Product image too small? Check the rest of the gallery

The main product photo can be the obvious choice for a social post without being the only file worth opening. An analysis of archived Amazon listing metadata shows how often another recorded image clears a chosen pixel threshold when the main image does not.

An original analysis of Amazon Berkeley Objects listing and image metadata archived in June 2021.

Abstract

When a product’s main photo is too small for your planned layout, check its other approved images before deciding how to proceed. Among 5,985 archived amazon.com listings whose main image fell below our chosen 1,080 × 1,350 pixel condition, 3,073 had at least one recorded secondary image that met it: 51.3%. The comparison covers 19,542 listings with the required metadata; 74 others lacked a main-image reference.[1]

Meeting the condition means the recorded width and height allow proportional filling and cropping without enlargement. It does not establish that the product survives the crop or that the photo looks good. These are June 2021 archive records, not an estimate of today’s market. The practical implication is to separate finding a large-enough candidate from approving the image that customers will see.

Imagine a small business preparing a social post for a new product. The designer starts with the main photo from the product page. It already represents the item on the website, so it feels like the natural place to start. Then the planned portrait layout needs more pixels than that file contains.

At that point, the conversation can turn straight to enlarging the image, changing the design or arranging another shoot. There is an earlier question worth asking: what else is already in the approved product gallery? A different file might offer enough pixels, even if its angle, background or purpose needs a closer look.

If a brief contains only a product-page link, ask which original files the brand can supply. The view chosen for a listing has one job; a campaign may need a different angle or detail. An asset request should name that need. “Please send the approved original gallery for this product and color” gives the creator a clearer starting point than “make this image bigger.”

We examined that question using Amazon Berkeley Objects, a public dataset that links product listings to their main and other catalog images.[1][2] Rather than counting large photos across the whole archive, we paired each listing’s main image with its own recorded alternatives. The result speaks to a specific handoff between a business and a creator: deciding which existing files deserve inspection before requesting new material.

The first photo is one view of the product

Conceptual illustration of an open folder and several cards showing a teal bottle from different angles, with a translucent portrait crop frame in front.
An illustrated gallery, not photographs evaluated in the study. Recorded dimensions identify candidates; opening the files is a separate step.

Enough pixels is the beginning of the decision

To fill a portrait canvas, a photo must cover its full width and height. If the photo has a different shape, part of it will extend beyond the canvas and be cropped. The scale needed is the larger of two ratios: canvas width divided by image width, and canvas height divided by image height.

For our chosen canvas, a 1,350 × 1,080 landscape image fails the condition. Its width is sufficient, but its height is not. Having the same total number of pixels as the canvas does not solve that mismatch. The calculation keeps the recorded orientation; it does not rotate the photo, add borders or generate missing background.

Keeping the whole photo visible asks a different question. A designer could shrink an image to fit inside the canvas and leave space around it, instead of filling every edge. That may suit a layout with a background and a caption, but our results do not count that approach. Decide whether the brief requires a photo that fills the canvas or allows space around it before using a size check to reject a file.

For example, a 1,800 × 2,400 image can fill the canvas after shrinking to 60% of its original dimensions. The result is 1,080 × 1,440, leaving 90 pixels of height to crop in the resized image. That arithmetic establishes a candidate, not a safe composition. A handle, label or product edge could sit in the area that would disappear.

This is why “large enough” and “ready to publish” should be separate decisions. We analyzed dimension records, not the image pixels. A secondary image could be a close-up, packaging view or instruction panel. Someone still needs to open the actual file and decide whether it communicates the intended product story.

The result changed with the comparison

We also specified a square canvas of 1,080 × 1,080 before examining the results. With that condition, 5,107 main images fell short and 2,544 of those listings had a qualifying secondary image: 49.8%. The portrait result was therefore not the only chosen shape for which the paired comparison found a substantial group of alternatives.

Some listings refer to exactly the same main image and set of other images. Counting each such combination once reduced the portrait comparison to 5,204 combinations with a main-image failure. Of those, 2,641 had a qualifying secondary image, or 50.7%. Repeated image combinations did not account for the headline pattern.

Expanding the analysis to every eligible marketplace listing in the archive produced a lower share: 14,890 of 37,934 main-image failures, or 39.3%. That comparison covers a different population. Products can recur across marketplaces, and their mix differs. The variation is a reason to check your own files, rather than adopt 51.3% as an expected result for your catalog.

The share depended on the canvas and the comparison group

Each row counts cases where the main image missed its stated condition. The numerator counts those with a qualifying recorded secondary image. The image-combination row counts distinct bundles; the other rows count listings.

