Evaluations

Detection COCO

eval_detection_coco

chainercv.evaluations.eval_detection_coco(pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels, gt_areas=None, gt_crowdeds=None)[source]

Evaluate detections based on evaluation code of MS COCO.

This function evaluates predicted bounding boxes obtained from a dataset by using average precision for each class. The code is based on the evaluation code used in MS COCO.

Parameters:
  • pred_bboxes (iterable of numpy.ndarray) – See the table below.
  • pred_labels (iterable of numpy.ndarray) – See the table below.
  • pred_scores (iterable of numpy.ndarray) – See the table below.
  • gt_bboxes (iterable of numpy.ndarray) – See the table below.
  • gt_labels (iterable of numpy.ndarray) – See the table below.
  • gt_areas (iterable of numpy.ndarray) – See the table below. If None, some scores are not returned.
  • gt_crowdeds (iterable of numpy.ndarray) – See the table below.
name shape dtype format
pred_bboxes \([(R, 4)]\) float32 \((y_{min}, x_{min}, y_{max}, x_{max})\)
pred_labels \([(R,)]\) int32 \([0, \#fg\_class - 1]\)
pred_scores \([(R,)]\) float32 –
gt_bboxes \([(R, 4)]\) float32 \((y_{min}, x_{min}, y_{max}, x_{max})\)
gt_labels \([(R,)]\) int32 \([0, \#fg\_class - 1]\)
gt_areas \([(R,)]\) float32 –
gt_crowdeds \([(R,)]\) bool –

All inputs should have the same length. For more detailed explanation of the inputs, please refer to chainercv.datasets.COCOBboxDataset.

See also

chainercv.datasets.COCOBboxDataset.

Returns:The keys, value-types and the description of the values are listed below. The APs and ARs calculated with different iou thresholds, sizes of objects, and numbers of detections per image. For more details on the 12 patterns of evaluation metrics, please refer to COCO’s official evaluation page.
key type description
ap/iou=0.50:0.95/area=all/max_dets=100 numpy.ndarray [1]
ap/iou=0.50/area=all/max_dets=100 numpy.ndarray [1]
ap/iou=0.75/area=all/max_dets=100 numpy.ndarray [1]
ap/iou=0.50:0.95/area=small/max_dets=100 numpy.ndarray [1] [5]
ap/iou=0.50:0.95/area=medium/max_dets=100 numpy.ndarray [1] [5]
ap/iou=0.50:0.95/area=large/max_dets=100 numpy.ndarray [1] [5]
ar/iou=0.50:0.95/area=all/max_dets=1 numpy.ndarray [2]
ar/iou=0.50/area=all/max_dets=10 numpy.ndarray [2]
ar/iou=0.75/area=all/max_dets=100 numpy.ndarray [2]
ar/iou=0.50:0.95/area=small/max_dets=100 numpy.ndarray [2] [5]
ar/iou=0.50:0.95/area=medium/max_dets=100 numpy.ndarray [2] [5]
ar/iou=0.50:0.95/area=large/max_dets=100 numpy.ndarray [2] [5]
map/iou=0.50:0.95/area=all/max_dets=100 float [3]
map/iou=0.50/area=all/max_dets=100 float [3]
map/iou=0.75/area=all/max_dets=100 float [3]
map/iou=0.50:0.95/area=small/max_dets=100 float [3] [5]
map/iou=0.50:0.95/area=medium/max_dets=100 float [3] [5]
map/iou=0.50:0.95/area=large/max_dets=100 float [3] [5]
mar/iou=0.50:0.95/area=all/max_dets=1 float [4]
mar/iou=0.50/area=all/max_dets=10 float [4]
mar/iou=0.75/area=all/max_dets=100 float [4]
mar/iou=0.50:0.95/area=small/max_dets=100 float [4] [5]
mar/iou=0.50:0.95/area=medium/max_dets=100 float [4] [5]
mar/iou=0.50:0.95/area=large/max_dets=100 float [4] [5]
coco_eval pycocotools.cocoeval.COCOeval result from pycocotools
existent_labels numpy.ndarray used labels
Return type:dict
[1](1, 2, 3, 4, 5, 6) An array of average precisions. The \(l\)-th value corresponds to the average precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, the corresponding value is set to numpy.nan.
[2](1, 2, 3, 4, 5, 6) An array of average recalls. The \(l\)-th value corresponds to the average precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, the corresponding value is set to numpy.nan.
[3](1, 2, 3, 4, 5, 6) The average of average precisions over classes.
[4](1, 2, 3, 4, 5, 6) The average of average recalls over classes.
[5](1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) Skip if gt_areas is None.

