As datasets grow, the need for automated methods to ensure dataset quality arises. This report presents an experiment conducted on the MSCOCO train2017 dataset to identify image outliers using a force-directed graph built from a co-occurrence context, focusing on the mean average precision and average precision. The experiment involved placing anomaly scores on images using Euclidean distance and k-means clustering, creating subsets where a percentage of images withthe highest anomaly scores were removed. You Only Look Once version 8 models were trained on each subset, and the results showed a promising increase in performance compared to randomlyr emoving images. However, the increase was relatively small, and further research is needed. Interms of future work, other methods of identifying outliers, other datasets, and investigating the uses of contextual information in other areas are discussed.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-22868 |
| Date | January 2023 |
| Creators | Pettersson, Patrik, Gomez Palomäki, José Gabriel |
| Publisher | Högskolan i Skövde, Institutionen för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0012 seconds