Lecture 03主实验的输入、目标和固定协议 · 实现代码
主实验的输入、目标和固定协议 · 实现代码
主线阅读与优先实验
RGB图像、单特征层锚框检测器、分类/位置损失、NMS、独立检测评价
展开完整实现代码
import importlib.util, subprocess, sys
from pathlib import Path
# The only download, if needed, is open-source Python packages, not model weights.
if importlib.util.find_spec('torch') is None:
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'torch', '--index-url', 'https://download.pytorch.org/whl/cpu'])
missing=[p for p in ['numpy','matplotlib'] if importlib.util.find_spec(p) is None]
if missing: subprocess.check_call([sys.executable,'-m','pip','install',*missing])
import torch, numpy as np
print('Python',sys.version.split()[0], 'torch',torch.__version__, 'numpy',np.__version__, 'device: CPU')