私はEsri India Living Atlasで利用可能な新しいText SAM（Segment Anything Model）を使用して、テキストプロンプト「Buildings」を使い、0.3m解像度の衛星画像から建物を検出しました。しかし、モデルはわずかしか建物を検出しませんでした。Text SAMモデルの検出精度を向上させるにはどうすればよいですか？さらに、より良い結果を得るためにSAMモデルをファインチューニングする方法はありますか？
The issue you're encountering may be due to the varying sizes of buildings in the satellite imagery. For instance, in an urban context, there are buildings of small, medium, and large footprints. Although deep learning models are typically trained on multiple imagery resolutions, the variation in building sizes can pose challenges. To improve detection, try manually adjusting the cell size in the environments tab of Detect Objects Using Deep Learning tool between 0.1 to 0.5. This should enhance detection across all building sizes. For more details, refer to this blog post: "https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/multiresolution-object-detection-with-text-sam/?srsltid=AfmBOoohMVcSgbSg-K_J2jtp9HCdvsALOdiEeKT02Ss-d15y2ZU_Pk6L"Regarding your second question, yes, the SAM model can be fine-tuned. The SamLoRA model, a variation of SAM now available in ArcGIS, introduces trainable layers called Low-Rank Adaptation (LoRA) to the frozen image encoder of SAM. You can train the SamLoRA model to detect specific features of interest for more accurate results.
サインインしたメンバーは投稿、更新のフォローなどができます。初めてですか？無料アカウントを登録してください。
Find useful guides, FAQs, and documents to help you navigate and make the most of Esri Community.
