diff --git a/__pycache__/predict_mmdetection.cpython-38.pyc b/__pycache__/predict_mmdetection.cpython-38.pyc
index 0e6daa7fbac91760ef683f1a0ec86ed0317d4ee6..a45d053eedcfc062a1751858d56eba9b4fd08ff7 100644
Binary files a/__pycache__/predict_mmdetection.cpython-38.pyc and b/__pycache__/predict_mmdetection.cpython-38.pyc differ
diff --git a/aicrowd.json b/aicrowd.json
index a048a18b0aa29f541f1e259823ae70fdf4aad29d..4efe8d77142adda9022ec688e4801f58f88ac57b 100644
--- a/aicrowd.json
+++ b/aicrowd.json
@@ -7,7 +7,7 @@
   "license": "MIT",
   "gpu": true,
   "debug": false,
-  "model_path": "models/epoch_18_new.pth",
+  "model_path": "models/epoch_14.pth",
   "model_type": "mmdetection",
   "model_config_file": "configs/queryinst_old.py"
 }
\ No newline at end of file
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diff --git a/data/images/2.jpg b/data/images/2.jpg
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diff --git a/data/images/3.jpg b/data/images/3.jpg
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diff --git a/data/results/predictions.json b/data/results/predictions.json
index 1fc48b5177fd48c740142fd38a8795cdcddb17be..48afee9b6a14a14062fe7059011d1fd9c71f9288 100644
--- a/data/results/predictions.json
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"omi0a0[>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;C?^OcT^1"}}, {"image_id": 2, "bbox": [52.4048957824707, 72.23290252685547, 330.98395919799805, 268.2403030395508], "score": 0.27167534828186035, "category_id": 101266, "segmentation": {"size": [479, 479], "counts": 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\ No newline at end of file
diff --git a/evaluator/__pycache__/food_challenge.cpython-38.pyc b/evaluator/__pycache__/food_challenge.cpython-38.pyc
index e182a1c931ac03b7cab5891d239d79263b97a708..9e80e3c61adbbbe3fa587b463a85f1a811bf3944 100644
Binary files a/evaluator/__pycache__/food_challenge.cpython-38.pyc and b/evaluator/__pycache__/food_challenge.cpython-38.pyc differ
diff --git a/evaluator/food_challenge.py b/evaluator/food_challenge.py
index 8593ed484f15d25365bb7862bdca5ee827a199af..ff0cfa525cd76be2c5620b7bc93de67b4decb930 100644
--- a/evaluator/food_challenge.py
+++ b/evaluator/food_challenge.py
@@ -17,18 +17,10 @@ from pycocotools.cocoeval import COCOeval
 
 class FoodChallengePredictor:
     def __init__(self):
-        self.test_data_path = os.getenv(
-            "TEST_DATASET_PATH", os.getcwd() + "/data/images/"
-        )
-        self.results_data_path = os.getenv(
-            "RESULTS_DATASET_PATH", os.getcwd() + "/data/results/"
-        )
-        self.prediction_setup_timeout = int(
-            os.getenv("PREDICTION_SETUP_TIMEOUT_SECONDS", "120")
-        )
-        self.prediction_per_image_timeout = int(
-            os.getenv("PREDICTION_PER_IMAGE_TIMEOUT_SECONDS", "1")
-        )
+        self.test_data_path = os.getenv("TEST_DATASET_PATH", os.getcwd() + "/data/images/")
+        self.results_data_path = os.getenv("RESULTS_DATASET_PATH", os.getcwd() + "/data/results/")
+        self.prediction_setup_timeout = int(os.getenv("PREDICTION_SETUP_TIMEOUT_SECONDS", "120"))
+        self.prediction_per_image_timeout = int(os.getenv("PREDICTION_PER_IMAGE_TIMEOUT_SECONDS", "1"))
         self.is_online_run = False
         self.predictions = []
 
@@ -48,9 +40,7 @@ class FoodChallengePredictor:
                 self.prediction_setup()
             self.update_status_to_evaluation_system("Completed prediction_setup")
         except NotImplementedError:
-            self.update_status_to_evaluation_system(
-                "prediction_setup doesn't exist for this run, skipping..."
-            )
+            self.update_status_to_evaluation_system("prediction_setup doesn't exist for this run, skipping...")
 
         image_paths = self.get_all_image_paths()
 
