# Model Type के अनुसार Training Resolutions

प्रशिक्षण रेज़ोल्यूशन मॉडल की सटीकता, इनफरेंस गति और प्रशिक्षण समय को प्रभावित करता है। प्रत्येक मॉडल आर्किटेक्चर का एक डिफ़ॉल्ट रेज़ोल्यूशन होता है जो इन पहलुओं का संतुलन बनाता है। डिफ़ॉल्ट रूप से, Roboflow चयनित मॉडल आर्किटेक्चर के लिए डिफ़ॉल्ट प्रशिक्षण रेज़ोल्यूशन सुझाता है। 

नीचे की तालिका प्रत्येक मॉडल आर्किटेक्चर और आकार के लिए डिफ़ॉल्ट प्रशिक्षण रेज़ोल्यूशन दिखाती है। आप नए बनाते समय resize preprocessing चरण को कॉन्फ़िगर करके इन डिफ़ॉल्ट्स को ओवरराइड कर सकते हैं [Dataset Version](https://docs.roboflow.com/roboflow/roboflow-hi/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>