Update README.md

README.md

@@ -82,4 +82,144 @@ M9z-SAUF-VEHICULES-AUTORISES     0.9253    0.9817    0.9527       164
                     accuracy                         0.9732      9298
                    macro avg     0.9093    0.8968    0.9009      9298
                 weighted avg     0.9731    0.9732    0.9729      9298
-```
+```
+
+I've updated the document with the new name **Road-Subsigns-Classification** and the corresponding classification labels. Here is the updated documentation and code:
+
+---
+
+# **Road-Subsigns-Classification**  
+
+> **Road-Subsigns-Classification** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify images of road subsigns using the **SiglipForImageClassification** architecture.
+
+The model categorizes road subsigns into 60 classes:  
+- **Class 0:** "M1"  
+- **Class 1:** "M11c1-E"  
+- **Class 2:** "M2"  
+- **Class 3:** "M3a-droite"  
+- **Class 4:** "M3a-gauche"  
+- **Class 5:** "M3b-gauche"  
+- **Class 6:** "M4a"  
+- **Class 7:** "M4b"  
+- **Class 8:** "M4c"  
+- **Class 9:** "M4d1"  
+- **Class 10:** "M4d2"  
+- **Class 11:** "M4f"  
+- **Class 12:** "M4g"  
+- **Class 13:** "M4h"  
+- **Class 14:** "M4u"  
+- **Class 15:** "M4v"  
+- **Class 16:** "M4z1"  
+- **Class 17:** "M4z2"  
+- **Class 18:** "M5-STOP"  
+- **Class 19:** "M6a"  
+- **Class 20:** "M6h"  
+- **Class 21:** "M6i"  
+- **Class 22:** "M6j"  
+- **Class 23:** "M8a"  
+- **Class 24:** "M8b"  
+- **Class 25:** "M8c"  
+- **Class 26:** "M8d"  
+- **Class 27:** "M8e"  
+- **Class 28:** "M8f"  
+- **Class 29:** "M9Z-INTERDIT-HORS-CASES"  
+- **Class 30:** "M9Z-SAUF-BUS"  
+- **Class 31:** "M9Z-SAUF-BUS-SCOLAIRE"  
+- **Class 32:** "M9c"  
+- **Class 33:** "M9d"  
+- **Class 34:** "M9v"  
+- **Class 35:** "M9z"  
+- **Class 36:** "M9z-DES-DEUX-COTES"  
+- **Class 37:** "M9z-ECOLE"  
+- **Class 38:** "M9z-PARKING-PRIVE"  
+- **Class 39:** "M9z-PASSAGE-SURELEVE"  
+- **Class 40:** "M9z-PROPRIETE-PRIVEE"  
+- **Class 41:** "M9z-RAPPEL"  
+- **Class 42:** "M9z-SAUF-CHANTIER"  
+- **Class 43:** "M9z-SAUF-CONVOIS-EXCEPT"  
+- **Class 44:** "M9z-SAUF-CYCLISTES"  
+- **Class 45:** "M9z-SAUF-DESSERTE"  
+- **Class 46:** "M9z-SAUF-LIVRAISONS"  
+- **Class 47:** "M9z-SAUF-POLICE"  
+- **Class 48:** "M9z-SAUF-RIVERAINS"  
+- **Class 49:** "M9z-SAUF-SERVICE"  
+- **Class 50:** "M9z-SAUF-TAXIS"  
+- **Class 51:** "M9z-SAUF-VEHICULES-AGRICOLES"  
+- **Class 52:** "M9z-SAUF-VEHICULES-AUTORISES"  
+- **Class 53:** "M9z-SECOURS"  
+- **Class 54:** "M9z-SIGNAL-AUTO"  
+- **Class 55:** "M9z-SORTIE-POMPIERS"  
+- **Class 56:** "M9z-SORTIE-VEHICULES"  
+- **Class 57:** "M9z-SUR-LE-TROTTOIR"  
+- **Class 58:** "M9z-VERGLAS"  
+- **Class 59:** "zz"  
+
+# **Run with Transformers🤗**  
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```py
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Road-Subsigns-Classification"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+labels = {
+    "0": "M1", "1": "M11c1-E", "2": "M2", "3": "M3a-droite", "4": "M3a-gauche",
+    "5": "M3b-gauche", "6": "M4a", "7": "M4b", "8": "M4c", "9": "M4d1",
+    "10": "M4d2", "11": "M4f", "12": "M4g", "13": "M4h", "14": "M4u",
+    "15": "M4v", "16": "M4z1", "17": "M4z2", "18": "M5-STOP", "19": "M6a",
+    "20": "M6h", "21": "M6i", "22": "M6j", "23": "M8a", "24": "M8b",
+    "25": "M8c", "26": "M8d", "27": "M8e", "28": "M8f", "29": "M9Z-INTERDIT-HORS-CASES",
+    "30": "M9Z-SAUF-BUS", "31": "M9Z-SAUF-BUS-SCOLAIRE", "32": "M9c", "33": "M9d", "34": "M9v",
+    "35": "M9z", "36": "M9z-DES-DEUX-COTES", "37": "M9z-ECOLE", "38": "M9z-PARKING-PRIVE",
+    "39": "M9z-PASSAGE-SURELEVE", "40": "M9z-PROPRIETE-PRIVEE", "41": "M9z-RAPPEL",
+    "42": "M9z-SAUF-CHANTIER", "43": "M9z-SAUF-CONVOIS-EXCEPT", "44": "M9z-SAUF-CYCLISTES",
+    "45": "M9z-SAUF-DESSERTE", "46": "M9z-SAUF-LIVRAISONS", "47": "M9z-SAUF-POLICE",
+    "48": "M9z-SAUF-RIVERAINS", "49": "M9z-SAUF-SERVICE", "50": "M9z-SAUF-TAXIS",
+    "51": "M9z-SAUF-VEHICULES-AGRICOLES", "52": "M9z-SAUF-VEHICULES-AUTORISES", "53": "M9z-SECOURS",
+    "54": "M9z-SIGNAL-AUTO", "55": "M9z-SORTIE-POMPIERS", "56": "M9z-SORTIE-VEHICULES",
+    "57": "M9z-SUR-LE-TROTTOIR", "58": "M9z-VERGLAS", "59": "zz"
+}
+
+def classify_subsign(image):
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+    
+    return {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_subsign,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Road Subsigns Classification",
+    description="Upload an image to predict the road subsign category."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use:**  
+
+The **Road-Subsigns-Classification** model is designed to classify images of road subsigns into 60 categories. Potential use cases include:  
+
+- **Traffic Management:** Assisting in automated monitoring and analysis of road signs.  
+- **Autonomous Vehicles:** Helping vehicles understand road sign information.  
+- **Smart Cities:** Enhancing traffic regulation systems.  
+- **Driver Assistance Systems:** Providing visual cues for safer driving.  
+- **Urban Planning:** Analyzing road sign data for infrastructure improvements.