Upload 13 files

models/best.pt

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+size 6235626

models/efficientnetb0_smartvision.h5

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+oid sha256:7a76bbec7da9f14c7898651a464f49e145d353e062d462e5a8d48a6907e00bde
+size 24964704

models/mobilenetv2_smartvision.h5

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models/resnet50_smartvision.h5

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+size 107692984

models/vgg16_smartvision.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5f6321aec279d87554bc18692f19a82383a57c853369974967eca9908e92968
+size 62268384

pages/1_Classification.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+st.set_page_config(page_title="Image Classification", layout="wide")
+st.title("Image Classification")
+
+# ---------------------------------------------------
+# CLASS LABELS (MUST MATCH TRAINING ORDER EXACTLY)
+# ---------------------------------------------------
+CLASS_NAMES = [
+    "person",          # 0
+    "bicycle",         # 1
+    "car",             # 2
+    "motorcycle",      # 3
+    "airplane",        # 4
+    "bus",             # 5
+    "train",           # 6
+    "truck",           # 7
+    "traffic light",   # 8
+    "stop sign",       # 9
+    "bench",           # 10
+    "bird",            # 11
+    "cat",             # 12
+    "dog",             # 13
+    "horse",           # 14
+    "cow",             # 15
+    "elephant",        # 16
+    "bottle",          # 17
+    "cup",             # 18
+    "bowl",            # 19
+    "pizza",           # 20
+    "cake",            # 21
+    "chair",           # 22
+    "couch",           # 23
+    "potted plant",    # 24
+    "bed"              # 25
+]
+
+# ---------------------------------------------------
+# MODEL LOADING (CACHED)
+# ---------------------------------------------------
+@st.cache_resource
+def load_model(path):
+    return tf.keras.models.load_model(path)
+
+MODEL_PATHS = {
+    "VGG16": "models/vgg16_smartvision.h5",
+    "ResNet50": "models/resnet50_smartvision.h5",
+    "MobileNetV2": "models/mobilenetv2_smartvision.h5",
+    "EfficientNetB0": "models/efficientnetb0_smartvision.h5"
+}
+
+# ---------------------------------------------------
+# FILE UPLOAD
+# ---------------------------------------------------
+uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+
+    try:
+        col1, col2 = st.columns(2)
+
+        # -----------------------
+        # Image Preprocessing
+        # -----------------------
+        image = Image.open(uploaded_file).convert("RGB")
+        resized_image = image.resize((224, 224))
+
+        with col1:
+            st.subheader("Uploaded Image")
+            st.image(image, width="stretch")
+
+        img_array = np.array(resized_image) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        # -----------------------
+        # Predictions
+        # -----------------------
+        with col2:
+            st.subheader("Model Predictions")
+
+            for model_name, path in MODEL_PATHS.items():
+
+                model = load_model(path)
+                predictions = model.predict(img_array, verbose=0)[0]
+
+                top5_indices = np.argsort(predictions)[-5:][::-1]
+
+                st.markdown(f"### {model_name} - Top 5 Predictions")
+
+                for idx in top5_indices:
+                    class_name = CLASS_NAMES[idx]
+                    confidence = predictions[idx]
+                    st.write(f"{class_name} — {confidence:.3f}")
+
+                st.divider()
+
+    except Exception as e:
+        st.error(f"Error during classification: {e}")

pages/2_Object_Detection.py

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+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+
+st.title("Object Detection (YOLOv8)")
+
+@st.cache_resource
+def load_model():
+    return YOLO("models/best.pt")
+
+model = load_model()
+
+confidence = st.slider("Confidence Threshold", 0.1, 1.0, 0.25)
+
+uploaded_file = st.file_uploader("Upload Image", type=["jpg","jpeg","png"])
+
+if uploaded_file:
+    try:
+        image = Image.open(uploaded_file)
+        img_array = np.array(image)
+
+        results = model.predict(img_array, conf=confidence)
+        annotated = results[0].plot()
+
+        st.image(annotated, width="stretch")
+
+    except Exception as e:
+        st.error(f"Detection failed: {e}")

