mirror of
https://github.com/ferdzo/vesselDetection.git
synced 2026-04-05 10:16:25 +00:00
Updated version with yolo v11
This commit is contained in:
mveco
395
testing.ipynb
Normal file
395
testing.ipynb
Normal file
@@ -0,0 +1,395 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "5f9b42a1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Collecting ultralytics\n",
|
||||
" Downloading ultralytics-8.3.159-py3-none-any.whl.metadata (37 kB)\n",
|
||||
"Collecting numpy>=1.23.0 (from ultralytics)\n",
|
||||
" Using cached numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (62 kB)\n",
|
||||
"Collecting matplotlib>=3.3.0 (from ultralytics)\n",
|
||||
" Using cached matplotlib-3.10.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
|
||||
"Collecting opencv-python>=4.6.0 (from ultralytics)\n",
|
||||
" Using cached opencv_python-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
|
||||
"Requirement already satisfied: pillow>=7.1.2 in ./.venv/lib/python3.12/site-packages (from ultralytics) (11.2.1)\n",
|
||||
"Collecting pyyaml>=5.3.1 (from ultralytics)\n",
|
||||
" Using cached PyYAML-6.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB)\n",
|
||||
"Collecting requests>=2.23.0 (from ultralytics)\n",
|
||||
" Downloading requests-2.32.4-py3-none-any.whl.metadata (4.9 kB)\n",
|
||||
"Collecting scipy>=1.4.1 (from ultralytics)\n",
|
||||
" Using cached scipy-1.16.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (61 kB)\n",
|
||||
"Collecting torch>=1.8.0 (from ultralytics)\n",
|
||||
" Using cached torch-2.7.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (29 kB)\n",
|
||||
"Collecting torchvision>=0.9.0 (from ultralytics)\n",
|
||||
" Using cached torchvision-0.22.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.1 kB)\n",
|
||||
"Collecting tqdm>=4.64.0 (from ultralytics)\n",
|
||||
" Using cached tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n",
|
||||
"Requirement already satisfied: psutil in ./.venv/lib/python3.12/site-packages (from ultralytics) (7.0.0)\n",
|
||||
"Collecting py-cpuinfo (from ultralytics)\n",
|
||||
" Downloading py_cpuinfo-9.0.0-py3-none-any.whl.metadata (794 bytes)\n",
|
||||
"Collecting pandas>=1.1.4 (from ultralytics)\n",
|
||||
" Downloading pandas-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (91 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hCollecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
|
||||
" Downloading ultralytics_thop-2.0.14-py3-none-any.whl.metadata (9.4 kB)\n",
|
||||
"Collecting contourpy>=1.0.1 (from matplotlib>=3.3.0->ultralytics)\n",
|
||||
" Using cached contourpy-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.5 kB)\n",
|
||||
"Collecting cycler>=0.10 (from matplotlib>=3.3.0->ultralytics)\n",
|
||||
" Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
|
||||
"Collecting fonttools>=4.22.0 (from matplotlib>=3.3.0->ultralytics)\n",
|
||||
" Using cached fonttools-4.58.4-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl.metadata (106 kB)\n",
|
||||
"Collecting kiwisolver>=1.3.1 (from matplotlib>=3.3.0->ultralytics)\n",
|
||||
" Using cached kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.2 kB)\n",
|
||||
"Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.12/site-packages (from matplotlib>=3.3.0->ultralytics) (25.0)\n",
|
||||
"Collecting pyparsing>=2.3.1 (from matplotlib>=3.3.0->ultralytics)\n",
|
||||
" Downloading pyparsing-3.2.3-py3-none-any.whl.metadata (5.0 kB)\n",
|
||||
"Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.12/site-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\n",
|
||||
"Collecting pytz>=2020.1 (from pandas>=1.1.4->ultralytics)\n",
|
||||
" Downloading pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\n",
|
||||
"Collecting tzdata>=2022.7 (from pandas>=1.1.4->ultralytics)\n",
|
||||
" Downloading tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
|
||||
"Collecting charset_normalizer<4,>=2 (from requests>=2.23.0->ultralytics)\n",
|
||||
" Downloading charset_normalizer-3.4.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (35 kB)\n",
|
||||
"Collecting idna<4,>=2.5 (from requests>=2.23.0->ultralytics)\n",
|
||||
" Using cached idna-3.10-py3-none-any.whl.metadata (10 kB)\n",
|
||||
"Collecting urllib3<3,>=1.21.1 (from requests>=2.23.0->ultralytics)\n",
|
||||
" Downloading urllib3-2.5.0-py3-none-any.whl.metadata (6.5 kB)\n",
|
||||
"Collecting certifi>=2017.4.17 (from requests>=2.23.0->ultralytics)\n",
|
||||
" Downloading certifi-2025.6.15-py3-none-any.whl.metadata (2.4 kB)\n",
|
||||
"Collecting filelock (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached filelock-3.18.0-py3-none-any.whl.metadata (2.9 kB)\n",
|
||||
"Collecting typing-extensions>=4.10.0 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached typing_extensions-4.14.0-py3-none-any.whl.metadata (3.0 kB)\n",
|
||||
"Collecting setuptools (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached setuptools-80.9.0-py3-none-any.whl.metadata (6.6 kB)\n",
|
||||
"Collecting sympy>=1.13.3 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached sympy-1.14.0-py3-none-any.whl.metadata (12 kB)\n",
|
||||
"Collecting networkx (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached networkx-3.5-py3-none-any.whl.metadata (6.3 kB)\n",
|
||||
"Collecting jinja2 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB)\n",
|
||||
"Collecting fsspec (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached fsspec-2025.5.1-py3-none-any.whl.metadata (11 kB)\n",