Detection VOC

eval_detection_voc

chainercv.evaluations.eval_detection_voc(pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels, gt_difficults=None, iou_thresh=0.5, use_07_metric=False)[source]

Calculate average precisions based on evaluation code of PASCAL VOC.

This function evaluates predicted bounding boxes obtained from a dataset which has \(N\) images by using average precision for each class. The code is based on the evaluation code used in PASCAL VOC Challenge.

Parameters:
  • pred_bboxes (iterable of numpy.ndarray) – An iterable of \(N\) sets of bounding boxes. Its index corresponds to an index for the base dataset. Each element of pred_bboxes is a set of coordinates of bounding boxes. This is an array whose shape is \((R, 4)\), where \(R\) corresponds to the number of bounding boxes, which may vary among boxes. The second axis corresponds to \(y_{min}, x_{min}, y_{max}, x_{max}\) of a bounding box.
  • pred_labels (iterable of numpy.ndarray) – An iterable of labels. Similar to pred_bboxes, its index corresponds to an index for the base dataset. Its length is \(N\).
  • pred_scores (iterable of numpy.ndarray) – An iterable of confidence scores for predicted bounding boxes. Similar to pred_bboxes, its index corresponds to an index for the base dataset. Its length is \(N\).
  • gt_bboxes (iterable of numpy.ndarray) – An iterable of ground truth bounding boxes whose length is \(N\). An element of gt_bboxes is a bounding box whose shape is \((R, 4)\). Note that the number of bounding boxes in each image does not need to be same as the number of corresponding predicted boxes.
  • gt_labels (iterable of numpy.ndarray) – An iterable of ground truth labels which are organized similarly to gt_bboxes.
  • gt_difficults (iterable of numpy.ndarray) – An iterable of boolean arrays which is organized similarly to gt_bboxes. This tells whether the corresponding ground truth bounding box is difficult or not. By default, this is None. In that case, this function considers all bounding boxes to be not difficult.
  • iou_thresh (float) – A prediction is correct if its Intersection over Union with the ground truth is above this value.
  • use_07_metric (bool) – Whether to use PASCAL VOC 2007 evaluation metric for calculating average precision. The default value is False.
Returns:

The keys, value-types and the description of the values are listed below.

  • ap (numpy.ndarray): An array of average precisions. The \(l\)-th value corresponds to the average precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, the corresponding value is set to numpy.nan.
  • map (float): The average of Average Precisions over classes.
Return type:

dict

calc_detection_voc_ap

chainercv.evaluations.calc_detection_voc_ap(prec, rec, use_07_metric=False)[source]

Calculate average precisions based on evaluation code of PASCAL VOC.

This function calculates average precisions from given precisions and recalls. The code is based on the evaluation code used in PASCAL VOC Challenge.

Parameters:
  • prec (list of numpy.array) – A list of arrays. prec[l] indicates precision for class \(l\). If prec[l] is None, this function returns numpy.nan for class \(l\).
  • rec (list of numpy.array) – A list of arrays. rec[l] indicates recall for class \(l\). If rec[l] is None, this function returns numpy.nan for class \(l\).
  • use_07_metric (bool) – Whether to use PASCAL VOC 2007 evaluation metric for calculating average precision. The default value is False.
Returns:

This function returns an array of average precisions. The \(l\)-th value corresponds to the average precision for class \(l\). If prec[l] or rec[l] is None, the corresponding value is set to numpy.nan.
Return type:

ndarray

calc_detection_voc_prec_rec

chainercv.evaluations.calc_detection_voc_prec_rec(pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels, gt_difficults=None, iou_thresh=0.5)[source]

Calculate precision and recall based on evaluation code of PASCAL VOC.

