Complete Code

.gitattributes

@@ -0,0 +1,3 @@
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text

.gitignore

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+/dataset
+/venv
+rename.py
+model_training.py
+correction.py
+dataset.zip
+
+*.pt

README.md

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+
+# 🍽️ Utensils Object Detection System
+
+Welcome to **Utensils Object Detection System** — an end-to-end pipeline that detects Utensils items like plates, glasses, spoons, and forkss using a custom-trained deep learning model.
+
+This project was built **from scratch** (no Roboflow or auto-annotation tools!) and demonstrates a full lifecycle: dataset creation, model training, performance evaluation, and an interactive demo app.
+
+---
+
+## 🏗️ Project Overview
+
+We set out to solve a real-world problem:
+> _“Can we reliably detect common Utensils items in images, videos, or real-time webcam streams using only a small, custom-labeled dataset?”_
+
+To achieve this, we:
+✅ Collected & annotated a custom dataset (100–500 images)
+✅ Built a clean Python codebase to handle training, inference, and deployment
+✅ Delivered an interactive demo using **Streamlit / Flask**
+
+---
+
+## 📁 Project Structure
+
+```
+├── app/                # Streamlit or Flask app for demo
+│   └── app.py
+├── dataset/            # Custom dataset (images + labels)
+│   ├── images/
+│   └── labels/
+├── inference/          # Inference scripts (image, video, webcam)
+│   ├── detect_image.py
+│   ├── detect_video.py
+│   └── detect_webcam.py
+├── runs/detect/        # Training results & saved weights
+│   ├── weights/
+│   ├── results.png
+│   └── Other Metrics ...
+├── training/           # Training pipeline
+│   ├── train.py
+│   └── model_training.ipynb
+├── data.yaml           # Dataset config
+├── requirements.txt    # Python dependencies
+└── README.md           # This file
+```
+
+---
+
+## 🗂️ Dataset
+
+- **Images collected:** Manually photographed or sourced from public domain (Kaggle)
+- **Classes:** Example — plate, fork, spoon, glass
+- **Annotation tool:** [LabelImg](https://github.com/heartexlabs/labelImg)
+- **Format:** YOLO txt labels
+
+---
+
+## 🏋️‍♂️ Model Training
+
+- **Framework:** YOLOv8
+- **Training script:** `training/train.py`
+- **Best checkpoint:** `runs/detect/weights/best.pt`
+- **Metrics logged:** loss curves, mAP, precision, recall, F1
+
+---
+
+## 🔍 Inference & Results
+
+- Run detection on:
+  - Static images → `inference/detect_image.py`
+  - Video files → `inference/detect_video.py`
+  - Real-time webcam → `inference/detect_webcam.py`
+
+- Visual outputs include:
+  - Bounding boxes with class names and confidence
+  - Confusion matrix
+  - Precision-recall, F1 curves
+
+---
+
+## 🌐 Interactive Demo
+
+Launch the demo app:
+```bash
+pip install -r requirements.txt
+streamlit run app/app.py 
+```
+
+Features:
+- Upload image or video and get detections
+- View predicted bounding boxes + class names + confidence scores
+- (Optional) Real-time webcam support
+
+---
+
+## 🚀 Getting Started
+
+1️⃣ Clone the repo:
+```bash
+git clone https://github.com/yourusername/Utensils-object-detection.git
+cd Utensils-object-detection
+```
+
+2️⃣ Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3️⃣ Run training:
+```bash
+python training/train.py --data data.yaml
+```
+
+4️⃣ Try inference:
+```bash
+python inference/detect_image.py --source path/to/image.jpg
+```
+
+5️⃣ Launch app:
+```bash
+streamlit run app/app.py
+```
+Model summary (fused): 92 layers, 25,842,076 parameters, 0 gradients, 78.7 GFLOPs
+                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:02<00:00,  1.48it/s]
+                   all         40         40      0.681      0.725      0.731      0.468
+                  fork         10         10      0.338        0.2      0.265      0.113
+                 glass         10         10      0.643        0.9      0.888      0.432
+                 plate         10         10          1          1      0.995      0.833
+                 spoon         10         10      0.744        0.8      0.776      0.496
+---
+
+## 📊 Performance
+
+| Metric         | Value    |
+|---------------|----------|
+| mAP@0.5       | 78.0%    |
+| mAP@0.5:0.95  | 50.8%    |
+| Precision     | 85.5%    |
+| Recall        | 67.5%    |
+
+> _These numbers are based on our custom dataset; actual results may vary depending on data size and quality._
+
+---
+
+## 💡 Challenges & Learnings
+
+- **Challenge:** Small dataset size → risk of overfitting
+- **Solution:** Data augmentation and careful validation splitting
+- **Challenge:** Labeling errors → noisy annotations
+- **Solution:** Manual re-checking of all labels
+- **Challenge:** Real-time inference speed
+- **Solution:** Optimized image preprocessing pipeline
+
+
+---
+
+## 🛡️ License & Acknowledgments
+
+- Built using open-source tools: [Ultralytics YOLO](https://github.com/ultralytics/yolov5), [Streamlit](https://streamlit.io/)
+- Dataset annotated manually, no pre-annotated sources used
+- No external pre-trained models on non-custom data
+
+---
+
+If you like this project, ⭐ the repo and feel free to contribute!
+Happy detecting! 🍳🍴🥄

