# Model Type 別の Training Resolutions

トレーニング解像度はモデルの精度、推論速度、およびトレーニング時間に影響します。各モデルアーキテクチャにはこれらの要素のバランスを取るデフォルト解像度が設定されています。デフォルトでは、Roboflowは選択したモデルアーキテクチャに対するデフォルトのトレーニング解像度を推奨します。 

下の表は各モデルアーキテクチャとサイズごとのデフォルトのトレーニング解像度を示しています。新しいデータセットバージョンを作成する際にリサイズ前処理ステップを設定することで、これらのデフォルトを上書きできます。 [Dataset Version](https://docs.roboflow.com/roboflow/roboflow-jp/datasets/dataset-versions).

### Object Detection

<table><thead><tr><th>Model Type</th><th width="273.3359375">Family &#x26; Size</th><th>Default Training Resolution</th></tr></thead><tbody><tr><td>Object Detection</td><td>RF-DETR Nano</td><td>384×384</td></tr><tr><td>Object Detection</td><td>RF-DETR Small</td><td>512×512</td></tr><tr><td>Object Detection</td><td>RF-DETR Medium</td><td>576×576</td></tr><tr><td>Object Detection</td><td>RF-DETR Large</td><td>704×704</td></tr><tr><td>Object Detection</td><td>RF-DETR X Large</td><td>700x700</td></tr><tr><td>Object Detection</td><td>RF-DETR 2X Large</td><td>880x880</td></tr><tr><td>Object Detection</td><td>Roboflow 3.0 - Fast</td><td>640×640</td></tr><tr><td>Object Detection</td><td>Roboflow 3.0 - Accurate</td><td>640×640</td></tr><tr><td>Object Detection</td><td>Roboflow 3.0 - Medium</td><td>640×640</td></tr><tr><td>Object Detection</td><td>Roboflow 3.0 - Large</td><td>640×640</td></tr><tr><td>Object Detection</td><td>Roboflow 3.0 - Extra Large</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv26(n/s/m/l/x)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv12 (n/s/m/l/x)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv11 (n/s/m/l/x)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv10 (n/s/m/b/l/x)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv9 (s/m/c/e)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv8 (n/s/m/l/x)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv5 (n/s/m/l/x)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLOv7 (legacy)</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLO‑NAS Small</td><td>640×640</td></tr><tr><td>Object Detection</td><td>YOLO‑NAS Medium</td><td>640×640</td></tr><tr><td>Object Detection</td><td>Roboflow Instant</td><td>1008x1008</td></tr></tbody></table>

### Instance Segmentation

<table><thead><tr><th>Model Type</th><th width="272.8203125">Family &#x26; Size</th><th>Default Training Resolution</th></tr></thead><tbody><tr><td>Instance Segmentation</td><td>RF-DETR Seg Nano</td><td>312x312</td></tr><tr><td>Instance Segmentation</td><td>RF-DETR Seg Small</td><td>384x384</td></tr><tr><td>Instance Segmentation</td><td>RF-DETR Seg Medium</td><td>432x432</td></tr><tr><td>Instance Segmentation</td><td>RF-DETR Seg Large</td><td>504x504</td></tr><tr><td>Instance Segmentation</td><td>RF-DETR Seg X Large</td><td>624x624</td></tr><tr><td>Instance Segmentation</td><td>RF-DETR Seg 2X Large</td><td>768x768</td></tr><tr><td>Instance Segmentation</td><td>Roboflow 3.0 - Fast (Seg)</td><td>640×640</td></tr><tr><td>Instance Segmentation</td><td>Roboflow 3.0 - Accurate (Seg)</td><td>640×640</td></tr><tr><td>Instance Segmentation</td><td>Roboflow 3.0 - Medium (Seg)</td><td>640×640</td></tr><tr><td>Instance Segmentation</td><td>Roboflow 3.0 - Large (Seg)</td><td>640×640</td></tr><tr><td>Instance Segmentation</td><td>Roboflow 3.0 - Extra Large (Seg)</td><td>640×640</td></tr><tr><td>Instance Segmentation</td><td>YOLO-seg (v8/10/11/12)</td><td>640×640</td></tr><tr><td>Instance Segmentation</td><td>SAM3 (Segment Anything 3)</td><td>1008x1008</td></tr><tr><td>Instance Segmentation</td><td>Semantic segmentation (DeepLabV3+)</td><td>≥ 512×512</td></tr></tbody></table>

### Classification & Pose

<table><thead><tr><th>Model Type</th><th width="272.66796875">Family &#x26; Size</th><th>Default Training Resolution</th></tr></thead><tbody><tr><td>Classification &#x26; Pose</td><td>Resnet-18/34/50</td><td>224x224</td></tr><tr><td>Classification &#x26; Pose</td><td>YOLO-cls (v8/11)</td><td>224x224</td></tr><tr><td>Classification &#x26; Pose</td><td>Vision Transformer (ViT)</td><td>224x224</td></tr><tr><td>Classification &#x26; Pose</td><td>YOLO-pose (keypoints)</td><td>640x640</td></tr></tbody></table>

### Multimodal/VLM

<table><thead><tr><th>Model Type</th><th width="272.96484375">Family &#x26; Size</th><th>Default Training Resolution</th></tr></thead><tbody><tr><td>Multimodal/VLM</td><td>PaliGemma 2 - 3 B</td><td>448x448</td></tr><tr><td>Multimodal/VLM</td><td>PaliGemma 2 - 10 B/28 B</td><td>448x448</td></tr><tr><td>Multimodal/VLM</td><td>Florence-2</td><td>448x448</td></tr><tr><td>Multimodal/VLM</td><td>QWEN 2.5 VL</td><td>448x448</td></tr><tr><td>Multimodal/VLM</td><td>SmolVLM2</td><td>384x384</td></tr></tbody></table>