This function calculates precision and recall of predicted bounding boxes obtained from a dataset which has \(N\) images. The code is based on the evaluation code used in PASCAL VOC Challenge.

Parameters:
  • pred_bboxes (iterable of numpy.ndarray) – An iterable of \(N\) sets of bounding boxes. Its index corresponds to an index for the base dataset. Each element of pred_bboxes is a set of coordinates of bounding boxes. This is an array whose shape is \((R, 4)\), where \(R\) corresponds to the number of bounding boxes, which may vary among boxes. The second axis corresponds to \(y_{min}, x_{min}, y_{max}, x_{max}\) of a bounding box.
  • pred_labels (iterable of numpy.ndarray) – An iterable of labels. Similar to pred_bboxes, its index corresponds to an index for the base dataset. Its length is \(N\).
  • pred_scores (iterable of numpy.ndarray) – An iterable of confidence scores for predicted bounding boxes. Similar to pred_bboxes, its index corresponds to an index for the base dataset. Its length is \(N\).
  • gt_bboxes (iterable of numpy.ndarray) – An iterable of ground truth bounding boxes whose length is \(N\). An element of gt_bboxes is a bounding box whose shape is \((R, 4)\). Note that the number of bounding boxes in each image does not need to be same as the number of corresponding predicted boxes.
  • gt_labels (iterable of numpy.ndarray) – An iterable of ground truth labels which are organized similarly to gt_bboxes.
  • gt_difficults (iterable of numpy.ndarray) – An iterable of boolean arrays which is organized similarly to gt_bboxes. This tells whether the corresponding ground truth bounding box is difficult or not. By default, this is None. In that case, this function considers all bounding boxes to be not difficult.
  • iou_thresh (float) – A prediction is correct if its Intersection over Union with the ground truth is above this value..
Returns:

This function returns two lists: prec and rec.

  • prec: A list of arrays. prec[l] is precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, prec[l] is set to None.
  • rec: A list of arrays. rec[l] is recall for class \(l\). If class \(l\) that is not marked as difficult does not exist in gt_labels, rec[l] is set to None.
Return type:

tuple of two lists

Instance Segmentation COCO

eval_instance_segmentation_coco

chainercv.evaluations.eval_instance_segmentation_coco(pred_masks, pred_labels, pred_scores, gt_masks, gt_labels, gt_areas=None, gt_crowdeds=None)[source]

Evaluate instance segmentations based on evaluation code of MS COCO.

This function evaluates predicted instance segmentations obtained from a dataset by using average precision for each class. The code is based on the evaluation code used in MS COCO.

Parameters:
  • pred_masks (iterable of numpy.ndarray) – See the table below.
  • pred_labels (iterable of numpy.ndarray) – See the table below.
  • pred_scores (iterable of numpy.ndarray) – See the table below.
  • gt_masks (iterable of numpy.ndarray) – See the table below.
  • gt_labels (iterable of numpy.ndarray) – See the table below.
  • gt_areas (iterable of numpy.ndarray) – See the table below. If None, some scores are not returned.
  • gt_crowdeds (iterable of numpy.ndarray) – See the table below.
name shape dtype format
pred_masks \([(R, H, W)]\) bool –
pred_labels \([(R,)]\) int32 \([0, \#fg\_class - 1]\)
pred_scores \([(R,)]\) float32 –
gt_masks \([(R, H, W)]\) bool –
gt_labels \([(R,)]\) int32 \([0, \#fg\_class - 1]\)
gt_areas \([(R,)]\) float32 –
gt_crowdeds \([(R,)]\) bool –

All inputs should have the same length. For more detailed explanation of the inputs, please refer to chainercv.datasets.COCOInstanceSegmentationDataset.