|
||||
"Collecting nvidia-cuda-nvrtc-cu12==12.6.77 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Collecting nvidia-cuda-runtime-cu12==12.6.77 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Collecting nvidia-cuda-cupti-cu12==12.6.80 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)\n",
|
||||
"Collecting nvidia-cudnn-cu12==9.5.1.17 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl.metadata (1.6 kB)\n",
|
||||
"Collecting nvidia-cublas-cu12==12.6.4.1 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Collecting nvidia-cufft-cu12==11.3.0.4 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Collecting nvidia-curand-cu12==10.3.7.77 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Collecting nvidia-cusolver-cu12==11.7.1.2 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)\n",
|
||||
"Collecting nvidia-cusparse-cu12==12.5.4.2 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)\n",
|
||||
"Collecting nvidia-cusparselt-cu12==0.6.3 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl.metadata (6.8 kB)\n",
|
||||
"Collecting nvidia-nccl-cu12==2.26.2 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.0 kB)\n",
|
||||
"Collecting nvidia-nvtx-cu12==12.6.77 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)\n",
|
||||
"Collecting nvidia-nvjitlink-cu12==12.6.85 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Collecting nvidia-cufile-cu12==1.11.1.6 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Collecting triton==3.3.1 (from torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached triton-3.3.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.5 kB)\n",
|
||||
"Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n",
|
||||
"Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)\n",
|
||||
"Collecting MarkupSafe>=2.0 (from jinja2->torch>=1.8.0->ultralytics)\n",
|
||||
" Using cached MarkupSafe-3.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB)\n",
|
||||
"Downloading ultralytics-8.3.159-py3-none-any.whl (1.0 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m37.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading matplotlib-3.10.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.6 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m95.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mta \u001b[36m0:00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl (16.6 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.6/16.6 MB\u001b[0m \u001b[31m29.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading opencv_python-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (63.0 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.0/63.0 MB\u001b[0m \u001b[31m30.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading pandas-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.0 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.0/12.0 MB\u001b[0m \u001b[31m39.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading PyYAML-6.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (767 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m767.5/767.5 kB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading requests-2.32.4-py3-none-any.whl (64 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.8/64.8 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading scipy-1.16.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (35.1 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.1/35.1 MB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading torch-2.7.1-cp312-cp312-manylinux_2_28_x86_64.whl (821.0 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m821.0/821.0 MB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (393.1 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m393.1/393.1 MB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (8.9 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl (23.7 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (897 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m897.7/897.7 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl (571.0 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m571.0/571.0 MB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (200.2 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.2/200.2 MB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.1 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (56.3 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (158.2 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.2/158.2 MB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (216.6 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m216.6/216.6 MB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl (156.8 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m156.8/156.8 MB\u001b[0m \u001b[31m30.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (201.3 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.3/201.3 MB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (19.7 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.7/19.7 MB\u001b[0m \u001b[31m37.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.3/89.3 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading triton-3.3.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (155.7 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.7/155.7 MB\u001b[0m \u001b[31m24.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hUsing cached torchvision-0.22.1-cp312-cp312-manylinux_2_28_x86_64.whl (7.5 MB)\n",
