Upload 3 files

Update YOLOv8 to YOLOv11

detection_model.py

@@ -1,3 +1,4 @@
+# %%writefile detection_model.py
 from ultralytics import YOLO
 from typing import Any, List, Dict, Optional
 import torch
@@ -5,36 +6,36 @@ import numpy as np
 import os
 
 class DetectionModel:
-    """Core detection model class for object detection using YOLOv8"""
+    """Core detection model class for object detection using YOLOv11"""
 
     # Model information dictionary
     MODEL_INFO = {
-        "yolov8n.pt": {
-            "name": "YOLOv8n (Nano)",
-            "description": "Fastest model with smallest size (3.2M parameters). Best for speed-critical applications.",
-            "size_mb": 6,
+        "yolo11n.pt": {
+            "name": "YOLOv11n (Nano)",
+            "description": "Fastest model with smallest size (2.6M parameters). Best for speed-critical applications.",
+            "size_mb": 5,
             "inference_speed": "Very Fast"
         },
-        "yolov8m.pt": {
-            "name": "YOLOv8m (Medium)",
-            "description": "Balanced model with good accuracy-speed tradeoff (25.9M parameters). Recommended for general use.",
-            "size_mb": 25,
+        "yolo11m.pt": {
+            "name": "YOLOv11m (Medium)",
+            "description": "Balanced model with good accuracy-speed tradeoff (20.1M parameters). Recommended for general use.",
+            "size_mb": 20,
             "inference_speed": "Medium"
         },
-        "yolov8x.pt": {
-            "name": "YOLOv8x (XLarge)",
-            "description": "Most accurate but slower model (68.2M parameters). Best for accuracy-critical applications.",
-            "size_mb": 68,
+        "yolo11x.pt": {
+            "name": "YOLOv11x (XLarge)",
+            "description": "Most accurate but slower model (56.9M parameters). Best for accuracy-critical applications.",
+            "size_mb": 56,
             "inference_speed": "Slower"
         }
     }
 
-    def __init__(self, model_name: str = 'yolov8m.pt', confidence: float = 0.25, iou: float = 0.25):
+    def __init__(self, model_name: str = 'yolo11m.pt', confidence: float = 0.25, iou: float = 0.25):
         """
         Initialize the detection model
 
         Args:
-            model_name: Model name or path, default is yolov8m.pt
+            model_name: Model name or path, default is yolo11m.pt
             confidence: Confidence threshold, default is 0.25
             iou: IoU threshold for non-maximum suppression, default is 0.45
         """

requirements.txt

@@ -1,6 +1,6 @@
 torch>=2.0.0
 torchvision>=0.15.0
-ultralytics>=8.0.0
+ultralytics>=8.3.0
 opencv-python>=4.7.0
 pillow>=9.4.0
 numpy>=1.23.5

ui_manager.py

@@ -1,3 +1,4 @@
+# %%writefile ui_manager.py
 import gradio as gr
 from typing import Dict, List, Any, Optional, Tuple
 import matplotlib.pyplot as plt
@@ -25,7 +26,7 @@ class UIManager:
             self.model_choices = [model["model_file"] for model in self.available_models]
         except ImportError:
             # Fallback model choices if DetectionModel is not available
-            self.model_choices = ["yolov8n.pt", "yolov8s.pt", "yolov8m.pt", "yolov8l.pt", "yolov8x.pt"]
+            self.model_choices = ["yolo11n.pt", "yolo11s.pt", "yolo11m.pt", "yolo11l.pt", "yolo11x.pt"]
 
         # Setup class choices
         self.class_choices_formatted = [f"{id}: {name}" for id, name in self.get_all_classes()]
@@ -171,7 +172,7 @@ class UIManager:
         return gr.HTML("""
             <div class="footer" style="padding: 25px 0; text-align: center; background: linear-gradient(to right, #f5f9fc, #e1f5fe); border-top: 1px solid #e2e8f0; margin-top: 30px;">
                 <div style="margin-bottom: 15px;">
-                    <p style="font-size: 14px; color: #4A5568; margin: 5px 0;">Powered by YOLOv8, CLIP, Places365, Meta Llama3.2 and Ultralytics • Enhanced Video Processing with Temporal Analysis • Created with Gradio</p>
+                    <p style="font-size: 14px; color: #4A5568; margin: 5px 0;">Powered by YOLOv11, CLIP, Places365, Meta Llama3.2 and Ultralytics • Enhanced Video Processing with Temporal Analysis • Created with Gradio</p>
                 </div>
                 <div style="display: flex; align-items: center; justify-content: center; gap: 20px; margin-top: 15px;">
                     <p style="font-family: 'Arial', sans-serif; font-size: 14px; font-weight: 500; letter-spacing: 2px; background: linear-gradient(90deg, #38b2ac, #4299e1); -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin: 0; text-transform: uppercase; display: inline-block;">EXPLORE THE CODE →</p>
@@ -192,7 +193,7 @@ class UIManager:
         components = {}
 
         with gr.Tab("Image Processing"):
-            components['current_image_model'] = gr.State("yolov8m.pt")
+            components['current_image_model'] = gr.State("yolo11m.pt")
 
             with gr.Row(equal_height=False):
                 # Left Column: Image Input & Controls
@@ -208,13 +209,13 @@ class UIManager:
                         with gr.Accordion("Image Analysis Settings", open=False):
                             components['image_model_dropdown'] = gr.Dropdown(
                                 choices=self.model_choices,
-                                value="yolov8m.pt",
+                                value="yolo11m.pt",
                                 label="Select Model",
                                 info="Choose speed vs. accuracy (n=fast, m=balanced, x=accurate)"
                             )
 
                             components['image_model_info'] = gr.Markdown(
-                                self.get_model_description("yolov8m.pt")
+                                self.get_model_description("yolo11m.pt")
                             )
 
                             components['image_confidence'] = gr.Slider(
@@ -451,7 +452,7 @@ class UIManager:
                         with gr.Accordion("Video Analysis Settings", open=True):
                             components['video_model_dropdown'] = gr.Dropdown(
                                 choices=self.model_choices,
-                                value="yolov8n.pt",
+                                value="yolo11n.pt",
                                 label="Select Model (Video)",
                                 info="Faster models (like 'n') are recommended for video processing"
                             )
@@ -491,7 +492,7 @@ class UIManager:
                         gr.Markdown("""
                         1. Choose your input method: Upload a file or enter a URL.
                         2. Adjust settings if needed:
-                            * Use **faster models** (yolov8n) for quicker processing
+                            * Use **faster models** (yolo11n) for quicker processing
                             * Set **larger intervals** (15+ frames) for longer videos
                             * Adjust **confidence threshold** to filter low-quality detections
                         3. Click "Analyze Video". **Processing time varies based on video length.**
@@ -499,7 +500,7 @@ class UIManager:
 
                         **⚡ Performance Tips:**
                         * For videos longer than 2 minutes, use interval ≥ 15 frames
-                        * YOLOv8n model provides best speed for video processing
+                        * YOLOv11n model provides best speed for video processing
                         * Higher confidence thresholds reduce processing noise
                         """)