[LAB 11 · 从 RGB 到像素：读出、FCN 与边界](/course/notebooks/9e268706471944e3a59e6f568f9ce9a3.html)

# 运行：模型输出必须和输入、真值一起看 · 实现代码
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前景/背景/mIoU口径、RGB到mask训练、FCN与1×1读出及边界
展开完整实现代码
```
import subprocess,sys,json
subprocess.run([sys.executable,'lab11_segmentation.py','--out','lab11-results-rerun','--epochs','20'],check=True)
R=json.loads(Path('lab11-results-rerun/run.json').read_text())
print('sampling:',R['sampling_warmup'])
for key in ['random_FCN','learned_pixel_readout','trained_FCN','condition_nearest_logits','condition_scale_045','condition_RGB_blur5','readout_RGB_blur5']:
    r=R[key];print(key,{k:round(v,5) for k,v in r.items() if 'IoU' in k or k=='pixel_accuracy'})
print(R['provenance'])
from IPython.display import Image,display
for filename in ['sampling-warmup.png','heldout-segmentation.png','failure-segmentation.png','small-object-segmentation.png','blur-segmentation.png']:
    display(Image(filename='lab11-results-rerun/'+filename))
```