|
||||
"Downloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading ultralytics_thop-2.0.14-py3-none-any.whl (26 kB)\n",
|
||||
"Downloading py_cpuinfo-9.0.0-py3-none-any.whl (22 kB)\n",
|
||||
"Downloading certifi-2025.6.15-py3-none-any.whl (157 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.7/157.7 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading charset_normalizer-3.4.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (148 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m148.6/148.6 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading contourpy-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (323 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m323.7/323.7 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
|
||||
"Downloading fonttools-4.58.4-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl (4.9 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hUsing cached idna-3.10-py3-none-any.whl (70 kB)\n",
|
||||
"Downloading kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading pyparsing-3.2.3-py3-none-any.whl (111 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.1/111.1 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading pytz-2025.2-py2.py3-none-any.whl (509 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m509.2/509.2 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hUsing cached setuptools-80.9.0-py3-none-any.whl (1.2 MB)\n",
|
||||
"Downloading sympy-1.14.0-py3-none-any.whl (6.3 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m32.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading typing_extensions-4.14.0-py3-none-any.whl (43 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.8/43.8 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading tzdata-2025.2-py2.py3-none-any.whl (347 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m347.8/347.8 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading urllib3-2.5.0-py3-none-any.whl (129 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.8/129.8 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hUsing cached filelock-3.18.0-py3-none-any.whl (16 kB)\n",
|
||||
"Downloading fsspec-2025.5.1-py3-none-any.whl (199 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading jinja2-3.1.6-py3-none-any.whl (134 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.9/134.9 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hDownloading networkx-3.5-py3-none-any.whl (2.0 MB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m32.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
|
||||
"\u001b[?25hDownloading MarkupSafe-3.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23 kB)\n",
|
||||
"Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.2/536.2 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\u001b[?25hInstalling collected packages: pytz, py-cpuinfo, nvidia-cusparselt-cu12, mpmath, urllib3, tzdata, typing-extensions, tqdm, sympy, setuptools, pyyaml, pyparsing, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, kiwisolver, idna, fsspec, fonttools, filelock, cycler, charset_normalizer, certifi, triton, scipy, requests, pandas, opencv-python, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, jinja2, contourpy, nvidia-cusolver-cu12, matplotlib, torch, ultralytics-thop, torchvision, ultralytics\n",
|
||||
"Successfully installed MarkupSafe-3.0.2 certifi-2025.6.15 charset_normalizer-3.4.2 contourpy-1.3.2 cycler-0.12.1 filelock-3.18.0 fonttools-4.58.4 fsspec-2025.5.1 idna-3.10 jinja2-3.1.6 kiwisolver-1.4.8 matplotlib-3.10.3 mpmath-1.3.0 networkx-3.5 numpy-2.3.1 nvidia-cublas-cu12-12.6.4.1 nvidia-cuda-cupti-cu12-12.6.80 nvidia-cuda-nvrtc-cu12-12.6.77 nvidia-cuda-runtime-cu12-12.6.77 nvidia-cudnn-cu12-9.5.1.17 nvidia-cufft-cu12-11.3.0.4 nvidia-cufile-cu12-1.11.1.6 nvidia-curand-cu12-10.3.7.77 nvidia-cusolver-cu12-11.7.1.2 nvidia-cusparse-cu12-12.5.4.2 nvidia-cusparselt-cu12-0.6.3 nvidia-nccl-cu12-2.26.2 nvidia-nvjitlink-cu12-12.6.85 nvidia-nvtx-cu12-12.6.77 opencv-python-4.11.0.86 pandas-2.3.0 py-cpuinfo-9.0.0 pyparsing-3.2.3 pytz-2025.2 pyyaml-6.0.2 requests-2.32.4 scipy-1.16.0 setuptools-80.9.0 sympy-1.14.0 torch-2.7.1 torchvision-0.22.1 tqdm-4.67.1 triton-3.3.1 typing-extensions-4.14.0 tzdata-2025.2 ultralytics-8.3.159 ultralytics-thop-2.0.14 urllib3-2.5.0\n",
|
||||
"Requirement already satisfied: pillow in ./.venv/lib/python3.12/site-packages (11.2.1)\n",
|
||||
"Requirement already satisfied: torchvision in ./.venv/lib/python3.12/site-packages (0.22.1)\n",
|
||||
"Requirement already satisfied: numpy in ./.venv/lib/python3.12/site-packages (from torchvision) (2.3.1)\n",
|
||||
"Requirement already satisfied: torch==2.7.1 in ./.venv/lib/python3.12/site-packages (from torchvision) (2.7.1)\n",
|
||||
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in ./.venv/lib/python3.12/site-packages (from torchvision) (11.2.1)\n",
|
||||
"Requirement already satisfied: filelock in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (3.18.0)\n",
|
||||
"Requirement already satisfied: typing-extensions>=4.10.0 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (4.14.0)\n",
|
||||
"Requirement already satisfied: setuptools in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (80.9.0)\n",
|
||||
"Requirement already satisfied: sympy>=1.13.3 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (1.14.0)\n",
|
||||