Data table: qualifying secondary counts, main-image failure denominators and percentages for primary portrait, square, distinct image combinations and all-marketplace portrait comparisons
ComparisonWith a qualifying secondaryMain image below conditionShare with a qualifying secondary
amazon.com, portrait: listings3,0735,98551.3%
amazon.com, square: listings2,5445,10749.8%
amazon.com, portrait: unique image combinations2,6415,20450.7%
All marketplaces, portrait: listings14,89037,93439.3%
Source: Amazon Berkeley Objects metadata archived in June 2021; Influence IQ analysis.[1] Portrait is 1,080 × 1,350 pixels; square is 1,080 × 1,080. A unique image combination contains the same main image and set of distinct secondary images. All-marketplace listing counts can include products in several markets. These are different comparison groups, not estimates of current national markets.

Each row answers its own question. The square test changes the chosen canvas while keeping the eligible listings. The image-combination check changes how repeated references are counted. The all-marketplace check changes the source population and can count a product in several markets. Reading the rows as a single ranking would lose those differences.

For someone managing a catalog, the important point is that many listings still had an alternative after repeated image combinations were counted only once. The exact proportion does not necessarily carry over to another catalog. A team’s products, photography and filing practices may differ from this archive, so the practical recommendation is an inspection step rather than a forecast of how many usable replacements the team will find.

Give the creator a choice they can actually use

For a business briefing a creator, the finding suggests a small change to the asset handoff. Provide access to the approved originals for the relevant product, rather than assuming the main website photo defines the available options. Ask the creator to shortlist files for the intended layout and explain what each one would show.

For the creator, make the request specific enough to answer in one handoff: the exact product variant, the approved original files, any restrictions on their use, and the person who can approve a changed crop. For the business, retain the file names when sharing the gallery so the selected view can be traced back to its source. Use those file names when discussing a different crop or requesting approval.

Start with the message. A post introducing the whole product needs a different view from a post explaining a fastening, texture or assembly step. A large detail shot can be useful for the second job and unsuitable for the first. The size check should help the creative decision, not choose the subject of the post.

Next, check the actual file dimensions against the planned canvas. Then open the candidate and test the crop with the intended text or graphic elements in place. Look for the product edges, identifying details and any information the viewer needs. Review the exported result as well as the source file, because the layout is what customers will encounter.

Keep a short reason alongside the selection. “Enough pixels; label remains readable after crop” tells a teammate more than “use photo two.” Where the crop fails, record the missing view or framing. That gives a photographer or brand contact a concrete request instead of a vague demand for something bigger.

The handoff can also return a useful rejection. If a secondary file is large enough but shows the wrong color, keep that reason. If a detail shot works for one post but cannot introduce the whole product, save the distinction. It helps the next brief start with what the asset actually shows.

This workflow applies to your own approved assets and the permissions for the campaign. A photo appearing on a public product page does not by itself establish permission to reuse it. The dataset’s research license is separate from the rights needed for an individual business’s marketing materials.[4][6]

Review the gallery before choosing the composition

Three conceptual scenes: a folder of bottle images, image cards beside a portrait frame and measuring edge, and a bottle image with a crop frame and magnifying glass.
  1. Request the original gallery

    Identify the correct product and the approved files.

  2. Compare width and height

    Check both directions against the intended canvas.

  3. Inspect the actual composition

    Review the crop, product details and final export.

Request the approved originals, compare their dimensions with the layout, then inspect the candidate in the composition.

A fictional bottle campaign, from file check to approval

Consider a fictional refillable-bottle brand preparing a portrait product introduction. Its chosen canvas is 1,080 × 1,350 pixels. The designer has a 900 × 1,200 main image and a 1,800 × 2,400 alternative. These invented files illustrate the calculation; they are not images we inspected in the archive.

The main image needs a scale of 1.2 to cover the width. At that scale it would become 1,080 × 1,440, so it would require enlargement and a 90-pixel vertical crop. It fails this study’s no-enlargement condition. That result does not decide whether enlargement would be acceptable for another creative brief; it answers the rule chosen here.

The alternative needs a scale of 0.6. It also becomes 1,080 × 1,440, but by shrinking. It passes the pixel condition and still requires the same total vertical crop. If centered, 45 pixels would be removed from the top and 45 from the bottom of the resized image.

Suppose the folder also contains a 2,000 × 1,000 landscape image. Its wider edge does not make it a better answer to this portrait brief: reaching 1,350 pixels high would require enlargement. This third fictional file illustrates why sorting candidates by their longest edge alone would miss the relevant condition.

The designer now checks whether the cap, bottle base and label remain visible. Suppose the lower edge of the label would be cut off. The numerical pass does not override that observation. The designer can try a different crop position and inspect it, choose another approved view, or ask for a file with more useful space around the bottle.

If the revised composition works, the team records the selected file, crop position and final approval. If it does not, the request for new material becomes precise: a full-bottle view with enough room above and below for this portrait layout. The selected file must work for that message as well as that canvas.

A gallery review sheet for your next product post

Use the following fields for one product and one intended layout. Duplicate the candidate entry for each file you review. The downloadable package contains the complete blank sheet and the worked example, with no sign-up required.[5]

Brief: product and version ______; campaign or post ______; intended message ______; person reviewing ______; date ______.

