diff --git a/pcdet/datasets/lyft/lyft_mAP_eval/lyft_eval.py b/pcdet/datasets/lyft/lyft_mAP_eval/lyft_eval.py
new file mode 100644
index 0000000..872476a
--- /dev/null
+++ b/pcdet/datasets/lyft/lyft_mAP_eval/lyft_eval.py
@@ -0,0 +1,435 @@
+"""
+modified from lyft toolkit https://github.com/lyft/nuscenes-devkit.git
+"""
+
+"""
+mAP 3D calculation for the data in nuScenes format.
+
+
+The intput files expected to have the format:
+
+Expected fields:
+
+
+gt = [{
+    'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+    'translation': [974.2811881299899, 1714.6815014457964, -23.689857123368846],
+    'size': [1.796, 4.488, 1.664],
+    'rotation': [0.14882026466054782, 0, 0, 0.9888642620837121],
+    'name': 'car'
+}]
+
+prediction_result = {
+    'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+    'translation': [971.8343488872263, 1713.6816097857359, -25.82534357061308],
+    'size': [2.519726579986132, 7.810161372666739, 3.483438286096803],
+    'rotation': [0.10913582721095375, 0.04099572636992043, 0.01927712319721745, 1.029328402625659],
+    'name': 'car',
+    'score': 0.3077029437237213
+}
+
+
+input arguments:
+
+--pred_file:  file with predictions
+--gt_file: ground truth file
+--iou_threshold: IOU threshold
+
+
+In general we would be interested in average of mAP at thresholds [0.5, 0.55, 0.6, 0.65,...0.95], similar to the
+standard COCO => one needs to run this file N times for every IOU threshold independently.
+
+"""
+
+import argparse
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+from pyquaternion import Quaternion
+from shapely.geometry import Polygon
+
+
+class Box3D:
+    """Data class used during detection evaluation. Can be a prediction or ground truth."""
+
+    def __init__(self, **kwargs):
+        sample_token = kwargs["sample_token"]
+        translation = kwargs["translation"]
+        size = kwargs["size"]
+        rotation = kwargs["rotation"]
+        name = kwargs["name"]
+        score = kwargs.get("score", -1)
+
+        if not isinstance(sample_token, str):
+            raise TypeError("Sample_token must be a string!")
+
+        if not len(translation) == 3:
+            raise ValueError("Translation must have 3 elements!")
+
+        if np.any(np.isnan(translation)):
+            raise ValueError("Translation may not be NaN!")
+
+        if not len(size) == 3:
+            raise ValueError("Size must have 3 elements!")
+
+        if np.any(np.isnan(size)):
+            raise ValueError("Size may not be NaN!")
+
+        if not len(rotation) == 4:
+            raise ValueError("Rotation must have 4 elements!")
+
+        if np.any(np.isnan(rotation)):
+            raise ValueError("Rotation may not be NaN!")
+
+        if name is None:
+            raise ValueError("Name cannot be empty!")
+
+        # Assign.
+        self.sample_token = sample_token
+        self.translation = translation
+        self.size = size
+        self.volume = np.prod(self.size)
+        self.score = score
+
+        assert np.all([x > 0 for x in size])
+        self.rotation = rotation
+        self.name = name
+        self.quaternion = Quaternion(self.rotation)
+
+        self.width, self.length, self.height = size
+
+        self.center_x, self.center_y, self.center_z = self.translation
+
+        self.min_z = self.center_z - self.height / 2
+        self.max_z = self.center_z + self.height / 2
+
+        self.ground_bbox_coords = None
+        self.ground_bbox_coords = self.get_ground_bbox_coords()
+
+    @staticmethod
+    def check_orthogonal(a, b, c):
+        """Check that vector (b - a) is orthogonal to the vector (c - a)."""
+        return np.isclose((b[0] - a[0]) * (c[0] - a[0]) + (b[1] - a[1]) * (c[1] - a[1]), 0)
+
+    def get_ground_bbox_coords(self):
+        if self.ground_bbox_coords is not None:
+            return self.ground_bbox_coords
+        return self.calculate_ground_bbox_coords()
+
+    def calculate_ground_bbox_coords(self):
+        """We assume that the 3D box has lower plane parallel to the ground.
+
+        Returns: Polygon with 4 points describing the base.
