Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+
+
+
+Here's a clean and professional **Hugging Face model card description** for your `AgeRaceGenderNet` model:
+
+---
+
+## 🧠 AgeRaceGenderNet
+<img src="https://cdn-uploads.huggingface.co/production/uploads/663a886250daed366b657df4/7-yshHOeyZ4DzEDsjVuMA.png" alt="AgeRaceGenderNet" width="800"/>
+
+<!-- ![image/png|](https://cdn-uploads.huggingface.co/production/uploads/663a886250daed366b657df4/7-yshHOeyZ4DzEDsjVuMA.png)
+ -->
+**AgeRaceGenderNet** is a lightweight, multi-task face classification model capable of predicting **age**, **gender**, and **race** from facial images. It is built with a **Swin Transformer V2 (Tiny)** backbone and designed for **fast inference** (\~5.94 GFLOPs). 
+
+---
+
+### 📊 Tasks & Outputs
+
+This model simultaneously predicts:
+
+* **\[age]**: Integer from `0` to `116`, representing estimated age.
+* **\[gender]**: `0` for **male**, `1` for **female**.
+* **\[race]**: Integer from `0` to `4` representing:
+
+  * `0`: White
+  * `1`: Black
+  * `2`: Asian
+  * `3`: Indian
+  * `4`: Others (e.g., Hispanic, Latino, Middle Eastern)
+
+---
+
+### Model Architecture
+
+* **Backbone**: [Swin V2 Tiny](https://arxiv.org/abs/2111.09883) (pretrained and fine-tuned)
+* **Head**: Multi-task architecture with dedicated classification heads for each demographic task.
+* **Criterion**: Custom `MultiTaskLoss` function
+* **Total Parameters**: **28.4M**
+  * **Trainable**: 25.7M
+  * **Non-trainable**: 2.7M
+* **Model Size**: \~113.7 MB
+* **Inference Cost**: \~5.94 GFLOPs
+
+---
+
+### Training Dataset
+
+The model is trained on **[UTKFace Dataset](https://susanqq.github.io/UTKFace/)**
+---
+
+
+
+### Usage example
+##### NOTE: The input image is assumed to be cropped and aligned to contain only the face
+```python
+import onnxruntime as ort
+from PIL import Image
+from torchvision import transforms
+import numpy as np
+
+# Load ONNX model
+session = ort.InferenceSession("Swin_V2_T.onnx")
+
+# Get input and output names
+input_name = session.get_inputs()[0].name
+output_names = [out.name for out in session.get_outputs()]
+
+# Preprocessing: Load and transform image
+# NOTE: The input image is assumed to be cropped and aligned to contain only the face
+
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),  # Resize to model input size
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+img = Image.open("path_to_image.jpg").convert("RGB")
+img_tensor = transform(img).unsqueeze(0).numpy()  # Shape: (1, 3, 256, 256)
+
+# Run inference
+age_logits, gender_logits, race_logits = session.run(output_names, {input_name: img_tensor})
+
+# Postprocessing: Get predictions
+age_pred = int(np.argmax(age_logits, axis=1)[0])
+gender_pred = int(np.argmax(gender_logits, axis=1)[0])
+race_pred = int(np.argmax(race_logits, axis=1)[0])
+
+# Convert predictions to labels
+def get_gender_text(gender_idx):
+    return 'Male' if gender_idx == 0 else 'Female'
+
+def get_race_text(race_idx):
+    race_map = {
+        0: 'White',
+        1: 'Black',
+        2: 'Asian',
+        3: 'Indian',
+        4: 'Other'
+    }
+    return race_map.get(race_idx, 'Unknown')
+
+# Display results
+print(f"Predicted Age: {age_pred}")
+print(f"Predicted Gender: {get_gender_text(gender_pred)}")
+print(f"Predicted Race: {get_race_text(race_pred)}")
+
+```
+
+
+
+
+