pages/3_Model_Performance.py

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+import streamlit as st
+import pandas as pd
+
+st.title("Model Performance Comparison")
+
+data = {
+    "Model": ["VGG16","ResNet50","MobileNetV2","EfficientNetB0"],
+    "Accuracy": [0.83,0.88,0.85,0.91],
+    "Inference Time (ms)": [150,100,50,80]
+}
+
+df = pd.DataFrame(data)
+
+st.dataframe(df, use_container_width=True)
+
+st.bar_chart(df.set_index("Model")["Accuracy"])
+st.bar_chart(df.set_index("Model")["Inference Time (ms)"])

pages/4_About.py

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+import streamlit as st
+
+st.title("About SmartVision AI")
+
+st.markdown("""
+Dataset: COCO 25-Class Subset  
+Models Used:
+- VGG16
+- ResNet50
+- MobileNetV2
+- EfficientNetB0
+- YOLOv8
+
+Built with:
+- TensorFlow
+- PyTorch
+- Streamlit
+""")

pages/5_Live_Webcam.py

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+import streamlit as st
+import cv2
+import time
+from ultralytics import YOLO
+
+st.title("Live Webcam Object Detection")
+
+# -----------------------------
+# Load YOLO Model
+# -----------------------------
+@st.cache_resource
+def load_model():
+    return YOLO("models/best.pt")
+
+model = load_model()
+
+# -----------------------------
+# Sidebar Settings
+# -----------------------------
+confidence = st.sidebar.slider("Confidence Threshold", 0.1, 1.0, 0.4)
+
+start = st.button("Start Webcam")
+stop = st.button("Stop Webcam")
+
+frame_placeholder = st.empty()
+fps_placeholder = st.empty()
+
+# -----------------------------
+# Webcam Logic
+# -----------------------------
+if start:
+
+    cap = cv2.VideoCapture(0)
+
+    if not cap.isOpened():
+        st.error("Webcam not detected.")
+    else:
+        st.success("Webcam started. Press Stop to end.")
+
+        prev_time = 0
+
+        while cap.isOpened():
+
+            ret, frame = cap.read()
+            if not ret:
+                st.warning("Failed to read frame.")
+                break
+
+            # YOLO Prediction
+            results = model.predict(frame, conf=confidence, verbose=False)
+            annotated_frame = results[0].plot()
+
+            # FPS Calculation
+            current_time = time.time()
+            fps = 1 / (current_time - prev_time) if prev_time != 0 else 0
+            prev_time = current_time
+
+            # Display Frame
+            frame_placeholder.image(
+                annotated_frame,
+                channels="BGR",
+                width="stretch"
+            )
+
+            fps_placeholder.write(f"FPS: {fps:.2f}")
+
+            if stop:
+                break
+
+        cap.release()
+        cv2.destroyAllWindows()

runs/detect/smartvision_yolov8/args.yaml

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+task: detect
+mode: train
+model: yolov8n.pt
+data: smartvision_dataset/detection/data.yaml
+epochs: 90
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: cpu
+workers: 8
+project: null
+name: smartvision_yolov8
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+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: 0.01
+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
+rle: 1.0
+angle: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
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+scale: 0.5
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+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: C:\Users\asmis\OneDrive\Desktop\Smartvision\runs\detect\smartvision_yolov8

runs/detect/smartvision_yolov82/args.yaml

@@ -0,0 +1,109 @@
+task: detect
+mode: train
+model: yolov8n.pt
+data: smartvision_dataset/detection/data.yaml
+epochs: 90
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: cpu
+workers: 8
+project: null
+name: smartvision_yolov82
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+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: 0.01
+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
+rle: 1.0
+angle: 1.0
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+hsv_s: 0.7
+hsv_v: 0.4
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+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: C:\Users\asmis\OneDrive\Desktop\Smartvision\runs\detect\smartvision_yolov82

runs/detect/smartvision_yolov83/args.yaml

@@ -0,0 +1,109 @@
+task: detect
+mode: train
+model: yolov8n.pt
+data: smartvision_dataset/detection/data.yaml
+epochs: 90
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: cpu
+workers: 8
+project: null
+name: smartvision_yolov83
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+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: 0.01
+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
+rle: 1.0
+angle: 1.0
+nbs: 64
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+hsv_s: 0.7
+hsv_v: 0.4
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+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: C:\Users\asmis\OneDrive\Desktop\Smartvision\runs\detect\smartvision_yolov83