Layout: width ______ pixels; height ______ pixels; intended placement ______; product features that must remain visible ______; planned text or graphic elements ______.

Candidate file: name or link ______; source and permission reference ______; original width ______ pixels; original height ______ pixels; main image or alternative ______; view or detail shown ______.

Size calculation: layout width ÷ file width = ______; layout height ÷ file height = ______; larger ratio = ______. A ratio of 1 or less meets the no-enlargement condition for filling this canvas. A larger ratio needs enlargement. Unknown dimensions stay “unknown” until checked.

Crop check: resized width ______; resized height ______; horizontal pixels to remove ______; vertical pixels to remove ______; crop position ______. Open the file and mark whether the product, version, necessary details and intended message remain correct ______.

Export review: final file ______; visible quality and legibility checked ______; permission for this use confirmed ______; approved by ______; approved date ______.

Decision: use this candidate / inspect another file / change the layout / request new material. Reason ______. If requesting material, describe the missing view, framing or detail ______.

Keep the finished sheet with the selected asset so another person can understand the choice. A recorded size pass is useful evidence for the handoff, but the final decision belongs to the inspected composition and approved use.

No sign-up required. The ZIP includes aggregate results, calculation code, the blank gallery review sheet and the fictional example. Technical reproduction instructions are in English. Source metadata are credited to Amazon.com and the ABO dataset builders under CC BY 4.0.

How we compared the archived images

We read all 16 listing-metadata files and the original-dimension image table in Amazon Berkeley Objects. The producer describes the archive as 147,702 listings and 398,212 unique catalog images.[1][2] We used the listing’s recorded main and other image references, not the archive’s smaller display-image versions.

The primary unit was a unique item ID and marketplace-domain pair with the exact domain amazon.com. Of 19,616 such listings, 74 lacked a main-image reference and were excluded. The remaining 19,542 had the required dimensions, giving 99.6% coverage. Missing data were not counted as small images.

Within each listing, we removed repeated secondary references and kept the main image in its stated role. The paired table asks whether the main meets the chosen dimensions and whether any distinct recorded secondary does. Three repeated secondary references were removed in the primary group. Conflicting image or listing identifiers would stop the calculation rather than be resolved by choosing a convenient row.

The portrait and square dimensions, gallery-count groups and supporting comparisons were fixed before the outcomes were examined. The listing files carry June 16, 2021 last-modified dates and the image table June 17; those dates identify the archived objects, not the precise date each photograph was taken. These listing counts are not a random sample of today’s businesses, so we report exact counts and shares rather than population confidence intervals. The accompanying package provides the calculation and source details.[3][5]

Sources and notes

  1. Amazon Berkeley Objects (ABO), product-listing and catalog-image metadata. Influence IQ paired each archived listing’s main image with its distinct recorded secondary references and calculated dimension conditions for two fixed example canvases. The primary domain is exactly amazon.com. The analysis uses original-image width and height metadata; no product-photo pixels were inspected. The source files and calculations are documented in the analysis package.
  2. Collins and colleagues, ABO: Dataset and Benchmarks for Real-World 3D Object Understanding, CVPR 2022, section 3. The producer describes worldwide listings, structured product metadata and catalog images. Its computer-vision benchmarks address different questions from this article’s within-listing dimension comparison.
  3. The producer’s listing documentation identifies a listing by item_id and domain_name and distinguishes main_image_id from other_image_id. An absent secondary-reference field means no secondary reference is recorded; it is not evidence about every image the seller owns. The image documentation defines width and height as original-image dimensions. Duplicate secondary IDs were collapsed before classifying each listing. The package includes the full eligibility counts and all six paired tables, including the square, unique-combination and all-marketplace checks.
  4. Data credit: Amazon.com. Dataset, archives and benchmark builders: Matthieu Guillaumin, Thomas Dideriksen, Kenan Deng, Himanshu Arora, Arnab Dhua, Xi (Brian) Zhang and Tomas Yago-Vicente (Amazon.com); Jasmine Collins, Shubham Goel and Jitendra Malik (UC Berkeley). The current producer site licenses ABO under Creative Commons Attribution 4.0 International. Influence IQ joined and filtered metadata, deduplicated image references and produced aggregate calculations and interpretation. No endorsement by Amazon.com or the dataset builders is implied. Source links and the license are included with the analysis package.
  5. Influence IQ analysis package: fixed comparison rules, source manifest, analysis code, aggregate results, and the complete gallery review sheet and fictional worked example. Results describe archived metadata and geometric candidate availability. The download does not include the original image archive.
  6. Creative Commons Attribution 4.0 International license for the Amazon Berkeley Objects source data. The current producer license governs this reuse; source attribution and a description of the transformations accompany the aggregate outputs.

All language editions describe the same June 2021 archive and primary amazon.com comparison. A translation is not a new study of the market where that language is spoken.

Develop the product story and its layout