diff --git a/docs/COCO.md b/docs/COCO.md
index db7dc3117..647292a69 100644
--- a/docs/COCO.md
+++ b/docs/COCO.md
@@ -172,3 +172,35 @@ subsampled_coco = coco.get_subsampled_coco(subsample_ratio=10)
 # export subsampled COCO dataset
 save_json(subsampled_coco.json, "subsampled_coco.json")
 ```
+
+## Get dataset stats:
+
+```python
+from sahi.utils.coco import Coco
+
+# init Coco object
+coco = Coco.from_coco_dict_or_path("coco.json")
+
+# get dataset stats
+coco.stats
+{
+    'avg_annotation_area': 2448.405738278109,
+    'avg_num_annotations_in_image': 53.037243084530985,
+    'max_annotation_area': 328640,
+    'max_num_annotations_in_image': 902,
+    'min_annotation_area': 3,
+    'min_num_annotations_in_image': 1,
+    'num_annotations': 343204,
+    'num_annotations_per_category': {
+        'human': 106396,
+        'vehicle': 236808
+    },
+    'num_categories': 2,
+    'num_images': 6471,
+    'num_images_per_category': {
+        'human': 5684,
+        'vehicle': 6323
+    }
+}
+
+```
diff --git a/sahi/utils/coco.py b/sahi/utils/coco.py
index 7a36f42c5..601eb8f1d 100644
--- a/sahi/utils/coco.py
+++ b/sahi/utils/coco.py
@@ -4,7 +4,7 @@
 
 import copy
 import os
-from collections import OrderedDict, defaultdict
+from collections import Counter, OrderedDict, defaultdict
 from dataclasses import dataclass
 from multiprocessing import Pool
 from pathlib import Path
@@ -737,7 +737,7 @@ def __repr__(self):
 
 
 class Coco:
-    def __init__(self, name=None, image_dir=None, remapping_dict=None):
+    def __init__(self, name=None, image_dir=None, remapping_dict=None, ignore_negative_samples=True):
         """
         Creates Coco object.
 
@@ -748,12 +748,16 @@ def __init__(self, name=None, image_dir=None, remapping_dict=None):
                 Base file directory that contains dataset images. Required for dataset merging.
             remapping_dict: dict
                 {1:0, 2:1} maps category id 1 to 0 and category id 2 to 1
+            ignore_negative_samples: bool
+                If True ignores images without annotations in all operations.
         """
         self.name = name
         self.image_dir = image_dir
         self.remapping_dict = remapping_dict
+        self.ignore_negative_samples = ignore_negative_samples
         self.categories = []
         self.images = []
+        self._stats = None
 
     def add_categories_from_coco_category_list(self, coco_category_list):
         """
@@ -998,9 +1002,71 @@ def json(self):
         return create_coco_dict(
             images=self.images,
             categories=self.json_categories,
-            ignore_negative_samples=True,
+            ignore_negative_samples=self.ignore_negative_samples,
         )
 
+    @property
+    def stats(self):
+        if not self._stats:
+            self.calculate_stats()
+        return self._stats
+
+    def calculate_stats(self):
+        """
+        Iterates over all annotations and calculates total number of
+        """
+        num_annotations = 0
+        num_images = len(self.images)
+        num_categories = len(self.json_categories)
+        category_name_to_zero = {category["name"]:0 for category in self.json_categories}
+        num_images_per_category = copy.deepcopy(category_name_to_zero)
+        num_annotations_per_category = copy.deepcopy(category_name_to_zero)
+        min_num_annotations_in_image = 1e10
+        max_num_annotations_in_image = 0
+        total_annotation_area = 0
+        min_annotation_area = 1e10
+        max_annotation_area = 0
+        for image in self.images:
+            image_contains_category = {}
+            for annotation in image.annotations:
+                annotation_area = annotation.area
+                total_annotation_area += annotation_area
+                num_annotations_per_category[annotation.category_name] += 1
+                image_contains_category[annotation.category_name] = 1
+                # update min&max annotation area
+                if annotation_area>max_annotation_area:
+                    max_annotation_area = annotation_area
+                if annotation_area<min_annotation_area:
+                    min_annotation_area = annotation_area
+            # update num_annotations
+            num_annotations += len(image.annotations)
+            # update num_images_per_category
+            num_images_per_category = dict(
+                Counter(num_images_per_category) + Counter(image_contains_category)
+            )
+            # update min&max_num_annotations_in_image
+            num_annotations_in_image = len(image.annotations)
+            if num_annotations_in_image>max_num_annotations_in_image:
+                max_num_annotations_in_image = num_annotations_in_image
+            if num_annotations_in_image<min_num_annotations_in_image:
+                min_num_annotations_in_image = num_annotations_in_image
+        avg_num_annotations_in_image = num_annotations/num_images
+        avg_annotation_area = total_annotation_area/num_annotations
+
+        self._stats = {
+            "num_images": num_images,
+            "num_annotations": num_annotations,
+            "num_categories": num_categories,
+            "num_images_per_category": num_images_per_category,
+            "num_annotations_per_category": num_annotations_per_category,
+            "min_num_annotations_in_image": min_num_annotations_in_image,
+            "max_num_annotations_in_image": max_num_annotations_in_image,
+            "avg_num_annotations_in_image": avg_num_annotations_in_image,
+            "min_annotation_area": min_annotation_area,
+            "max_annotation_area": max_annotation_area,
+            "avg_annotation_area": avg_annotation_area
+        }
+
     def split_coco_as_train_val(
         self, train_split_rate=0.9, numpy_seed=0
     ):
diff --git a/tests/test_cocoutils.py b/tests/test_cocoutils.py
index 0eae6dbab..0f7944a46 100644
--- a/tests/test_cocoutils.py
+++ b/tests/test_cocoutils.py
@@ -286,6 +286,18 @@ def test_coco(self):
             coco2.category_mapping,
             category_mapping,
         )
+        self.assertEqual(
+            coco1.stats,
+            coco2.stats,
+        )
+        self.assertEqual(
+            coco1.stats["num_images"],
+            len(coco1.images),
+        )
+        self.assertEqual(
+            coco1.stats["num_annotations"],
+            len(coco1.json["annotations"]),
+        )
 
     def test_split_coco_as_train_val(self):
         from sahi.utils.coco import Coco
@@ -300,11 +312,15 @@ def test_split_coco_as_train_val(self):
         self.assertEqual(len(result["train_coco"].json["annotations"]), 5)
         self.assertEqual(result["train_coco"].json["images"][0]["height"], 682)
         self.assertEqual(result["train_coco"].image_dir, image_dir)
+        self.assertEqual(result["train_coco"].stats["num_images"], len(result["train_coco"].images))
+        self.assertEqual(result["train_coco"].stats["num_annotations"], len(result["train_coco"].json["annotations"]))
 
         self.assertEqual(len(result["val_coco"].json["images"]), 1)
         self.assertEqual(len(result["val_coco"].json["annotations"]), 7)
         self.assertEqual(result["val_coco"].json["images"][0]["height"], 1365)
         self.assertEqual(result["val_coco"].image_dir, image_dir)
+        self.assertEqual(result["val_coco"].stats["num_images"], len(result["val_coco"].images))
+        self.assertEqual(result["val_coco"].stats["num_annotations"], len(result["val_coco"].json["annotations"]))
 
     def test_coco2yolo(self):
         from sahi.utils.coco import Coco
@@ -367,6 +383,8 @@ def test_coco_update_categories(self):
         )
         self.assertEqual(coco.json["annotations"][1]["category_id"], 1)
         self.assertEqual(coco.image_dir, image_dir)
+        self.assertEqual(coco.stats["num_images"], len(coco.images))
+        self.assertEqual(coco.stats["num_annotations"], len(coco.json["annotations"]))
 
         # update categories
         desired_name2id = {"human": 1, "car": 2, "big_vehicle": 3}
@@ -385,6 +403,8 @@ def test_coco_update_categories(self):
         )
         self.assertEqual(coco.json["annotations"][1]["category_id"], 2)
         self.assertEqual(coco.image_dir, image_dir)
+        self.assertEqual(coco.stats["num_images"], len(coco.images))
+        self.assertEqual(coco.stats["num_annotations"], len(coco.json["annotations"]))
 
     def test_get_imageid2annotationlist_mapping(self):
         from sahi.utils.coco import get_imageid2annotationlist_mapping
@@ -405,7 +425,7 @@ def check_image_id(image_id):
         check_image_id(image_id=1)
         check_image_id(image_id=2)
 
-    def test_get_imageid2annotationlist_mapping_mt(self):
+    def test_get_imageid2annotationlist_mapping_mp(self):
         from sahi.utils.coco import get_imageid2annotationlist_mapping_mp
 
         coco_path = "tests/data/coco_utils/combined_coco.json"
@@ -547,6 +567,8 @@ def test_coco_merge(self):
             coco2.image_dir,
             image_dir,
         )
+        self.assertEqual(coco2.stats["num_images"], len(coco2.images))
+        self.assertEqual(coco2.stats["num_annotations"], len(coco2.json["annotations"]))
 
     def test_get_subsampled_coco(self):
         from sahi.utils.coco import Coco
@@ -580,6 +602,8 @@ def test_get_subsampled_coco(self):
             subsampled_coco.image_dir,
             image_dir,
         )
+        self.assertEqual(subsampled_coco.stats["num_images"], len(subsampled_coco.images))
+        self.assertEqual(subsampled_coco.stats["num_annotations"], len(subsampled_coco.json["annotations"]))
 
     def test_cocovid(self):
         from sahi.utils.coco import CocoVid