app/app.py

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+import streamlit as st
+from PIL import Image
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import tempfile
+import os
+from streamlit.web.bootstrap import run
+
+
+# Disable file watcher
+os.environ["STREAMLIT_SERVER_ENABLE_FILE_WATCHER"] = "false"  # Disable watcher
+os.environ["STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION"] = "false"
+# Configure environment to suppress warnings
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+os.environ["STREAMLIT_WATCHER_TYPE"] = "none"
+
+# Load model with caching
+@st.cache_resource
+def load_model():
+    return YOLO("best50.pt")
+
+# Initialize session state for webcam
+if 'webcam_active' not in st.session_state:
+    st.session_state.webcam_active = False
+
+# App title and layout
+st.title("Object Detection App")
+st.write("Upload an image/video or use your webcam")
+
+# Load model
+model = load_model()
+
+# Create tabs for different input sources
+tab_upload, tab_webcam = st.tabs(["Upload Media", "Webcam"])
+
+def process_image(img):
+    """Process image and return annotated version with results"""
+    img_array = np.array(img)
+    img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+    results = model(img_bgr, conf=0.5, iou=0.4)
+    annotated_img = results[0].plot()
+    return cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB), results
+
+with tab_upload:
+    uploaded_file = st.file_uploader(
+        "Choose an image or video", 
+        type=["jpg", "jpeg", "png", "mp4", "mov"],
+        label_visibility="collapsed"
+    )
+    
+    if uploaded_file:
+        if uploaded_file.type.startswith('image'):
+            # Process image
+            image = Image.open(uploaded_file)
+            annotated_img, results = process_image(image)
+            
+            # Display side by side
+            col1, col2 = st.columns(2)
+            with col1:
+                st.image(image, caption="Original Image", use_container_width=True)
+            with col2:
+                st.image(annotated_img, caption="Detected Objects", use_container_width=True)
+            
+            # Show detection results
+            st.subheader("Detected Objects:")
+            for box in results[0].boxes:
+                class_name = model.names[int(box.cls)]
+                confidence = float(box.conf)
+                if confidence >= 0.5:
+                    st.write(f"- {class_name} (confidence: {confidence:.2f})")
+
+
+        elif uploaded_file.type.startswith('video'):
+            # Process video
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tfile:
+                tfile.write(uploaded_file.read())
+                video_path = tfile.name
+            
+            st.video(video_path)
+            
+            # Process and show output video
+            with st.spinner('Processing video...'):
+                cap = cv2.VideoCapture(video_path)
+                fps = cap.get(cv2.CAP_PROP_FPS)
+                width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                
+                output_path = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name
+                out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+                
+                while cap.isOpened():
+                    ret, frame = cap.read()
+                    if not ret:
+                        break
+                    results = model(frame)
+                    annotated_frame = results[0].plot()
+                    out.write(annotated_frame)
+                
+                cap.release()
+                out.release()
+                
+                st.video(output_path)
+                os.unlink(video_path)
+                os.unlink(output_path)
+
+with tab_webcam:
+    if st.checkbox("Start Webcam", key="webcam_toggle"):
+        st.session_state.webcam_active = True
+        st.write("Click below to stop the webcam")
+        
+        cap = cv2.VideoCapture(0)
+        frame_placeholder = st.empty()
+        stop_button = st.button("Stop Webcam")
+        
+        while cap.isOpened() and st.session_state.webcam_active:
+            ret, frame = cap.read()
+            if not ret or stop_button:
+                st.session_state.webcam_active = False
+                break
+            
+            results = model(frame)
+            annotated_frame = results[0].plot()
+            frame_placeholder.image(
+                cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB),
+                channels="RGB",
+                use_container_width=True
+            )
+            
+            if stop_button:
+                st.session_state.webcam_active = False
+                break
+        
+        cap.release()
+        if stop_button:
+            st.success("Webcam stopped")

