Train_ssd.py – Could not find image warning

Issue Overview

Users are encountering a warning message while running the train_ssd.py script for training an SSD-Mobilenet model, specifically: "warning – could not find image yotoXXXX – ignoring from dataset." This issue arises during the training process when users attempt to use a dataset that was manually downloaded and converted into Pascal VOC format. The symptoms include multiple warnings about missing images, leading to a train dataset size of zero, which results in a ValueError indicating that num_samples should be a positive integer value but is zero.

The problem typically occurs after users have set up their directories with the necessary .xml files and images but have not placed the images in the required subdirectory structure. Users report that they have followed tutorials but did not utilize camera capture, which is commonly referenced in these guides. The issue affects users’ ability to train their models effectively, as it prevents any data from being processed.

Possible Causes

1. Incorrect Directory Structure: The images may not be placed in the required JPEGImages folder, which is mandatory for the script to locate them.

   • Explanation: The train_ssd.py script expects a specific directory layout consistent with Pascal VOC format, where images must reside in a designated folder.

2. Empty or Incorrectly Formatted Text Files: The train.txt, val.txt, and test.txt files may be empty or incorrectly formatted.

   • Explanation: These files should reference the corresponding image filenames without extensions; if they are empty or incorrectly formatted, the dataset loader cannot find any samples.

3. Missing Image Files: Some image files referenced in the annotations may not exist in the specified directory.

   • Explanation: If the XML annotations reference images that are not present, warnings will be generated for each missing file.

4. XML Format Issues: The XML files may not adhere to the expected structure or naming conventions.

   • Explanation: Each XML file should contain a <filename> tag that matches the actual image filename (including its extension).

5. User Misconfiguration: Users may overlook required steps in setting up their datasets.

   • Explanation: Tutorials often assume certain configurations that users might miss, especially when adapting datasets from different formats.

Troubleshooting Steps, Solutions & Fixes

1. Verify Directory Structure:

   • Ensure that all images are located in a folder named JPEGImages, which should be inside your dataset directory.
   • Example structure:

     dataset/
     ├── Annotations/
     │   └── *.xml
     ├── ImageSets/
     │   └── Main/
     │       ├── train.txt
     │       ├── val.txt
     │       └── test.txt
     └── JPEGImages/
         └── *.jpg
     

2. Check Text Files:

   • Open train.txt, val.txt, and test.txt to ensure they contain references to image filenames without extensions.
   • Example content of train.txt:

     yoto10383
     yoto10412
     

3. Inspect XML Files:

   • Open each .xml file in the Annotations folder and verify that the <filename> tag matches the corresponding image file name exactly (including extension).
   • Example XML snippet:

     <annotation>
         <filename>yoto10383.jpg</filename>
         ...
     </annotation>
     

4. Run Diagnostic Commands:

   • Execute the following command to check if your dataset paths are correctly set:

     python3 train_ssd.py --dataset-type=voc --data=/path/to/your/dataset --model-dir=/path/to/model/dir
     

5. Simplify Dataset for Testing:

   • Temporarily reduce your dataset size by using only a few images to isolate issues more easily.

6. Consult Documentation:

   • Review the official documentation for SSD training and ensure all requirements are met, including any additional dependencies or software configurations.

7. Seek Community Support:

   • If issues persist after following these steps, consider posting detailed information about your setup on forums dedicated to Jetson development for further assistance.

By following these structured troubleshooting steps, users can systematically identify and resolve issues related to missing images during model training with train_ssd.py.