"Requirement already satisfied: networkx in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (3.5)\n",
|
||||
"Requirement already satisfied: jinja2 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (3.1.6)\n",
|
||||
"Requirement already satisfied: fsspec in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (2025.5.1)\n",
|
||||
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (12.6.77)\n",
|
||||
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (12.6.77)\n",
|
||||
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (12.6.80)\n",
|
||||
"Requirement already satisfied: nvidia-cudnn-cu12==9.5.1.17 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (9.5.1.17)\n",
|
||||
"Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (12.6.4.1)\n",
|
||||
"Requirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (11.3.0.4)\n",
|
||||
"Requirement already satisfied: nvidia-curand-cu12==10.3.7.77 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (10.3.7.77)\n",
|
||||
"Requirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (11.7.1.2)\n",
|
||||
"Requirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (12.5.4.2)\n",
|
||||
"Requirement already satisfied: nvidia-cusparselt-cu12==0.6.3 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (0.6.3)\n",
|
||||
"Requirement already satisfied: nvidia-nccl-cu12==2.26.2 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (2.26.2)\n",
|
||||
"Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (12.6.77)\n",
|
||||
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (12.6.85)\n",
|
||||
"Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (1.11.1.6)\n",
|
||||
"Requirement already satisfied: triton==3.3.1 in ./.venv/lib/python3.12/site-packages (from torch==2.7.1->torchvision) (3.3.1)\n",
|
||||
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in ./.venv/lib/python3.12/site-packages (from sympy>=1.13.3->torch==2.7.1->torchvision) (1.3.0)\n",
|
||||
"Requirement already satisfied: MarkupSafe>=2.0 in ./.venv/lib/python3.12/site-packages (from jinja2->torch==2.7.1->torchvision) (3.0.2)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip install ultralytics\n",
|
||||
"!pip install --upgrade pillow\n",
|
||||
"!pip install --upgrade torchvision"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "794b81ae",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import random\n",
|
||||
"import torch\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import cv2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1e613e7f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['shipdetection.ipynb', 'runs', 'yolo11n.pt', 'dataset.zip', '.venv', '.gitignore', 'ships-aerial-images', 'testing.ipynb', '.git', 'vesselDetection.ipynb', 'yolo11x.pt', 'README.md']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from ultralytics import YOLO\n",
|
||||
"model = YOLO(\"yolo11x.pt\") \n",
|
||||
"\n",
|
||||
"data_path = 'ships-aerial-images/data.yaml'\n",
|
||||
"\n",
|
||||
"train_params = {\n",
|
||||
" 'epochs': 20,\n",
|
||||
" 'batch': 8,\n",
|
||||
" 'imgsz': 640,\n",
|
||||
" 'lr0': 0.01,\n",
|
||||
" 'lrf': 0.01,\n",
|
||||
" 'momentum': 0.937,\n",
|
||||
" 'weight_decay': 0.0005,\n",
|
||||
" 'optimizer': 'Adam',\n",
|
||||
" 'device': '0,1',\n",
|
||||
" 'pretrained': True,\n",
|
||||
"}\n",
|
||||
"model.train(data=data_path, **train_params)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1632a046",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Ultralytics 8.3.159 🚀 Python-3.12.3 torch-2.7.1+cu126 \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "ValueError",
|
||||
"evalue": "Invalid CUDA 'device=0,1' requested. Use 'device=cpu' or pass valid CUDA device(s) if available, i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n\ntorch.cuda.is_available(): True\ntorch.cuda.device_count(): 1\nos.environ['CUDA_VISIBLE_DEVICES']: 0\n",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mtrain_params\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/ferdzo/vesselDetection/.venv/lib/python3.12/site-packages/ultralytics/engine/model.py:791\u001b[39m, in \u001b[36mModel.train\u001b[39m\u001b[34m(self, trainer, **kwargs)\u001b[39m\n\u001b[32m 788\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m args.get(\u001b[33m\"\u001b[39m\u001b[33mresume\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 789\u001b[39m args[\u001b[33m\"\u001b[39m\u001b[33mresume\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28mself\u001b[39m.ckpt_path\n\u001b[32m--> \u001b[39m\u001b[32m791\u001b[39m \u001b[38;5;28mself\u001b[39m.trainer = \u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_smart_load\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtrainer\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43moverrides\u001b[49m\u001b[43m=\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_callbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 792\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args.get(\u001b[33m\"\u001b[39m\u001b[33mresume\u001b[39m\u001b[33m\"\u001b[39m): \u001b[38;5;66;03m# manually set model only if not resuming\u001b[39;00m\n\u001b[32m 793\u001b[39m \u001b[38;5;28mself\u001b[39m.trainer.model = \u001b[38;5;28mself\u001b[39m.trainer.get_model(weights=\u001b[38;5;28mself\u001b[39m.model \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.ckpt \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, cfg=\u001b[38;5;28mself\u001b[39m.model.yaml)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/ferdzo/vesselDetection/.venv/lib/python3.12/site-packages/ultralytics/engine/trainer.py:121\u001b[39m, in \u001b[36mBaseTrainer.