See also

chainercv.datasets.COCOInstanceSegmentationDataset.

Returns:The keys, value-types and the description of the values are listed below. The APs and ARs calculated with different iou thresholds, sizes of objects, and numbers of detections per image. For more details on the 12 patterns of evaluation metrics, please refer to COCO’s official evaluation page.
key type description
ap/iou=0.50:0.95/area=all/max_dets=100 numpy.ndarray [6]
ap/iou=0.50/area=all/max_dets=100 numpy.ndarray [6]
ap/iou=0.75/area=all/max_dets=100 numpy.ndarray [6]
ap/iou=0.50:0.95/area=small/max_dets=100 numpy.ndarray [6] [10]
ap/iou=0.50:0.95/area=medium/max_dets=100 numpy.ndarray [6] [10]
ap/iou=0.50:0.95/area=large/max_dets=100 numpy.ndarray [6] [10]
ar/iou=0.50:0.95/area=all/max_dets=1 numpy.ndarray [7]
ar/iou=0.50/area=all/max_dets=10 numpy.ndarray [7]
ar/iou=0.75/area=all/max_dets=100 numpy.ndarray [7]
ar/iou=0.50:0.95/area=small/max_dets=100 numpy.ndarray [7] [10]
ar/iou=0.50:0.95/area=medium/max_dets=100 numpy.ndarray [7] [10]
ar/iou=0.50:0.95/area=large/max_dets=100 numpy.ndarray [7] [10]
map/iou=0.50:0.95/area=all/max_dets=100 float [8]
map/iou=0.50/area=all/max_dets=100 float [8]
map/iou=0.75/area=all/max_dets=100 float [8]
map/iou=0.50:0.95/area=small/max_dets=100 float [8] [10]
map/iou=0.50:0.95/area=medium/max_dets=100 float [8] [10]
map/iou=0.50:0.95/area=large/max_dets=100 float [8] [10]
mar/iou=0.50:0.95/area=all/max_dets=1 float [9]
mar/iou=0.50/area=all/max_dets=10 float [9]
mar/iou=0.75/area=all/max_dets=100 float [9]
mar/iou=0.50:0.95/area=small/max_dets=100 float [9] [10]
mar/iou=0.50:0.95/area=medium/max_dets=100 float [9] [10]
mar/iou=0.50:0.95/area=large/max_dets=100 float [9] [10]
coco_eval pycocotools.cocoeval.COCOeval result from pycocotools
existent_labels numpy.ndarray used labels
Return type:dict
[6](1, 2, 3, 4, 5, 6) An array of average precisions. The \(l\)-th value corresponds to the average precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, the corresponding value is set to numpy.nan.
[7](1, 2, 3, 4, 5, 6) An array of average recalls. The \(l\)-th value corresponds to the average precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, the corresponding value is set to numpy.nan.
[8](1, 2, 3, 4, 5, 6) The average of average precisions over classes.
[9](1, 2, 3, 4, 5, 6) The average of average recalls over classes.
[10](1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) Skip if gt_areas is None.

Instance Segmentation VOC

eval_instance_segmentation_voc

chainercv.evaluations.eval_instance_segmentation_voc(pred_masks, pred_labels, pred_scores, gt_masks, gt_labels, iou_thresh=0.5, use_07_metric=False)[source]

Calculate average precisions based on evaluation code of PASCAL VOC.

This function evaluates predicted masks obtained from a dataset which has \(N\) images by using average precision for each class. The code is based on the evaluation code used in FCIS.

