JSON file not found when using custom dataset in detectron2

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04-10-2024

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"JSON File Not Found": Debugging Detectron2 Custom Datasets

The Problem:

You're trying to train a Detectron2 model on your own custom dataset, and you're running into the dreaded "JSON file not found" error. This frustrating message can leave you scratching your head, wondering where your meticulously crafted annotations went.

Scenario & Code Example:

Let's imagine you're working with a dataset of images of flowers, and you've carefully annotated them using COCO format in a JSON file named flower_dataset.json. You've configured your Detectron2 training script like this:

from detectron2.data import DatasetCatalog, MetadataCatalog

def get_flower_dataset_dicts(img_dir):
  # Your function to read the annotations from 'flower_dataset.json'
  # and create a list of dictionaries in the Detectron2 format.

DatasetCatalog.register("flower_dataset", lambda: get_flower_dataset_dicts(img_dir))
MetadataCatalog.get("flower_dataset").set(thing_classes=["rose", "tulip", "daisy"])

# Rest of your training code

Now, when you run the training script, you're met with the dreaded "JSON file not found" error.

Analysis & Insights:

The "JSON file not found" error in Detectron2 usually boils down to one of these common culprits:

1. Incorrect File Path: The most frequent cause is a simple mistake in the path to your flower_dataset.json file. Make sure the path provided in your get_flower_dataset_dicts function is correct and points to the exact location of your annotation file.
2. File Name Mismatch: Double-check that the name of your JSON file (e.g., flower_dataset.json) matches the one referenced in your code (e.g., 'flower_dataset').
3. Wrong Working Directory: The working directory where your script is run might not be the same as the directory containing your JSON file. Ensure that your script is executed from the correct directory or provide an absolute path to your JSON file.
4. Missing Dependencies: Although less likely, make sure you have the necessary libraries installed, including json and os which are used for reading and manipulating files.

Debugging Tips:

• Print Statements: Add print statements inside your get_flower_dataset_dicts function to verify the path being used and ensure your function is reading the file contents.
• Direct Access: Try accessing the JSON file directly using os.path.exists() or open() to see if it can be found.
• Absolute Paths: For simplicity, use absolute paths instead of relative ones.

Example:

If your JSON file is located at /home/user/datasets/flower_dataset/flower_dataset.json, modify your code like this:

from detectron2.data import DatasetCatalog, MetadataCatalog
import json
import os

def get_flower_dataset_dicts(img_dir):
  json_file = "/home/user/datasets/flower_dataset/flower_dataset.json" # Absolute path

  with open(json_file, 'r') as f:
    annotations = json.load(f)

  # ... rest of your function to create dictionaries ...

DatasetCatalog.register("flower_dataset", lambda: get_flower_dataset_dicts(img_dir))
MetadataCatalog.get("flower_dataset").set(thing_classes=["rose", "tulip", "daisy"])

# Rest of your training code

Additional Value:

• This error can also occur if your JSON file is in a format Detectron2 does not understand. Make sure your annotations are in the correct COCO format.
• The Detectron2 documentation provides excellent resources for understanding dataset registration and annotation formats. Refer to the Dataset Registration and COCO format sections for detailed information.

By carefully checking your file paths, file names, and working directory, you can overcome the "JSON file not found" error and successfully train your custom Detectron2 model.