# Flask app with RF-DETR, live uploads, and cached results to save credits **URL:** \ **Category:** ✨ Show & Tell\ **Created:** [October 28, 2026, 3:08pm UTC](https://discuss.roboflow.com/t/flask-app-with-rf-detr-live-uploads-and-cached-results-to-save-credits/12618 "2026-10-28T15:08:11Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![davidcarroll](https://yyz1.discourse-cdn.com/flex029/user_avatar/discuss.roboflow.com/davidcarroll/32/10016_2.png) [@davidcarroll](https://discuss.roboflow.com/u/davidcarroll)\ **Post date:** [October 28, 2026, 3:08pm UTC](https://discuss.roboflow.com/t/flask-app-with-rf-detr-live-uploads-and-cached-results-to-save-credits/12618/1 "2026-10-28T15:08:11Z")
# Show RF: RF-DETR Flask Detector Hi everyone, I just finished my first project with Roboflow, RF-DETR Flask Detector, a Flask web app for object detection using RF-DETR through the serverless inference API. You can upload your own image and get detections back, or browse a gallery of sample images from the COCO dataset with results already loaded. Since I’m on the free plan, credits were a real constraint, so I added a caching layer using AWS CloudFront to avoid repeat requests. That made the app faster and a lot cheaper to run. Live demo: [What's in the picture?](https://davidcarroll.cloud/roboflow/) Code: [GitHub - david-carroll-aws/roboflow-detection-demo: Flask web app for AI-powered object detection and recognition, using Roboflow's RF-DETR transformer model via the serverless inference API, with cached results and live image uploads · GitHub](https://github.com/david-carroll-aws/roboflow-detection-demo) Since this is my first time working with Roboflow, I’d love feedback. Is anyone else caching inference results, or handling it a different way? Any tips for getting more out of RF-DETR would be great. Cheers! ![picture-detect](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/2X/3/3bf167c50d73540410ff8bbf28f6c955ffa40640.png)