Parameters:
  • pred_masks (iterable of numpy.ndarray) – An iterable of \(N\) sets of masks. Its index corresponds to an index for the base dataset. Each element of pred_masks is an object mask and is an array whose shape is \((R, H, W)\), where \(R\) corresponds to the number of masks, which may vary among images.
  • pred_labels (iterable of numpy.ndarray) – An iterable of labels. Similar to pred_masks, its index corresponds to an index for the base dataset. Its length is \(N\).
  • pred_scores (iterable of numpy.ndarray) – An iterable of confidence scores for predicted masks. Similar to pred_masks, its index corresponds to an index for the base dataset. Its length is \(N\).
  • gt_masks (iterable of numpy.ndarray) – An iterable of ground truth masks whose length is \(N\). An element of gt_masks is an object mask whose shape is \((R, H, W)\). Note that the number of masks \(R\) in each image does not need to be same as the number of corresponding predicted masks.
  • gt_labels (iterable of numpy.ndarray) – An iterable of ground truth labels which are organized similarly to gt_masks. Its length is \(N\).
  • iou_thresh (float) – A prediction is correct if its Intersection over Union with the ground truth is above this value.
  • use_07_metric (bool) – Whether to use PASCAL VOC 2007 evaluation metric for calculating average precision. The default value is False.
Returns:

The keys, value-types and the description of the values are listed below.

  • ap (numpy.ndarray): An array of average precisions. The \(l\)-th value corresponds to the average precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, the corresponding value is set to numpy.nan.
  • map (float): The average of Average Precisions over classes.
Return type:

dict

calc_instance_segmentation_voc_prec_rec

chainercv.evaluations.calc_instance_segmentation_voc_prec_rec(pred_masks, pred_labels, pred_scores, gt_masks, gt_labels, iou_thresh)[source]

Calculate precision and recall based on evaluation code of PASCAL VOC.

This function calculates precision and recall of predicted masks obtained from a dataset which has \(N\) images. The code is based on the evaluation code used in FCIS.

Parameters:
  • pred_masks (iterable of numpy.ndarray) – An iterable of \(N\) sets of masks. Its index corresponds to an index for the base dataset. Each element of pred_masks is an object mask and is an array whose shape is \((R, H, W)\), where \(R\) corresponds to the number of masks, which may vary among images.
  • pred_labels (iterable of numpy.ndarray) – An iterable of labels. Similar to pred_masks, its index corresponds to an index for the base dataset. Its length is \(N\).
  • pred_scores (iterable of numpy.ndarray) – An iterable of confidence scores for predicted masks. Similar to pred_masks, its index corresponds to an index for the base dataset. Its length is \(N\).
  • gt_masks (iterable of numpy.ndarray) – An iterable of ground truth masks whose length is \(N\). An element of gt_masks is an object mask whose shape is \((R, H, W)\). Note that the number of masks \(R\) in each image does not need to be same as the number of corresponding predicted masks.
  • gt_labels (iterable of numpy.ndarray) – An iterable of ground truth labels which are organized similarly to gt_masks. Its length is \(N\).
  • iou_thresh (float) – A prediction is correct if its Intersection over Union with the ground truth is above this value.
Returns:

This function returns two lists: prec and rec.

  • prec: A list of arrays. prec[l] is precision for class \(l\). If class \(l\) does not exist in either pred_labels or gt_labels, prec[l] is set to None.
  • rec: A list of arrays. rec[l] is recall for class \(l\). If class \(l\) that is not marked as difficult does not exist in gt_labels, rec[l] is set to None.
Return type:

tuple of two lists

Semantic Segmentation IoU

eval_semantic_segmentation

chainercv.evaluations.eval_semantic_segmentation(pred_labels, gt_labels)[source]

Evaluate metrics used in Semantic Segmentation.

This function calculates Intersection over Union (IoU), Pixel Accuracy and Class Accuracy for the task of semantic segmentation.

The definition of metrics calculated by this function is as follows, where \(N_{ij}\) is the number of pixels that are labeled as class \(i\) by the ground truth and class \(j\) by the prediction.

