# When using Platform deployment, intermittently, returned predictions contain false positives **URL:** \ **Category:** Ultraltyics Platform\ **Created:** [September 26, 2026, 2:34am UTC](https://community.ultralytics.com/t/when-using-platform-deployment-intermittently-returned-predictions-contain-false-positives/2241 "2026-09-26T02:34:23Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![stevem](https://avatars.discourse-cdn.com/v4/letter/s/a183cd/32.png) [@stevem](https://community.ultralytics.com/u/stevem)\ **Post date:** [September 26, 2026, 2:34am UTC](https://community.ultralytics.com/t/when-using-platform-deployment-intermittently-returned-predictions-contain-false-positives/2241/1 "2026-09-26T02:34:23Z")
Hi, I am using the Platform Deployment of a model that I have created. (Model is custom trained based on Yolo26s-seg 8.4.163) Intermittently, I have found that the Endpoint returns a false positive prediction. When I check the submitted image, the positive class is not there. I then checked the monitoring page for the Endpoint to check latest results of the saved images of what was submitted to the Endpoint for prediction. None of images showed that a prediction was found. (correct) Investigation: 1. When I use the platform to to manually “drop” the image into the Endpoint to run the prediction, correctly, no prediction is found. 2. I have used same configuration (Confidence, IOU and Image size) in both cases. 3. When I rerun the prediction via the Endpoint, I get a false positive again. Questions: Should I expect the same results when using the Endpoint and manually dropping images? Should I expect that the predictions in the saved images submitted to Endpoint be the same as the results returned from the Endpoint? Thanks, Steve ---
**Author:** ![Toxite](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/toxite/32/123_2.png) [@Toxite](https://community.ultralytics.com/u/Toxite)\ **Post date:** [September 26, 2026, 8:54am UTC](https://community.ultralytics.com/t/when-using-platform-deployment-intermittently-returned-predictions-contain-false-positives/2241/2 "2026-09-26T08:54:32Z")
Hello. Can you provide the code you used to run the prediction? ---
**Author:** ![stevem](https://avatars.discourse-cdn.com/v4/letter/s/a183cd/32.png) [@stevem](https://community.ultralytics.com/u/stevem)\ **Post date:** [September 26, 2026, 1:02pm UTC](https://community.ultralytics.com/t/when-using-platform-deployment-intermittently-returned-predictions-contain-false-positives/2241/3 "2026-09-26T13:02:33Z")
```auto var client = new RestClient("https://predict-6ab663062f703b3a47ad0790-dproatj77a-km.a.run.app"); var request = new RestRequest("", Method.Post); request.AddHeader("Accept", "application/json"); request.AddHeader("Content-Type", "multipart/form-data"); request.AddHeader("authorization", "################"); request.AddHeader("Content-Type", "multipart/form-data"); request.AddFile("file", FileName, "image/png"); request.AddParameter("conf", "0.15"); request.AddParameter("iou", "0.70"); request.AddParameter("imgsz", "640"); var response = await client.ExecuteAsync(request); GetPrediction1 myDeserializedClass = JsonConvert.DeserializeObject(response.Content); ``` As I am new to Yolo, I have a question please. With regards to the parameter ‘imgsz’ does the actual image that I supply to the Endpoint have to be 640, or does the Endpoint resize it? This is because I have found a difference in the confidence values when I submit the image via the endpoint vs when I “drop the image” on the endpoint. Maybe, when I “drop” the image on the endpoint, the image is first resized? But, this does not explain when I get a positive prediction, but the monitoring page of the endpoint shows the image without a prediction found. ---
**Author:** ![pderrenger](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/pderrenger/32/73_2.png) [@pderrenger](https://community.ultralytics.com/u/pderrenger)\ **Post date:** [September 27, 2026, 1:09am UTC](https://community.ultralytics.com/t/when-using-platform-deployment-intermittently-returned-predictions-contain-false-positives/2241/4 "2026-09-27T01:09:44Z")
Thanks for the code, Steve. **Your image does not have to be 640 × 640.** `imgsz=640` sets the inference input size; the model’s preprocessing handles images of other dimensions. There is one important difference between your two tests: the deployment’s browser **Predict** tab resizes an image _before uploading it_, while your C# request uploads the original file. That can change confidence scores, particularly for a detection near your `conf=0.15` threshold. The [Platform inference documentation](https://docs.ultralytics.com/platform/deploy/inference) describes that browser-side resize. For a closer comparison, send the exact same resized image through both routes to the **same deployment** with the same settings, then compare their raw JSON responses. The Monitoring gallery is **not a record of every response** : it captures examples on a best-effort basis, at most one per second. So a gallery image with no overlay does not, by itself, establish that it corresponds to the request that returned a positive result. If you can match a particular monitored example to a particular API response and they disagree, that warrants investigation. The [Monitoring documentation](https://docs.ultralytics.com/platform/deploy/monitoring) explains the capture limits. One thing to check in your snippet: the documented endpoint path ends in `/predict`, whereas the URL shown does not. Please confirm the actual URL being called, remove the duplicate `Content-Type` header and let RestSharp set the multipart boundary, and log the HTTP status and **raw** `response.Content` before deserializing it. If the discrepancy persists, please share a reproducible image, both raw responses, the request times, and the Ultralytics version reported in the responses—with your API key removed—after checking on the latest available version.