data.yaml

@@ -0,0 +1,10 @@
+# kitchen.yaml
+path: dataset
+train: images/train
+val: images/test
+
+names:
+  0: fork
+  1: glass
+  2: plate
+  3: spoon

inference/detect_image.py

@@ -0,0 +1,29 @@
+from ultralytics import YOLO
+import cv2
+import sys
+
+# Check for command-line argument
+if len(sys.argv) < 2:
+    print("Usage: python detect_image.py <image_path>")
+    sys.exit(1)
+
+image_path = sys.argv[1]
+
+# Load trained model
+model = YOLO("runs/detect/weights/best.pt")
+
+# Read image
+frame = cv2.imread(image_path)
+if frame is None:
+    print(f"Error: Could not read image at {image_path}")
+    sys.exit(1)
+
+# Run detection with conf and iou threshold
+results = model(frame, conf=0.5, iou=0.4, imgsz=640, augment=False)
+
+# Plot and save results
+annotated_img = results[0].plot()
+cv2.imwrite("output.jpg", annotated_img)
+cv2.imshow("Detection", annotated_img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

inference/detect_video.py

@@ -0,0 +1,19 @@
+from ultralytics import YOLO
+import cv2
+
+model = YOLO("runs/detect/weights/best.pt")
+video_path = "test_video.mp4"
+
+cap = cv2.VideoCapture(video_path)
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret: break
+
+    results = model(frame, conf=0.5, imgsz=640, augment=False)
+    annotated_frame = results[0].plot()
+
+    cv2.imshow("Video Detection", annotated_frame)
+    if cv2.waitKey(1) == ord('q'): break
+
+cap.release()
+cv2.destroyAllWindows()

inference/detect_webcam.py

@@ -0,0 +1,18 @@
+from ultralytics import YOLO
+import cv2
+
+model = YOLO("runs/detect/weights/best.pt")
+
+cap = cv2.VideoCapture(0)  # 0 = default webcam
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret: break
+
+    results = model(frame, conf=0.5, imgsz=640, augment=False)
+    annotated_frame = results[0].plot()
+
+    cv2.imshow("Webcam Detection", annotated_frame)
+    if cv2.waitKey(1) == ord('q'): break
+
+cap.release()
+cv2.destroyAllWindows()

runs/detect/args.yaml

@@ -0,0 +1,105 @@
+task: detect
+mode: train
+model: yolov8m.pt
+data: data.yaml
+epochs: 45
+time: null
+patience: 20
+batch: 8
+imgsz: 640
+save: true
+save_period: 10
+cache: false
+device: null
+workers: 8
+project: null
+name: detector2
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: true
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: 0.25
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: true
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.001
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+cutmix: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: runs/detect/detector2

runs/detect/results.csv

@@ -0,0 +1,46 @@
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+39,350.613,1.45414,1.34207,1.67427,0.87401,0.675,0.80025,0.49424,1.64481,1.33722,1.73205,8.49262e-05,8.49262e-05,8.49262e-05
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training/train.py

@@ -0,0 +1,21 @@
+from ultralytics import YOLO
+
+#base model
+model = YOLO("yolov8m.pt")  #for better accuracy
+
+
+model.train(
+    data="data.yaml",       
+    epochs=45,                # increased to allow better convergence
+    patience=20,               # early stopping if no val improvement
+    imgsz=640,                 # image size FOR ACCURACY
+    batch=8,                 
+    conf=0.25,                 # initial THRESHOLD
+    name="detector",
+    augment=True,              # enables data augmentation
+    auto_augment='randaugment',# advanced augmentation
+    lr0=0.001,                 # initial learning rate
+    cos_lr=True,               # cosine learning rate schedule (smoother training)
+    save=True,                 
+    save_period=10,            # save weights every 10 epochs
+)