How This Helps

Export datasets directly using curl without needing Python or SDKs. Ideal for automation scripts or CLI-based workflows.

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Initiate Export

Use a GET request to trigger export: 
curl -X GET "https://app.visual-layer.com/api/v1/dataset/<DATASET_ID>/export_context_async" \
  -H "Accept: application/json, text/plain, */*" \
  -G \
  --data-urlencode "file_name=export.zip" \
  --data-urlencode "export_format=json" \
  --data-urlencode "include_images=false"
  • <DATASET_ID>: Your dataset ID
  • export_format: Supported values are json or parquet
  • include_images: true or false
  • file_name: Name for the output ZIP

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Sample Response

{
  "id": "fdb84834-d19b-4797-861a-d48b7a16f908"
}

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Poll Export Status

Use the export id to monitor progress: 
curl -X GET "https://app.visual-layer.com/api/v1/dataset/<DATASET_ID>/export_status?export_task_id=<TASK_ID>" \
  -H "Accept: application/json, text/plain, */*"
Response includes a download URI once "status": "COMPLETED".

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Download ZIP

When ready, download your dataset: 
curl -L "<DOWNLOAD_URI>" --output exported_dataset.zip
Use the -L flag to follow redirects (required for S3 links).

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Unzip and Explore

Extract the ZIP archive: 
unzip exported_dataset.zip -d exported_dataset
Example folder structure: 
exported_dataset/
├── metadata.parquet
└── images/
    ├── image1.jpg
    ├── image2.jpg

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Full Automation Script

#!/bin/bash

DATASET_ID="your-dataset-id"
FILENAME="export.zip"

echo "Initiating export..."
EXPORT_TASK_RESPONSE=$(curl -s -G "https://app.visual-layer.com/api/v1/dataset/$DATASET_ID/export_context_async" \
  -H "Accept: application/json, text/plain, */*" \
  --data-urlencode "file_name=$FILENAME" \
  --data-urlencode "export_format=json" \
  --data-urlencode "include_images=false")

EXPORT_TASK_ID=$(echo $EXPORT_TASK_RESPONSE | grep -o '"id":"[^"]*' | cut -d':' -f2 | tr -d '"')

echo "Export Task ID: $EXPORT_TASK_ID"

echo "Waiting for export to complete..."
STATUS="PENDING"
while [ "$STATUS" != "COMPLETED" ]; do
  STATUS_RESPONSE=$(curl -s "https://app.visual-layer.com/api/v1/dataset/$DATASET_ID/export_status?export_task_id=$EXPORT_TASK_ID" \
    -H "Accept: application/json, text/plain, */*")
  STATUS=$(echo $STATUS_RESPONSE | grep -o '"status":"[^"]*' | cut -d':' -f2 | tr -d '"')
  echo "Status: $STATUS"
  [ "$STATUS" = "FAILED" ] && echo "Export failed." && exit 1
  [ "$STATUS" != "COMPLETED" ] && sleep 5
done

DOWNLOAD_URI=$(echo $STATUS_RESPONSE | grep -o '"download_uri":"[^"]*' | cut -d':' -f2- | sed 's/^"//' | sed 's/"$//' | sed 's/\\//g')
echo "Downloading ZIP..."
curl -L "$DOWNLOAD_URI" --output $FILENAME

echo "Unzipping..."
unzip $FILENAME -d exported_dataset

echo "Done!"

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Filter Exported Data by Uniqueness

Use pandas to find the most unique images: 
import pandas as pd

df = pd.read_parquet("exported_dataset/metadata.parquet")
top_unique_images = df.sort_values(by="uniqueness_score", ascending=False).head(100)
top_unique_images.to_csv("top_unique_images.csv", index=False)

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Copy Filtered Images

import os, shutil

os.makedirs("top_unique_images", exist_ok=True)
for fname in top_unique_images["image_filename"]:
    shutil.copy(f"exported_dataset/images/{fname}", f"top_unique_images/{fname}")

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Summary

  • Initiated export
  • Polled export status
  • Downloaded and unzipped archive
  • Parsed metadata using pandas
  • Used uniqueness_score for image filtering