  • \(\text{IoU of the i-th class} = \frac{N_{ii}}{\sum_{j=1}^k N_{ij} + \sum_{j=1}^k N_{ji} - N_{ii}}\)
  • \(\text{mIoU} = \frac{1}{k} \sum_{i=1}^k \frac{N_{ii}}{\sum_{j=1}^k N_{ij} + \sum_{j=1}^k N_{ji} - N_{ii}}\)
  • \(\text{Pixel Accuracy} = \frac {\sum_{i=1}^k N_{ii}} {\sum_{i=1}^k \sum_{j=1}^k N_{ij}}\)
  • \(\text{Class Accuracy} = \frac{N_{ii}}{\sum_{j=1}^k N_{ij}}\)
  • \(\text{Mean Class Accuracy} = \frac{1}{k} \sum_{i=1}^k \frac{N_{ii}}{\sum_{j=1}^k N_{ij}}\)

The more detailed description of the above metrics can be found in a review on semantic segmentation [11].

The number of classes \(n\_class\) is \(max(pred\_labels, gt\_labels) + 1\), which is the maximum class id of the inputs added by one.

[11]Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Jose Garcia-Rodriguez. A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv 2017.
Parameters:
  • pred_labels (iterable of numpy.ndarray) – A collection of predicted labels. The shape of a label array is \((H, W)\). \(H\) and \(W\) are height and width of the label. For example, this is a list of labels [label_0, label_1, ...], where label_i.shape = (H_i, W_i).
  • gt_labels (iterable of numpy.ndarray) – A collection of ground truth labels. The shape of a ground truth label array is \((H, W)\), and its corresponding prediction label should have the same shape. A pixel with value -1 will be ignored during evaluation.
Returns:

The keys, value-types and the description of the values are listed below.

  • iou (numpy.ndarray): An array of IoUs for the \(n\_class\) classes. Its shape is \((n\_class,)\).
  • miou (float): The average of IoUs over classes.
  • pixel_accuracy (float): The computed pixel accuracy.
  • class_accuracy (numpy.ndarray): An array of class accuracies for the \(n\_class\) classes. Its shape is \((n\_class,)\).
  • mean_class_accuracy (float): The average of class accuracies.
Return type:

dict

calc_semantic_segmentation_confusion

chainercv.evaluations.calc_semantic_segmentation_confusion(pred_labels, gt_labels)[source]

Collect a confusion matrix.

The number of classes \(n\_class\) is \(max(pred\_labels, gt\_labels) + 1\), which is the maximum class id of the inputs added by one.

Parameters:
  • pred_labels (iterable of numpy.ndarray) – A collection of predicted labels. The shape of a label array is \((H, W)\). \(H\) and \(W\) are height and width of the label.
  • gt_labels (iterable of numpy.ndarray) – A collection of ground truth labels. The shape of a ground truth label array is \((H, W)\), and its corresponding prediction label should have the same shape. A pixel with value -1 will be ignored during evaluation.
Returns:

A confusion matrix. Its shape is \((n\_class, n\_class)\). The \((i, j)\) th element corresponds to the number of pixels that are labeled as class \(i\) by the ground truth and class \(j\) by the prediction.
Return type:

numpy.ndarray

calc_semantic_segmentation_iou

chainercv.evaluations.calc_semantic_segmentation_iou(confusion)[source]

Calculate Intersection over Union with a given confusion matrix.

The definition of Intersection over Union (IoU) is as follows, where \(N_{ij}\) is the number of pixels that are labeled as class \(i\) by the ground truth and class \(j\) by the prediction.

  • \(\text{IoU of the i-th class} = \frac{N_{ii}}{\sum_{j=1}^k N_{ij} + \sum_{j=1}^k N_{ji} - N_{ii}}\)
Parameters:confusion (numpy.ndarray) – A confusion matrix. Its shape is \((n\_class, n\_class)\). The \((i, j)\) th element corresponds to the number of pixels that are labeled as class \(i\) by the ground truth and class \(j\) by the prediction.
Returns:An array of IoUs for the \(n\_class\) classes. Its shape is \((n\_class,)\).
Return type:numpy.ndarray