance Monitoring**
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+ * **Security and Access Control**
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+ * **Automated Surveillance Systems**
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+ * **Health Safety Enforcement in Public Spaces**