Added dataset and training data

YOLO_model.ipynb

@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ee6d3405-39e3-4109-84e0-f1ba6c7df74b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import random\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# --- CONFIGURATION ---\n",
+    "DATASET_DIR = os.path.abspath(\"dataset\")  # Absolute path to your dataset folder\n",
+    "# VIDEO_PATH = \"Screen_Recording_20260710_101822_Hades' Star.mp4\"  # Path to your test video\n",
+    "VIDEO_PATH = \"Screen_Recording_20260710_142109_Hades' Star.mp4\"\n",
+    "\n",
+    "# The exact list from your label.py script\n",
+    "CLASS_NAMES = [\n",
+    "    \"guardian\",\n",
+    "    \"interceptor\",\n",
+    "    \"sentinel\",\n",
+    "    \"rocket\",\n",
+    "    \"bomber\",\n",
+    "    \"background\",\n",
+    "    \"asteroid\",\n",
+    "]\n",
+    "# ---------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0245800c-f39d-479d-8093-a8573223d975",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-> Created dataset.yaml successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# === STEP 1: CREATE THE YOLO CONFIGURATION FILE ===\n",
+    "yaml_content = f\"\"\"\n",
+    "path: {DATASET_DIR}\n",
+    "train: images  # path relative to 'path'\n",
+    "val: images    # for a quick test, we will validate on the same images\n",
+    "\n",
+    "names:\n",
+    "\"\"\"\n",
+    "for idx, name in enumerate(CLASS_NAMES):\n",
+    "    yaml_content += f\"  {idx}: {name}\\n\"\n",
+    "\n",
+    "yaml_path = \"dataset.yaml\"\n",
+    "with open(yaml_path, \"w\") as f:\n",
+    "    f.write(yaml_content.strip())\n",
+    "\n",
+    "print(f\"-> Created {yaml_path} successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3e69197-7565-4464-8381-ffc871818819",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-> Initializing YOLO training...\n",
+      "Ultralytics 8.4.92 🚀 Python-3.13.14 torch-2.13.0+cu130 CPU (Intel Core i5-8365U 1.60GHz)\n",
+      "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, cls_pw=0.0, cls_remap=True, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=dataset.yaml, degrees=0.0, deterministic=True, device=, dfl=1.5, dis=6.0, distill_model=None, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=75, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=train-3, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, quantize=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/home/nathan/school/CSE_450/final/runs/detect/train-3, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=tracktrack.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
+      "Overriding model.yaml nc=80 with nc=7\n",
+      "\n",
+      "                   from  n    params  module                                       arguments                     \n",
+      "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
+      "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
+      "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
+      "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
+      "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
+      "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
+      "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
+      "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
+      "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
+      "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
+      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+      " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
+      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+      " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
+      " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
+      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+      " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
+      " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
+      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+      " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
+      " 22        [15, 18, 21]  1    752677  ultralytics.nn.modules.head.Detect           [7, 16, None, [64, 128, 256]] \n",
+      "Model summary: 130 layers, 3,012,213 parameters, 3,012,197 gradients, 8.2 GFLOPs\n",
+      "\n",
+      "Transferred 319/355 items from pretrained weights\n",
+      "Freezing layer 'model.22.dfl.conv.weight'\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2882.6±931.7 MB/s, size: 170.8 KB)\n",
+      "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/nathan/school/CSE_450/final/dataset/labels... 200 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 200/200 2.2Kit/s 0.1s\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0m/home/nathan/school/CSE_450/final/dataset/images/Screen_Recording_20260710_110910_Hades' Star_frame_1832.jpg: 1 duplicate labels removed\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0m/home/nathan/school/CSE_450/final/dataset/images/Screen_Recording_20260710_142109_Hades' Star_frame_500.jpg: 1 duplicate labels removed\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /home/nathan/school/CSE_450/final/dataset/labels.cache\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 3815.1±715.0 MB/s, size: 139.5 KB)\n",
+      "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/nathan/school/CSE_450/final/dataset/labels.cache... 200 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 200/200 52.4Mit/s 0.0s\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0m/home/nathan/school/CSE_450/final/dataset/images/Screen_Recording_20260710_110910_Hades' Star_frame_1832.jpg: 1 duplicate labels removed\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0m/home/nathan/school/CSE_450/final/dataset/images/Screen_Recording_20260710_142109_Hades' Star_frame_500.jpg: 1 duplicate labels removed\n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000909, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