+
+        """
+        if self.ground_bbox_coords is not None:
+            return self.ground_bbox_coords
+
+        rotation_matrix = self.quaternion.rotation_matrix
+
+        cos_angle = rotation_matrix[0, 0]
+        sin_angle = rotation_matrix[1, 0]
+
+        point_0_x = self.center_x + self.length / 2 * cos_angle + self.width / 2 * sin_angle
+        point_0_y = self.center_y + self.length / 2 * sin_angle - self.width / 2 * cos_angle
+
+        point_1_x = self.center_x + self.length / 2 * cos_angle - self.width / 2 * sin_angle
+        point_1_y = self.center_y + self.length / 2 * sin_angle + self.width / 2 * cos_angle
+
+        point_2_x = self.center_x - self.length / 2 * cos_angle - self.width / 2 * sin_angle
+        point_2_y = self.center_y - self.length / 2 * sin_angle + self.width / 2 * cos_angle
+
+        point_3_x = self.center_x - self.length / 2 * cos_angle + self.width / 2 * sin_angle
+        point_3_y = self.center_y - self.length / 2 * sin_angle - self.width / 2 * cos_angle
+
+        point_0 = point_0_x, point_0_y
+        point_1 = point_1_x, point_1_y
+        point_2 = point_2_x, point_2_y
+        point_3 = point_3_x, point_3_y
+
+        assert self.check_orthogonal(point_0, point_1, point_3)
+        assert self.check_orthogonal(point_1, point_0, point_2)
+        assert self.check_orthogonal(point_2, point_1, point_3)
+        assert self.check_orthogonal(point_3, point_0, point_2)
+
+        self.ground_bbox_coords = Polygon(
+            [
+                (point_0_x, point_0_y),
+                (point_1_x, point_1_y),
+                (point_2_x, point_2_y),
+                (point_3_x, point_3_y),
+                (point_0_x, point_0_y),
+            ]
+        )
+
+        return self.ground_bbox_coords
+
+    def get_height_intersection(self, other):
+        min_z = max(other.min_z, self.min_z)
+        max_z = min(other.max_z, self.max_z)
+
+        return max(0, max_z - min_z)
+
+    def get_area_intersection(self, other) -> float:
+        result = self.ground_bbox_coords.intersection(other.ground_bbox_coords).area
+
+        assert result <= self.width * self.length
+
+        return result
+
+    def get_intersection(self, other) -> float:
+        height_intersection = self.get_height_intersection(other)
+
+        area_intersection = self.ground_bbox_coords.intersection(other.ground_bbox_coords).area
+
+        return height_intersection * area_intersection
+
+    def get_iou(self, other):
+        intersection = self.get_intersection(other)
+        union = self.volume + other.volume - intersection
+
+        iou = np.clip(intersection / union, 0, 1)
+
+        return iou
+
+    def __repr__(self):
+        return str(self.serialize())
+
+    def serialize(self) -> dict:
+        """Returns: Serialized instance as dict."""
+
+        return {
+            "sample_token": self.sample_token,
+            "translation": self.translation,
+            "size": self.size,
+            "rotation": self.rotation,
+            "name": self.name,
+            "volume": self.volume,
+            "score": self.score,
+        }
+
+
+def group_by_key(detections, key):
+    groups = defaultdict(list)
+    for detection in detections:
+        groups[detection[key]].append(detection)
+    return groups
+
+
+def wrap_in_box(input):
+    result = {}
+    for key, value in input.items():
+        result[key] = [Box3D(**x) for x in value]
+
+    return result
+
+
+def get_envelope(precisions):
+    """Compute the precision envelope.
+
+    Args:
+      precisions:
+
+    Returns:
+
+    """
+    for i in range(precisions.size - 1, 0, -1):
+        precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
+    return precisions
+
+
+def get_ap(recalls, precisions):
+    """Calculate average precision.
+
+    Args:
+      recalls:
+      precisions: Returns (float): average precision.
+
+    Returns:
+
+    """
+    # correct AP calculation
+    # first append sentinel values at the end
+    recalls = np.concatenate(([0.0], recalls, [1.0]))
+    precisions = np.concatenate(([0.0], precisions, [0.0]))
+
+    precisions = get_envelope(precisions)
+
+    # to calculate area under PR curve, look for points where X axis (recall) changes value
+    i = np.where(recalls[1:] != recalls[:-1])[0]
+
+    # and sum (\Delta recall) * prec
+    ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
+    return ap
+
+
+def get_ious(gt_boxes, predicted_box):
+    return [predicted_box.get_iou(x) for x in gt_boxes]
+
+
+def recall_precision(gt, predictions, iou_threshold_list):
+    num_gts = len(gt)
+
+    if num_gts == 0:
+        return -1, -1, -1
+
+    image_gts = group_by_key(gt, "sample_token")
+    image_gts = wrap_in_box(image_gts)
+
+    sample_gt_checked = {sample_token: np.zeros((len(boxes), len(iou_threshold_list))) for sample_token, boxes in image_gts.items()}
+
+    predictions = sorted(predictions, key=lambda x: x["score"], reverse=True)
+
+    # go down dets and mark TPs and FPs
