You need to enable JavaScript to run this app.
优惠活动
大模型
产品
解决方案
定价
更多

在Google Colab训练目标检测模型时遇No module named 'deployment'错误求助

Fixing "No module named 'deployment'" Error in Google Colab for TensorFlow Object Detection

Hey Malathi, this error pops up because Google Colab doesn’t automatically add the TensorFlow Models research directory and its subfolders to your Python path—something your local tensorflow-gpu environment is already configured to handle. The deployment module lives right in the research folder of the TensorFlow Models repo, so we just need to make sure Colab knows where to find it.

Here’s how to fix it step-by-step:

  • First, set up the correct Python path so Colab can locate the deployment module and other Object Detection API dependencies. Run these commands in a Colab cell (adjust the path to match your actual research directory location):

    cd /content/my_drive/tut_kaggle/zero/models/research/
    export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
    

    If you prefer setting the path directly in Python (useful if you’re mixing code and shell commands), add this before importing any Object Detection modules:

    import sys
    sys.path.append('/content/my_drive/tut_kaggle/zero/models/research/')
    sys.path.append('/content/my_drive/tut_kaggle/zero/models/research/slim/')
    
  • Next, compile the protobuf files required by the Object Detection API. This is a critical step that’s often overlooked in Colab. From the same research directory, run:

    protoc object_detection/protos/*.proto --python_out=.
    
  • Finally, re-run your training command. To make sure the path settings take effect immediately, combine all the steps in one cell:

    cd /content/my_drive/tut_kaggle/zero/models/research/
    export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
    unset DISPLAY XAUTHORITY
    xvfb-run python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config
    

Why this works:

The deployment module is part of the TensorFlow Models research codebase. Adding the research and slim directories to your PYTHONPATH tells Python exactly where to look for this module. Compiling the protobuf files converts the API’s proto definitions into usable Python modules, which are required for the training script to run properly.

内容的提问来源于stack exchange,提问作者Malathi K

相关产品推荐
方舟 Agent Plan

超全模态模型 × Harness 升级,最新支持 Deepseek-V4.1-Flash、GLM-5.3 系列、Doubao-Seedream-5.0-pro、Kimi-K3 (部分), 限时 9.9 元起

最近更新时间:2026.05.29 06:42:54