__init__\u001b[39m\u001b[34m(self, cfg, overrides, _callbacks)\u001b[39m\n\u001b[32m 119\u001b[39m \u001b[38;5;28mself\u001b[39m.args = get_cfg(cfg, overrides)\n\u001b[32m 120\u001b[39m \u001b[38;5;28mself\u001b[39m.check_resume(overrides)\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m \u001b[38;5;28mself\u001b[39m.device = \u001b[43mselect_device\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 122\u001b[39m \u001b[38;5;66;03m# Update \"-1\" devices so post-training val does not repeat search\u001b[39;00m\n\u001b[32m 123\u001b[39m \u001b[38;5;28mself\u001b[39m.args.device = os.getenv(\u001b[33m\"\u001b[39m\u001b[33mCUDA_VISIBLE_DEVICES\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mcuda\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m.device) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m.device)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/ferdzo/vesselDetection/.venv/lib/python3.12/site-packages/ultralytics/utils/torch_utils.py:201\u001b[39m, in \u001b[36mselect_device\u001b[39m\u001b[34m(device, batch, newline, verbose)\u001b[39m\n\u001b[32m 194\u001b[39m LOGGER.info(s)\n\u001b[32m 195\u001b[39m install = (\n\u001b[32m 196\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mSee https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 197\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCUDA devices are seen by torch.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 198\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch.cuda.device_count() == \u001b[32m0\u001b[39m\n\u001b[32m 199\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 200\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m201\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 202\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid CUDA \u001b[39m\u001b[33m'\u001b[39m\u001b[33mdevice=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdevice\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m requested.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 203\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m Use \u001b[39m\u001b[33m'\u001b[39m\u001b[33mdevice=cpu\u001b[39m\u001b[33m'\u001b[39m\u001b[33m or pass valid CUDA device(s) if available,\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 204\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m i.e. \u001b[39m\u001b[33m'\u001b[39m\u001b[33mdevice=0\u001b[39m\u001b[33m'\u001b[39m\u001b[33m or \u001b[39m\u001b[33m'\u001b[39m\u001b[33mdevice=0,1,2,3\u001b[39m\u001b[33m'\u001b[39m\u001b[33m for Multi-GPU.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 205\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mtorch.cuda.is_available(): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtorch.cuda.is_available()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 206\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mtorch.cuda.device_count(): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtorch.cuda.device_count()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 207\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mos.environ[\u001b[39m\u001b[33m'\u001b[39m\u001b[33mCUDA_VISIBLE_DEVICES\u001b[39m\u001b[33m'\u001b[39m\u001b[33m]: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvisible\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 208\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minstall\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 209\u001b[39m )\n\u001b[32m 211\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cpu \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mps \u001b[38;5;129;01mand\u001b[39;00m torch.cuda.is_available(): \u001b[38;5;66;03m# prefer GPU if available\u001b[39;00m\n\u001b[32m 212\u001b[39m devices = device.split(\u001b[33m\"\u001b[39m\u001b[33m,\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m device \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m0\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;66;03m# i.e. \"0,1\" -> [\"0\", \"1\"]\u001b[39;00m\n",
|
||||
"\u001b[31mValueError\u001b[39m: Invalid CUDA 'device=0,1' requested. Use 'device=cpu' or pass valid CUDA device(s) if available, i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n\ntorch.cuda.is_available(): True\ntorch.cuda.device_count(): 1\nos.environ['CUDA_VISIBLE_DEVICES']: 0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
|
||||
"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
|
||||
"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
|
||||
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model.train(data=data_path, **train_params)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "054593ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metrics = model.val()\n",
|
||||
"print(f\"mAP@0.5: {metrics.box.map50:.4f}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "40b49cb7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_image = \"image.jpg\"\n",
|
||||
"results = model(test_image)\n",
|
||||
"\n",
|
||||
"results.show()\n",
|
||||
"\n",
|
||||
"results.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "da994a21",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.save(\"vessel_detector.pt\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user