+    num_predictions = len(predictions)
+    tp = np.zeros((num_predictions, len(iou_threshold_list)))
+    fp = np.zeros((num_predictions, len(iou_threshold_list)))
+
+    for prediction_index, prediction in enumerate(predictions):
+        predicted_box = Box3D(**prediction)
+
+        sample_token = prediction["sample_token"]
+
+        max_overlap = -np.inf
+        jmax = -1
+
+        try:
+            gt_boxes = image_gts[sample_token]  # gt_boxes per sample
+            gt_checked = sample_gt_checked[sample_token]  # gt flags per sample
+        except KeyError:
+            gt_boxes = []
+            gt_checked = None
+
+        if len(gt_boxes) > 0:
+            overlaps = get_ious(gt_boxes, predicted_box)
+
+            max_overlap = np.max(overlaps)
+
+            jmax = np.argmax(overlaps)
+
+        for i, iou_threshold in enumerate(iou_threshold_list):
+            if max_overlap > iou_threshold:
+                if gt_checked[jmax, i] == 0:
+                    tp[prediction_index, i] = 1.0
+                    gt_checked[jmax, i] = 1
+                else:
+                    fp[prediction_index, i] = 1.0
+            else:
+                fp[prediction_index, i] = 1.0
+
+    # compute precision recall
+    fp = np.cumsum(fp, axis=0)
+    tp = np.cumsum(tp, axis=0)
+
+    recalls = tp / float(num_gts)
+
+    assert np.all(0 <= recalls) & np.all(recalls <= 1)
+
+    # avoid divide by zero in case the first detection matches a difficult ground truth
+    precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
+
+    assert np.all(0 <= precisions) & np.all(precisions <= 1)
+
+    ap_list = []
+    for i in range(len(iou_threshold_list)):
+        recall = recalls[:, i]
+        precision = precisions[:, i]
+        ap = get_ap(recall, precision)
+        ap_list.append(ap)
+
+    return recalls, precisions, ap_list
+
+
+def get_average_precisions(gt: list, predictions: list, class_names: list, iou_thresholds: list) -> np.array:
+    """Returns an array with an average precision per class.
+
+
+    Args:
+        gt: list of dictionaries in the format described below.
+        predictions: list of dictionaries in the format described below.
+        class_names: list of the class names.
+        iou_threshold: list of IOU thresholds used to calculate TP / FN
+
+    Returns an array with an average precision per class.
+
+
+    Ground truth and predictions should have schema:
+
+    gt = [{
+    'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+    'translation': [974.2811881299899, 1714.6815014457964, -23.689857123368846],
+    'size': [1.796, 4.488, 1.664],
+    'rotation': [0.14882026466054782, 0, 0, 0.9888642620837121],
+    'name': 'car'
+    }]
+
+    predictions = [{
+        'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+        'translation': [971.8343488872263, 1713.6816097857359, -25.82534357061308],
+        'size': [2.519726579986132, 7.810161372666739, 3.483438286096803],
+        'rotation': [0.10913582721095375, 0.04099572636992043, 0.01927712319721745, 1.029328402625659],
+        'name': 'car',
+        'score': 0.3077029437237213
+    }]
+
+    """
+    assert all([0 <= iou_th <= 1 for iou_th in iou_thresholds])
+
+    gt_by_class_name = group_by_key(gt, "name")
+    pred_by_class_name = group_by_key(predictions, "name")
+
+    average_precisions = np.zeros(len(class_names))
+
+    for class_id, class_name in enumerate(class_names):
+        if class_name in pred_by_class_name:
+            recalls, precisions, ap_list = recall_precision(
+                gt_by_class_name[class_name], pred_by_class_name[class_name], iou_thresholds
+            )
+            aps = np.mean(ap_list)
+            average_precisions[class_id] = aps
+
+    return average_precisions
+
+
+def get_class_names(gt: dict) -> list:
+    """Get sorted list of class names.
+
+    Args:
+        gt:
+
+    Returns: Sorted list of class names.
+
+    """
+    return sorted(list(set([x["name"] for x in gt])))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    arg = parser.add_argument
+    arg("-p", "--pred_file", type=str, help="Path to the predictions file.", required=True)
+    arg("-g", "--gt_file", type=str, help="Path to the ground truth file.", required=True)
+    arg("-t", "--iou_threshold", type=float, help="iou threshold", default=0.5)
+
+    args = parser.parse_args()
+
+    gt_path = Path(args.gt_file)
+    pred_path = Path(args.pred_file)
+
+    with open(args.pred_file) as f:
+        predictions = json.load(f)
+
+    with open(args.gt_file) as f:
+        gt = json.load(f)
+
+    class_names = get_class_names(gt)
+    print("Class_names = ", class_names)
+
+    average_precisions = get_average_precisions(gt, predictions, class_names, args.iou_threshold)
+
+    mAP = np.mean(average_precisions)
+    print("Average per class mean average precision = ", mAP)
+
+    for class_id in sorted(list(zip(class_names, average_precisions.flatten().tolist()))):
+        print(class_id)