Upload complete dataset

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+labels/train.cache filter=lfs diff=lfs merge=lfs -text

classes.txt

@@ -0,0 +1,9 @@
+cat
+dog
+cup
+vase
+potted plant
+laptop
+keyboard
+remote
+book

dataset.yaml

@@ -7,7 +7,9 @@ names:
   5: laptop
   6: keyboard
   7: remote
-  8: book
+  8: couch
+  9: chair
+  10: bed
 path: C:\Users\ymr\Documents\files\polyu\year3sem2\cv\proj\dataset
 test: .\images\test\
 train: .\images\train\

dataset_tools.py

@@ -0,0 +1,166 @@
+"""
+Dataset Tools for YOLO Object Detection
+=====================================
+
+This script provides tools to work with your YOLO dataset locally
+and from Hugging Face Hub.
+"""
+
+from datasets import load_dataset
+import yaml
+from pathlib import Path
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+
+class YOLODatasetTools:
+    def __init__(self, dataset_path=".", hf_repo_id="Sean676/cv_proj"):
+        self.dataset_path = Path(dataset_path)
+        self.hf_repo_id = hf_repo_id
+        self.class_names = []
+        self.dataset_info = {}
+        
+    def load_config(self):
+        """Load dataset configuration from dataset.yaml"""
+        config_path = self.dataset_path / "dataset.yaml"
+        if config_path.exists():
+            with open(config_path, 'r') as f:
+                config = yaml.safe_load(f)
+                self.class_names = list(config.get('names', {}).values())
+                self.dataset_info = config
+                return config
+        return None
+    
+    def inspect_local_dataset(self):
+        """Inspect local dataset structure"""
+        print("=== Local Dataset Inspection ===")
+        
+        # Load config
+        config = self.load_config()
+        if config:
+            print(f"Classes: {self.class_names}")
+            print(f"Total classes: {len(self.class_names)}")
+        
+        # Check images and labels
+        images_dir = self.dataset_path / "images"
+        labels_dir = self.dataset_path / "labels"
+        
+        if images_dir.exists():
+            print(f"\nImages directory: {images_dir}")
+            for split in ["train", "val", "test"]:
+                split_dir = images_dir / split
+                if split_dir.exists():
+                    images = list(split_dir.glob("*.jpg")) + list(split_dir.glob("*.png"))
+                    print(f"  {split}: {len(images)} images")
+        
+        if labels_dir.exists():
+            print(f"\nLabels directory: {labels_dir}")
+            for split in ["train", "val", "test"]:
+                split_dir = labels_dir / split
+                if split_dir.exists():
+                    labels = list(split_dir.glob("*.txt"))
+                    print(f"  {split}: {len(labels)} label files")
+    
+    def load_from_hf(self):
+        """Load dataset from Hugging Face Hub"""
+        try:
+            print("Loading dataset from Hugging Face Hub...")
+            dataset = load_dataset(self.hf_repo_id)
+            print(f"Dataset loaded successfully!")
+            print(dataset)
+            return dataset
+        except Exception as e:
+            print(f"Failed to load from Hub: {e}")
+            return None
+    
+    def visualize_sample(self, split="train", max_samples=4):
+        """Visualize sample images with bounding boxes"""
+        images_dir = self.dataset_path / "images" / split
+        labels_dir = self.dataset_path / "labels" / split
+        
+        if not images_dir.exists() or not labels_dir.exists():
+            print(f"Split '{split}' not found locally")
+            return
+        
+        # Get sample images
+        image_files = list(images_dir.glob("*.jpg"))[:max_samples]
+        
+        fig, axes = plt.subplots(2, 2, figsize=(12, 10))
+        axes = axes.flatten()
+        
+        for i, img_path in enumerate(image_files):
+            if i >= max_samples:
+                break
+                
+            # Load image
+            img = cv2.imread(str(img_path))
+            img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            
+            # Load and draw bounding boxes
+            label_path = labels_dir / (img_path.stem + ".txt")
+            if label_path.exists():
+                with open(label_path, 'r') as f:
+                    for line in f:
+                        parts = line.strip().split()
+                        if len(parts) >= 5:
+                            class_id = int(parts[0])
+                            x_center, y_center, width, height = map(float, parts[1:5])
+                            
+                            # Convert to pixel coordinates
+                            h, w = img.shape[:2]
+                            x1 = int((x_center - width/2) * w)
+                            y1 = int((y_center - height/2) * h)
+                            x2 = int((x_center + width/2) * w)
+                            y2 = int((y_center + height/2) * h)
+                            
+                            # Draw rectangle
+                            cv2.rectangle(img_rgb, (x1, y1), (x2, y2), (255, 0, 0), 2)
+                            
+                            # Add class label
+                            if class_id < len(self.class_names):
+                                label = self.class_names[class_id]
+                                cv2.putText(img_rgb, label, (x1, y1-10), 
+                                          cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+            
+            axes[i].imshow(img_rgb)
+            axes[i].set_title(f"Sample {i+1}: {img_path.name}")
+            axes[i].axis('off')
+        
+        plt.tight_layout()
+        plt.savefig(f"{split}_samples.png")
+        print(f"Visualization saved as {split}_samples.png")
+        plt.show()
+    
+    def download_from_hf(self, local_path="./downloaded_dataset"):
+        """Download dataset from Hugging Face to local folder"""
+        try:
+            dataset = load_dataset(self.hf_repo_id)
+            save_path = Path(local_path)
+            save_path.mkdir(exist_ok=True)
+            
+            dataset.save_to_disk(str(save_path))
+            print(f"Dataset downloaded to: {save_path}")
+            return save_path
+        except Exception as e:
+            print(f"Download failed: {e}")
+            return None
+
+def main():
+    tools = YOLODatasetTools()
+    
+    print("=== YOLO Dataset Tools ===")
+    print("\n1. Inspect local dataset:")
+    tools.inspect_local_dataset()
+    
+    print("\n2. Load from Hugging Face:")
+    hf_dataset = tools.load_from_hf()
+    
+    print("\n3. Visualize samples (if matplotlib available):")
+    try:
+        tools.visualize_sample("train", max_samples=2)
+    except Exception as e:
+        print(f"Visualization failed: {e}")
+        print("Install matplotlib: pip install matplotlib")
+
+if __name__ == "__main__":
+    main()