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vesselDetection/.ipynb_checkpoints/Lab5-checkpoint.ipynb
ferdzo c1f24c3ac8 Updated version
2025-06-30 17:35:21 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "7f263647",
"metadata": {},
"source": [
"# Laboratory Exercise 5"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "448199f3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"!pip install ultralytics"
]
},
{
"cell_type": "markdown",
"id": "41b20cd9",
"metadata": {},
"source": [
"### Dataset\n",
"- **Source** : Kaggle\n",
"- **Format** : Images + annotations\n",
"- **Classes**: 1 (`ship`)\n",
"- **Resolution per image**: Typically 640x640\n",
"- **Dataset size**: 26900 pictures\n"
]
},
{
"cell_type": "markdown",
"id": "3f72c4c8",
"metadata": {},
"source": [
"#### Importing dataset and pre-trained model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dab37f87",
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"\n",
"model = YOLO(\"yolo11l.pt\")\n",
"\n",
"data_path = 'ships-aerial-images/data.yaml'"
]
},
{
"cell_type": "markdown",
"id": "9a15a1f5",
"metadata": {},
"source": [
"### Final training parameterse after couple iterations "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "33e8f858",
"metadata": {},
"outputs": [],
"source": [
"train_params = {\n",
" 'epochs': 40,\n",
" 'batch': 32,\n",
" 'imgsz': 640,\n",
" 'lr0': 5e-4,\n",
" 'lrf': 0.1,\n",
" 'warmup_epochs': 5,\n",
" 'warmup_bias_lr': 1e-6,\n",
" 'momentum': 0.937,\n",
" 'weight_decay': 0.0001,\n",
" 'optimizer': 'AdamW',\n",
" 'device': '0,1',\n",
" 'project': 'runs/train',\n",
" 'name': 'vessel_deteciton_v11l',\n",
" 'exist_ok': True,\n",
" 'save_period': 2,\n",
" 'workers': 8,\n",
" 'patience': 20, \n",
" 'cos_lr': True, \n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "940aca02",
"metadata": {},
"outputs": [],
"source": [
"model.train(data=data_path, **train_params)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "55e7e6a4",
"metadata": {},
"outputs": [],
"source": [
"model = YOLO(\"runs/train/vessel_deteciton_v11l/weights/best.pt\")"
]
},
{
"cell_type": "markdown",
"id": "05a6fd1f",
"metadata": {},
"source": [
"### Validation"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cf1f9cdb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics 8.3.159 🚀 Python-3.12.3 torch-2.7.1+cu126 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
" CUDA:1 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 106.4±87.7 MB/s, size: 11.8 KB)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mval: \u001b[0mScanning /home/mlmonster/Projects/ferdzo/vesselDetection/ships-aerial-images/valid/labels.cache... 2165 images, 68 backgrounds, 0 corrupt: 100%|██████████| 2165/2165 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 172, len(boxes) = 3720. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 136/136 [00:15<00:00, 8.74it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" all 2165 3720 0.603 0.543 0.551 0.341\n",
"Speed: 0.2ms preprocess, 6.2ms inference, 0.0ms loss, 0.2ms postprocess per image\n",
"Results saved to \u001b[1m/home/mlmonster/Projects/ferdzo/vesselDetection/runs/detect/val4\u001b[0m\n"
]
}
],
"source": [
"validation = model.val(conf=0.01,iou=0.7, max_det=300, imgsz=640, device='0,1')"
]
},
{
"cell_type": "markdown",
"id": "124cb886",
"metadata": {},
"source": [
"### Testing the model on custom images"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "43560cd1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"image 1/1 /home/mlmonster/Projects/ferdzo/vesselDetection/5af55.jpg: 640x640 2 ships, 20.7ms\n",
"Speed: 21.6ms preprocess, 20.7ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 640)\n",
"Predictions: tensor([[727.0521, 301.3238, 749.1639, 321.9431],\n",
" [631.3250, 203.8833, 668.7556, 220.2926]], device='cuda:0')\n",
"Confidence: tensor([0.7513, 0.3399], device='cuda:0')\n",
"Class IDs: tensor([0., 0.], device='cuda:0')\n",
"Number of detections: 2\n"
]
}
],
"source": [
"test_image = \"/home/mlmonster/Projects/ferdzo/vesselDetection/5af55.jpg\"\n",
"results = model(test_image)\n",
"\n",
"for result in results:\n",
" print(f\"Predictions: {result.boxes.xyxy}\")\n",
" print(f\"Confidence: {result.boxes.conf}\")\n",
" print(f\"Class IDs: {result.boxes.cls}\")\n",
" print(f\"Number of detections: {len(result.boxes)}\") \n",
" result.save()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c42e373",
"metadata": {},
"outputs": [],
"source": [
"model.export(format='onnx')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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