Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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import torch
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.utils import ops
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__all__ = ["NASValidator"]
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class NASValidator(DetectionValidator):
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"""Ultralytics YOLO NAS Validator for object detection.
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Extends DetectionValidator from the Ultralytics models package and is designed to post-process the raw predictions
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generated by YOLO NAS models. It performs non-maximum suppression to remove overlapping and low-confidence boxes,
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ultimately producing the final detections.
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Attributes:
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args (Namespace): Namespace containing various configurations for post-processing, such as confidence and IoU
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thresholds.
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lb (torch.Tensor): Optional tensor for multilabel NMS.
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Examples:
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>>> from ultralytics import NAS
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>>> model = NAS("yolo_nas_s")
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>>> validator = model.validator
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>>> # Assumes that raw_preds are available
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>>> final_preds = validator.postprocess(raw_preds)
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Notes:
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This class is generally not instantiated directly but is used internally within the NAS class.
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"""
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def postprocess(self, preds_in):
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"""Apply Non-maximum suppression to prediction outputs."""
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boxes = ops.xyxy2xywh(preds_in[0][0]) # Convert bounding box format from xyxy to xywh
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preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1) # Concatenate boxes with scores and permute
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return super().postprocess(preds)
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