What is Idmap?

IdmapResMap::Lookup

Who calls ApkAssets::LoadOverlay()?

  // Represents a Runtime Resource Overlay that overlays resources in the logical package.
  struct ConfiguredOverlay {
      // The set of package groups that overlay this package group.
      IdmapResMap overlay_res_maps_;

      // The cookie of the overlay assets.
      ApkAssetsCookie cookie;
  };

What is a ApkAssets?

AssetManager2::BuildDynamicRefTable()

AssetManager2::GetNonSystemOverlays()

LoadedIdmap::Load

createIdmap

com.android.server.om.IdmapDaemon#createIdmap

/home/andy/aosp13/frameworks/base/cmds/idmap2

Idmap::FromContainers

removeIdmap

todo:
/home/andy/aosp13/system/libziparchive

std::function :

bool ForEachFile(const std::string& root_path,
const std::function<void(const StringPiece&, FileType)>& f) const override;

std::ofstream fout(idmap_path);

reinterpret_cast

### YOLOv8 mAP Evaluation Metric Explanation and Usage The mean Average Precision (mAP) is one of the primary evaluation metrics used to assess object detection models like YOLOv8. This metric provides an overall measure of how well a model can detect objects across different categories. #### Definition of mAP In the context of object detection, precision measures the proportion of true positive detections among all detected instances. Recall represents the ratio of correctly identified positives out of actual positives present in images. The average precision (AP) for each class calculates the area under the precision-recall curve. Finally, mAP averages these AP values over multiple classes or IoU thresholds[^1]. For instance, when discussing classification problems such as those encountered with logarithmic loss, it's important that similar principles apply where accuracy matters significantly but within distinct contexts relevant to specific tasks at hand. #### Calculation Process To compute mAP specifically tailored towards YOLOv8: 1. For every image containing ground truth bounding boxes, 2. Predictions must be ranked based on confidence scores. 3. Compute Intersection Over Union (IoU) between predicted box and corresponding ground-truth label. 4. Determine whether prediction matches any existing labels using predefined threshold(s). 5. Accumulate results into confusion matrix entries per category. 6. Calculate interpolated precisions from recall points obtained during this process. 7. Integrate areas underneath resulting curves yielding individual-class APs. 8. Summarize final score through averaging calculated AP figures across various intersection-over-union levels typically ranging from .50:.95. This approach ensures comprehensive assessment covering not only localization quality but also classification performance effectively. ```python def calculate_map(predictions, ground_truths, iou_threshold=0.5): """ Simplified function demonstrating concept behind calculating mAP Args: predictions (list): List of tuples representing predicted bboxes [(class_id, conf_score, bbox), ...] ground_truths (list): List of tuples representing gt bboxes [(class_id, bbox),... ] iou_threshold (float): Threshold value above which match considered valid Returns: float: Mean Average Precision Score """ # Placeholder implementation details omitted for brevity pass ``` --related questions-- 1. How does changing IOU thresholds impact mAP calculation? 2. What other evaluation metrics complement mAP for assessing object detectors besides clustering algorithm metrics mentioned elsewhere? 3. Can you provide examples illustrating scenarios wherein high-confidence false negatives affect mAP more severely than low-confidence ones? 4. In what ways do dataset characteristics influence optimal choice of evaluation criteria including mAP?
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