187 lines
8.6 KiB
Plaintext
187 lines
8.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 02 — Trénování YOLOv8 na VisDrone\n",
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"\n",
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"Trénujeme YOLOv8s (small) fine-tuned z ImageNet vah na VisDrone dataset.\n",
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"Model detekuje 4 třídy vozidel z leteckých snímků.\n",
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"\n",
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"**GPU doporučeno** (trénink na CPU trvá ~10× déle)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\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",
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"\u001b[0m\n",
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"\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",
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"\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",
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"PyTorch: 2.4.1+cu124\n",
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"CUDA dostupná: True\n",
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"GPU: NVIDIA GeForce RTX 4090\n"
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]
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}
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],
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"source": [
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"!pip install ultralytics --quiet\n",
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"\n",
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"import torch\n",
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"print(f\"PyTorch: {torch.__version__}\")\n",
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"print(f\"CUDA dostupná: {torch.cuda.is_available()}\")\n",
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"if torch.cuda.is_available():\n",
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" print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n",
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"elif torch.backends.mps.is_available():\n",
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" print(\"MPS (Apple Silicon) dostupné\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Creating new Ultralytics Settings v0.0.6 file ✅ \n",
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"View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
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"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",
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"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",
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"YOLOv8s summary: 129 layers, 11,166,560 parameters, 0 gradients, 28.8 GFLOPs\n",
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"Model načten: (129, 11166560, 0, 28.816844800000002)\n"
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]
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}
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],
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"source": [
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"from ultralytics import YOLO\n",
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"from pathlib import Path\n",
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"\n",
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"YAML = Path(\"data/yolo_visdrone/dataset.yaml\")\n",
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"assert YAML.exists(), f\"Nejprve spusť 01_dataset_prep.ipynb! Chybí: {YAML}\"\n",
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"\n",
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"# Zvolíme YOLOv8s — dobrý poměr přesnosti a rychlosti\n",
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"# Alternativy: yolov8n (nejrychlejší), yolov8m (přesnější)\n",
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"model = YOLO(\"yolov8s.pt\") # stáhne předtrénované ImageNet váhy\n",
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"print(\"Model načten:\", model.info())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"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)"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Zobrazení tréninkových grafů\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.image as mpimg\n",
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"from pathlib import Path\n",
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"import glob\n",
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"\n",
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"run_dir = Path(\"runs/train/visdrone_vehicles\")\n",
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"\n",
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"for plot_name in [\"results.png\", \"confusion_matrix.png\", \"PR_curve.png\", \"val_batch0_pred.jpg\"]:\n",
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" p = run_dir / plot_name\n",
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" if p.exists():\n",
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" fig, ax = plt.subplots(figsize=(12, 6))\n",
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" ax.imshow(mpimg.imread(p))\n",
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" ax.axis(\"off\")\n",
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" ax.set_title(plot_name)\n",
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" plt.tight_layout()\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Fine-tuning na Lánov (volitelné)\n",
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"\n",
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"Pokud existuje `dataset_lanov.zip` nebo rozbalená složka `lanov/`, provede se fine-tuning předchozího modelu na vlastních datech z Lánova."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"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}\")"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"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}')"
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Nejlepší model uložen v: /workspace/runs/detect/runs/detect/runs/train/lanov_finetune-2/weights/best.pt\n",
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"Konfigurace zapsána do: model_config.json\n"
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]
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}
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],
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"source": [
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"# Uložení cesty k nejlepšímu modelu pro další notebooky\n",
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"import json\n",
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"\n",
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"config = {\"best_model\": str(best_weights.resolve())}\n",
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"with open(\"model_config.json\", \"w\") as f:\n",
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" json.dump(config, f)\n",
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"\n",
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"print(f\"Nejlepší model uložen v: {best_weights}\")\n",
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"print(f\"Konfigurace zapsána do: model_config.json\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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} |