+      "Plotting labels to /home/nathan/school/CSE_450/final/runs/detect/train-3/labels.jpg... \n",
+      "Image sizes 640 train, 640 val\n",
+      "Using 0 dataloader workers\n",
+      "Logging results to \u001b[1m/home/nathan/school/CSE_450/final/runs/detect/train-3\u001b[0m\n",
+      "Starting training for 75 epochs...\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       1/75         0G      2.958      5.133      1.861         24        640: 100% ━━━━━━━━━━━━ 13/13 5.7s/it 1:144.4ss\n",
+      "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 7/7 1.9s/it 13.3s2.4s\n",
+      "                   all        200        688          0          0          0          0\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       2/75         0G       2.32      4.479      1.373         62        640: 100% ━━━━━━━━━━━━ 13/13 6.4s/it 1:234.8ss\n",
+      "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 7/7 2.4s/it 16.7s3.1ss\n",
+      "                   all        200        688   0.000766     0.0548   0.000265   5.24e-05\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       3/75         0G      2.113      3.946      1.244         59        640: 100% ━━━━━━━━━━━━ 13/13 6.7s/it 1:276.1ss\n",
+      "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 7/7 2.8s/it 19.8s3.5ss\n",
+      "                   all        200        688     0.0023      0.121    0.00597    0.00267\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       4/75         0G      2.018      3.399       1.21         45        640: 100% ━━━━━━━━━━━━ 13/13 6.2s/it 1:215.4ss\n",
+      "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 7/7 3.0s/it 21.0s4.0ss\n",
+      "                   all        200        688     0.0103      0.753     0.0839     0.0235\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       5/75         0G      1.965      3.118      1.224         71        640: 100% ━━━━━━━━━━━━ 13/13 6.7s/it 1:274.7ss\n",
+      "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 7/7 2.4s/it 16.5s3.0s\n",
+      "                   all        200        688      0.493      0.128       0.12     0.0565\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       6/75         0G      1.819      2.967      1.173         74        640: 100% ━━━━━━━━━━━━ 13/13 7.0s/it 1:315.4ss\n",
+      "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 7/7 2.9s/it 20.0s3.6ss\n",
+      "                   all        200        688      0.171      0.101      0.149     0.0428\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       7/75         0G      1.865      2.806      1.192         49        640: 100% ━━━━━━━━━━━━ 13/13 6.3s/it 1:225.3ss\n",
+      "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 7/7 3.4s/it 23.8s4.4ss\n",
+      "                   all        200        688      0.359      0.237      0.224      0.108\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "\u001b[K       8/75         0G      1.783      2.766      1.169        124        640: 76% ━━━━━━━━━─── 10/13 9.0s/it 1:36<27.1s"
+     ]
+    }
+   ],
+   "source": [
+    "# === STEP 2: TRAIN A NANO YOLO MODEL ===\n",
+    "print(\"\\n-> Initializing YOLO training...\")\n",
+    "# 'yolov8n.pt' is the Nano model.\n",
+    "model = YOLO(\"yolov8n.pt\")\n",
+    "\n",
+    "# Train the model on your 130 annotations\n",
+    "model.train(data=yaml_path, epochs=75, imgsz=640, verbose=True)\n",
+    "print(\"\\n-> Training complete!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "138e1424-8ecd-4b76-a487-345bcb3b69f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = YOLO(\"runs/detect/train/weights/best.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "693c7e66-9c2f-427f-b2a0-311c4e5e8a38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# === STEP 3: EXTRACT A RANDOM FRAME AND TEST DETECTION ===\n",
+    "print(\"\\n-> Running full-frame inference test...\")\n",
+    "\n",
+    "# Grab a random frame from your video file\n",
+    "cap = cv2.VideoCapture(VIDEO_PATH)\n",
+    "if not cap.isOpened():\n",
+    "    print(f\"Error: Could not open video {VIDEO_PATH}\")\n",
+    "else:\n",
+    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+    "    random_idx = random.randint(0, total_frames - 1)\n",
+    "    cap.set(cv2.CAP_PROP_POS_FRAMES, random_idx)\n",
+    "    ret, frame = cap.read()\n",
+    "    cap.release()\n",
+    "\n",
+    "    if ret:\n",
+    "        # Run our freshly trained model on the single, complete video frame!\n",
+    "        results = model.predict(source=frame, conf=0.10, imgsz=640)\n",
+    "\n",
+    "        # Ultralytics built-in renderer\n",
+    "        annotated_frame = results[0].plot()\n",
+    "\n",
+    "        plt.figure(figsize=(24, 16))\n",
+    "\n",
+    "        # Display the result directly inside your Jupyter Notebook\n",
+    "        # plt.figure(figsize=(12, 8))\n",
+    "        # Convert BGR (OpenCV format) to RGB (Matplotlib format)\n",
+    "        plt.imshow(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))\n",
+    "        plt.title(f\"YOLO Object Detection Test (Frame: {random_idx})\", fontsize=14)\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.show()\n",
+    "    else:\n",
+    "        print(\"Error: Could not read a random frame from the video.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}