# Evaluation results format

###### tags: `Evaluation report`

## Binary

{
    'per_class':{
        'pos_label':{
            'precision': float,
            'recall': float ,
            'f1': float
        }
    },
    'one_class' : {
        'confusion_matrix': numpy.ndarray
    }
}

## Multiclass

{
    'per_class':{
        'label_1':{
            'precision': float numpy array, list of precisions,
            'recall': float numpy array, list of recalls,
            'f1': float numpy array, list of f1 measures
        },
        'label_2': { . . . },
        [ more labels ]
    },
    'one_class' : {
        'confusion_matrix': numpy.ndarray
    }
}

## Clasificación de imágenes
### Multiclase

{
    'per_class': {
        'label_1': {
            'precision': float,
            'recall': float,
            'f1': float
        },
        'label_2': { ... },
        ...
    },
    'one_class': {
        'confusion_matrix': array [n_classes, n_classes],
        'precision': float,
        'recall': float,
        'f1': float
    }
}

### Binario

{
    'per_class': {
        'pos_class': {
            'precision': float,
            'recall': float,
            'f1': float
        }
    },
    'one_class': {
        'confusion_matrix': array, shape = [2, 2]
    }
}

## Detección de imágenes

### Pascal VOC

{
    'per_class': {
        'label_1': {
            'precisions': float numpy array, list of precisions.
            'recalls': float numpy array, list of recalls.
            'average_precision': float, average precision for each class.
        },
        'label_2': { ... },
        ...
    },
    'one_class': {
        'mAP': float, mean average precision of all classes. 
    }
}

### COCO

{
    'per_class': {
        'label_1': {
            'Precision/mAP ByCategory/class_n': float,
            'Precision/mAP@.50IOU ByCategory/class_n': float, 
            'Precision/mAP@.75IOU ByCategory/class_n': float, 
            'Precision/mAP (small) ByCategory/class_n': float, 
            'Precision/mAP (medium) ByCategory/class_n': float, 
            'Precision/mAP (large) ByCategory/class_n': float, 
            'Recall/AR@1 ByCategory/class_n': float, 
            'Recall/AR@10 ByCategory/class_n': float, 
            'Recall/AR@100 ByCategory/class_n': float, 
            'Recall/AR@100 (small) ByCategory/class_n': float, 
            'Recall/AR@100 (medium) ByCategory/class_n': float, 
            'Recall/AR@100 (large) ByCategory/class_n': float
        },
        'label_2': { ... },
        ...
    },
    'one_class': {
        'Precision/mAP': float, 
        'Precision/mAP@.50IOU': float, 
        'Precision/mAP@.75IOU': float, 
        'Precision/mAP (small)': float, 
        'Precision/mAP (medium)': float, 
        'Precision/mAP (large)': float, 
        'Recall/AR@1': float, 
        'Recall/AR@10': float, 
        'Recall/AR@100': float, 
        'Recall/AR@100 (small)': float, 
        'Recall/AR@100 (medium)': float, 
        'Recall/AR@100 (large)': float
    }
}

### Google Open Images V2

{
    'per_class': {
        'label_1': {
            'precisions': float numpy array, list of precisions.
            'recalls': float numpy array, list of recalls.
            'average_precision': float, average precision for each class.
        },
        'label_2': { ... },
        ...
    },
    'one_class': {
        'mAP': float, mean average precision of all classes. 
        'total_positives': int, total number of ground truth positives.
        'total_TP': int, total number of True Positive detections.
        'total_FP': int, total number of False Negative detections.
    }
}

# Formato del parámetro eval_config del método eval

## Clasificación

{
    'metrics_set': {
        'BINARY': {
            'labels': list of str,
            'pos_label': str or int, default 1
            'average': str, default 'binary'
            'sample_weight': list or tuple
        },
        'MULTICLASS': {
            'labels': list of str,
            'pos_label': str or int, default 1
            'average': str, default 'macro'
            'sample_weight': list or tuple
        }
    },
    'dataset_partition': None or str,
    'eval_dir': str
}

## Detección

{
    'metrics_set': {
        'PASCAL_VOC': {
            'iou_threshold': float, default 0.5
            'nms_iou_threshold': float, default 0.5
            'nms_max_output_boxes': int, default 50
            'use_weighted_mean_ap': bool, default True
        }
    },
    'dataset_partition': None or str,
    'eval_dir': str
}