๐๐ ๐ฃ๐ฟ๐ฒโ๐๐ฟ๐ฎ๐ฑ๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ ๐๐ผ๐น๐น๐ฒ๐ฐ๐๐ถ๐ฏ๐น๐ฒ๐
You open your inbox to a flood of photos from collectors. Manually inspecting each image takes hours. It introduces bias. You miss good deals.
Turn snapshots into objective pre-grades. Focus your time on high-value deals.
Use a computer vision pipeline. It normalizes photos. It detects defects. It maps findings to a grade. The model sees the same features for every card. This removes inconsistency.
Use the Glare Removal model on Replicate. It removes highlights from sleeves or lighting. This stops false scratch reports. It makes grade predictions stable.
A seller uploads a card photo to Google Drive. A Make scenario watches the folder. It sends the image to Replicate. Then it sends the clean photo to Hugging Face. The model gives a PSA grade and a confidence score. You get a Slack notification. You decide if you want the physical lot.
- Set up intake. Use Make to grab photos from Google Drive. Crop cards to a standard size.
- Link the grading model. Use the Replicate API for glare removal. Use a Hugging Face model to find defects.
- Automate lead qualification. Push results to Airtable. Send an email to the seller.
Standard intake and AI grading create a fast pipeline. You get uniform defect detection. You qualify leads fast. Focus on cards moving the market.
Source: https://dev.to/ken_deng_ai/title-k4h Optional learning community: https://t.me/GyaanSetuAi