Free Google Shopping Product Image Checker

A Shopping listing can be disapproved even when the product, price, and feed are otherwise correct. The image is part of the product record, not decoration. This free checker gives ecommerce teams a fast first pass before images enter Google Merchant Center. Select up to ten JPEG, PNG, or WebP files and the browser checks them one at a time to limit memory use. It reads real pixel dimensions, file size, sampled edge color, and transparency without sending the files to Yito. The downloadable report separates objective failures from rules that still require a person to inspect the creative, so a green result never pretends to be official Google approval.

Google’s image requirements page now presents 500 by 500 pixels as the announced minimum and recommends 1500 by 1500, while the dated transition notice says warnings began on April 14, 2026 and enforcement starts on January 31, 2027. Before that enforcement date, this checker warns files below 500 instead of presenting them as approved; a file below the retained transition floor fails. It also checks the 64 megapixel and 16 MB ceilings, then samples edge color and transparency. Promotional overlays, borders, framing, category facts, and generative-AI metadata remain manual because pixels alone cannot prove them.

A browser-only rule checker cannot reliably identify every logo, price badge, promotional sentence, watermark, placeholder, generic image, or misleading product alteration. It also cannot know with certainty whether the photographed item occupies the recommended portion of the frame or whether every variant matches the submitted product ID. Those items appear as manual checks instead of invented pass marks. Review the full product, confirm that it is not cropped, remove calls to action and decorative borders, and make sure a multipack or bundle image represents the exact offer. Google remains the final decision maker after crawling the submitted URL.

Start with failures because they are deterministic. Re-export an undersized or oversized file from the original source rather than repeatedly compressing an already damaged JPEG. Then review warnings. A non-white edge can be intentional for certain lifestyle or additional images, but it is risky for a main image. Transparency may be converted to a dark or inconsistent background by downstream systems. Finally complete the manual checklist at normal size and at thumbnail size. Small text and subtle borders are often invisible while editing yet obvious in Shopping placements. Save the checked master and generate channel-specific derivatives from that approved source.

The main image should make product identity unambiguous. Keep the product centered, accurately colored, fully visible, and free from promotional overlays. Additional images can explain scale, show alternate angles, demonstrate use, or provide lifestyle context, but they should still correspond to the same item and variant. Do not force one image to do every job. Start with one primary image per SKU and no more than five SKUs. Use the downloadable findings to prioritize one product, then continue into Studio to repair or generate candidates. You or your team must confirm product identity and channel fit before publishing.

Google’s current guidance includes requirements for images created with generative AI, including preserving appropriate metadata that identifies AI-generated media. Editing tools and export pipelines can strip metadata without warning. This checker analyzes visible pixels and basic file properties; it does not certify provenance metadata. If an image was generated or materially edited with AI, keep the original output, retain generation records, record the model and version, and verify metadata with a dedicated metadata inspector before publishing. Yito Campaigns keep model, cost, version, and approval records so an agency can trace how each delivered asset was produced.

For one SKU, name the source image, checked image, repaired image, and final feed image consistently. Record the Merchant channel, locale, variant, and version rather than emailing files called final-final. For a catalog, run an image-link feed audit as well as individual pixel checks. The feed audit finds missing, duplicated, insecure, or malformed image URLs; this image checker finds problems inside a selected file. Together they catch different failure classes. Once the source passes, create a batch repair job, route outputs to a reviewer, approve each item, and export a Merchant CSV and ZIP under the SKU.