I Read Every Adobe Rejection Reason. Most of Them Are One Rule

说明与用途

自动化元数据、CSV 生成和上传跟踪将平台拒绝率降低到仅图像质量问题。

问题

Frequent rejections from Adobe and other platforms due to metadata and upload errors, despite high-quality images.

结果

Rejections dropped from almost all assets to only image quality issues; 39 finished products produced from a 60‑asset batch, 856 MB of output, and a 19,000‑line pipeline with 300+ pages of research notes.

工作流

• 1. Read all rejection emails and categorize reasons into a spreadsheet.
• 2. Identify that most rejections stem from metadata errors (title, keywords, filename).
• 3. Create a titles.json file with human‑written titles and keywords per asset.
• 4. Write make_metadata.py to generate platform‑specific CSV headers and metadata rows.
• 5. Use make_metadata.py with --platform flag to produce adobe.csv, vecteezy.csv, dreamstime.csv.
• 6. Implement upload_tracker.py to log pending, done, and status of uploads, preventing duplicate submissions.
• 7. Add memory‑management steps to the generation pipeline: single‑process execution, gc.collect(), peak RSS monitoring, subprocess rendering, and low‑dpi preview checks.
• 8. Package the scripts into a starter kit with dependencies Pillow and reportlab.
• 9. Submit batches using the generated CSVs and tracker to ensure compliance and avoid rejections.

证据

most of them trace back to one rule about metadata, not about the art.

来源

I stopped letting the pipeline invent titles. Now there is a titles.json file, one real title and one real keyword set per asset, written by a human who looked at the image.

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I wrote one script, make_metadata.py, that takes a folder of files and emits the exact header each platform demands, nothing else.

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The fix is a dumb ledger. upload_tracker.py is a plain file that remembers what you have already posted.

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One process at a time. Never fan out image jobs in parallel on a single core.

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A batch that used to die at asset 30 now runs to the end.

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