{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 02 — Trénování YOLOv8 na VisDrone\n", "\n", "Trénujeme YOLOv8s (small) fine-tuned z ImageNet vah na VisDrone dataset.\n", "Model detekuje 4 třídy vozidel z leteckých snímků.\n", "\n", "**GPU doporučeno** (trénink na CPU trvá ~10× déle)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n", "PyTorch: 2.4.1+cu124\n", "CUDA dostupná: True\n", "GPU: NVIDIA GeForce RTX 4090\n" ] } ], "source": [ "!pip install ultralytics --quiet\n", "\n", "import torch\n", "print(f\"PyTorch: {torch.__version__}\")\n", "print(f\"CUDA dostupná: {torch.cuda.is_available()}\")\n", "if torch.cuda.is_available():\n", " print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n", "elif torch.backends.mps.is_available():\n", " print(\"MPS (Apple Silicon) dostupné\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating new Ultralytics Settings v0.0.6 file ✅ \n", "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", "Downloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s.pt to 'yolov8s.pt': 100% ━━━━━━━━━━━━ 21.5MB 60.0MB/s 0.4s\n", "YOLOv8s summary: 129 layers, 11,166,560 parameters, 0 gradients, 28.8 GFLOPs\n", "Model načten: (129, 11166560, 0, 28.816844800000002)\n" ] } ], "source": [ "from ultralytics import YOLO\n", "from pathlib import Path\n", "\n", "YAML = Path(\"data/yolo_visdrone/dataset.yaml\")\n", "assert YAML.exists(), f\"Nejprve spusť 01_dataset_prep.ipynb! Chybí: {YAML}\"\n", "\n", "# Zvolíme YOLOv8s — dobrý poměr přesnosti a rychlosti\n", "# Alternativy: yolov8n (nejrychlejší), yolov8m (přesnější)\n", "model = YOLO(\"yolov8s.pt\") # stáhne předtrénované ImageNet váhy\n", "print(\"Model načten:\", model.info())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "import torch\nfrom pathlib import Path\n\n# Detekce dostupného zařízení\nif torch.cuda.is_available():\n DEVICE = \"cuda\"\nelif torch.backends.mps.is_available():\n DEVICE = \"mps\"\nelse:\n DEVICE = \"cpu\"\n\nprint(f\"Trénink na: {DEVICE}\")\n\nresults = model.train(\n data=str(YAML),\n epochs=50,\n imgsz=640,\n batch=16,\n device=DEVICE,\n name=\"visdrone_vehicles\",\n project=str(Path(\"runs/train\").resolve()),\n patience=10, # early stopping\n save=True,\n save_period=10,\n val=True,\n plots=True,\n # Augmentace pro letecké snímky\n degrees=15.0, # rotace\n flipud=0.5, # vertikální flip\n fliplr=0.5,\n mosaic=1.0,\n scale=0.5,\n)" }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Zobrazení tréninkových grafů\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "from pathlib import Path\n", "import glob\n", "\n", "run_dir = Path(\"runs/train/visdrone_vehicles\")\n", "\n", "for plot_name in [\"results.png\", \"confusion_matrix.png\", \"PR_curve.png\", \"val_batch0_pred.jpg\"]:\n", " p = run_dir / plot_name\n", " if p.exists():\n", " fig, ax = plt.subplots(figsize=(12, 6))\n", " ax.imshow(mpimg.imread(p))\n", " ax.axis(\"off\")\n", " ax.set_title(plot_name)\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fine-tuning na Lánov (volitelné)\n", "\n", "Pokud existuje `dataset_lanov.zip` nebo rozbalená složka `lanov/`, provede se fine-tuning předchozího modelu na vlastních datech z Lánova." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "# Validace nejlepšího modelu\nbest_weights = Path(\"runs/train/visdrone_vehicles/weights/best.pt\")\nassert best_weights.exists(), \"Trénink ještě neproběhl nebo selhal\"\n\nmodel_best = YOLO(str(best_weights))\nval_results = model_best.val(data=str(YAML), imgsz=640, split=\"val\")\n\nprint(f\"\\nmAP50: {val_results.box.map50:.4f}\")\nprint(f\"mAP50-95: {val_results.box.map:.4f}\")\nfor i, cls in enumerate([\"car\", \"van\", \"truck\", \"bus\"]):\n ap = val_results.box.ap50[i] if i < len(val_results.box.ap50) else float('nan')\n print(f\" AP50[{cls}]: {ap:.4f}\")" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "import zipfile\nimport shutil\nfrom pathlib import Path\n\nLANOV_ZIP = Path('dataset_lanov.zip')\nLANOV_YAML = Path('lanov/dataset.yaml')\n\n# Rozbal zip pokud yaml ještě není\nif LANOV_ZIP.exists() and not LANOV_YAML.exists():\n print('Rozbaluji dataset_lanov.zip ...')\n with zipfile.ZipFile(LANOV_ZIP) as zf:\n zf.extractall('.')\n print('Hotovo.')\n\nif not LANOV_YAML.exists():\n print('Lánov dataset nenalezen — fine-tuning přeskočen.')\n print('Spusť prepare_dataset.py nebo nahraj dataset_lanov.zip.')\nelse:\n print(f'Fine-tuning na: {LANOV_YAML}')\n\n # Záloha modelu před fine-tuningem\n backup = best_weights.parent / 'best_before_finetune.pt'\n shutil.copy(best_weights, backup)\n print(f'Záloha uložena: {backup}')\n\n lanov_model = YOLO(str(best_weights)) # navazuje na VisDrone trénink\n\n lanov_results = lanov_model.train(\n data=str(LANOV_YAML),\n epochs=30,\n imgsz=256,\n batch=32,\n device=DEVICE,\n name='lanov_finetune',\n project=str(Path(\"runs/train\").resolve()),\n patience=10,\n save=True,\n val=True,\n plots=True,\n degrees=10.0,\n flipud=0.5,\n fliplr=0.5,\n mosaic=0.5,\n lr0=0.001, # nižší LR pro fine-tuning\n )\n\n best_weights = Path(lanov_results.save_dir) / 'weights' / 'best.pt'\n print(f'Lánov fine-tuning hotov. Nejlepší váhy: {best_weights}')" }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nejlepší model uložen v: /workspace/runs/detect/runs/detect/runs/train/lanov_finetune-2/weights/best.pt\n", "Konfigurace zapsána do: model_config.json\n" ] } ], "source": [ "# Uložení cesty k nejlepšímu modelu pro další notebooky\n", "import json\n", "\n", "config = {\"best_model\": str(best_weights.resolve())}\n", "with open(\"model_config.json\", \"w\") as f:\n", " json.dump(config, f)\n", "\n", "print(f\"Nejlepší model uložen v: {best_weights}\")\n", "print(f\"Konfigurace zapsána do: model_config.json\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 4 }