@@ -67,25 +57,20 @@ class FoodChallengePredictor:
         self.save_predictions()
 
     def add_prediction(self, prediction):
-        if "image_id" not in prediction:
-            prediction["image_id"] = int(
-                os.path.splitext(os.path.basename(prediction["image_path"]))[0]
-            )
+        if 'image_id' not in prediction:
+            prediction['image_id'] = int(os.path.splitext(os.path.basename(prediction['image_path']))[0])
         self.validate_prediction(prediction)
         self.predictions.append(prediction)
 
     def validate_prediction(self, prediction):
-        assert prediction["image_id"] is not None
-        assert (
-            prediction["category_id"] is not None
-            and prediction["category_id"] in self.valid_categories()
-        )
-        assert prediction["score"] is not None and 0.0 <= prediction["score"] <= 1.0
-        assert prediction["segmentation"] is not None
-        assert prediction["bbox"] is not None
+        assert prediction['image_id'] is not None
+        assert prediction['category_id'] is not None and prediction['category_id'] in self.valid_categories()
+        assert prediction['score'] is not None and 0.0 <= prediction['score'] <= 1.0
+        assert prediction['segmentation'] is not None
+        assert prediction['bbox'] is not None
 
     def save_predictions(self):
-        fp = open(os.path.join(self.results_data_path, "predictions.json"), "w")
+        fp = open(os.path.join(self.results_data_path, 'predictions.json'), 'w')
         fp.write(json.dumps(self.predictions))
         fp.close()
 
@@ -100,7 +85,7 @@ class FoodChallengePredictor:
 
     def prediction_setup(self):
         """
-        You can do any preprocessing required for your codebase here :
+        You can do any preprocessing required for your codebase here : 
             like loading your models into memory, etc.
         """
         raise NotImplementedError
@@ -118,17 +103,13 @@ class FoodChallengePredictor:
     def scoring(self, ground_truth_path, predictions_file_path):
         if not os.path.exists(predictions_file_path):
             raise Exception(
-                "Invalid predictions_file_path provided : ".format(
-                    predictions_file_path
-                )
+                "Invalid predictions_file_path provided : ".format(predictions_file_path)
             )
 
         self._report("Loading Ground Truth Annotations...")
         ground_truth_annotations = COCO(ground_truth_path)
 
-        self._report(
-            "Loading Predicted Annotations from : {}".format(predictions_file_path)
-        )
+        self._report("Loading Predicted Annotations from : {}".format(predictions_file_path))
         predictions = json.loads(open(predictions_file_path).read())
         self._report("Predictions JSON read into memoery...")
         assert (
@@ -143,9 +124,7 @@ class FoodChallengePredictor:
         results = ground_truth_annotations.loadRes(predictions)
         self._report("Predicted Annotations Loaded Successfully...")
 
-        self._report(
-            "Initiating evaluation. This might take a few minutes. Hang in there :D "
-        )
+        self._report("Initiating evaluation. This might take a few minutes. Hang in there :D ")
         cocoEval = COCOeval(ground_truth_annotations, results, "segm")
         cocoEval.evaluate()
 
@@ -168,511 +147,12 @@ class FoodChallengePredictor:
                 "average_recall_iou_all_area_small_max_dets_1": cocoEval.stats[9],
                 "average_recall_iou_all_area_medium_max_dets_1": cocoEval.stats[10],
                 "average_recall_iou_all_area_large_max_dets_1": cocoEval.stats[11],
-            },
+            }
         }
         return _result_object
 
     def valid_categories(self):
         # List of valid categories to choose from
         # Description of categories is present in annotations.json file as part of dataset
-        VALID_CATEGORIES = [
-            50,
-            143,
-            158,
-            198,
-            232,
-            236,
-            259,
-            281,
-            282,
-            387,
-            483,
-            578,
-            629,
-            630,
-            633,
-            656,
-            727,
-            732,
-            733,
-            752,
-            780,
-            843,
-            870,
-            922,
-            929,
-            1004,
-            1007,
-            1009,
-            1010,
-            1013,
-            1014,
-            1019,
-            1020,
-            1021,
-            1022,
-            1024,
-            1026,
-            1032,
-            1033,
-            1038,
-            1040,
-            1050,
-            1054,
-            1055,
-            1056,
-            1058,
-            1060,
-            1061,
-            1062,
-            1065,
-            1068,
-            1069,
-            1070,
-            1074,
-            1075,
-            1076,
-            1078,
-            1082,
-            1084,
-            1085,
-            1086,
-            1089,
-            1090,
-            1091,
-            1092,
-            1093,
-            1094,
-            1098,
-            1102,
-            1107,
-            1108,
-            1111,
-            1112,
-            1113,
-            1115,
-            1116,
-            1119,
-            1120,
-            1121,
-            1123,
-            1124,
-            1125,
-            1126,
-            1130,
-            1134,
-            1138,
-            1141,
-            1143,
-            1144,
-            1150,
-            1151,
-            1152,
-            1153,
-            1154,
-            1156,
-            1157,
-            1158,
-            1162,
-            1163,
-            1164,
-            1166,
-            1167,
-            1169,
-            1170,
-            1174,
-            1175,
-            1176,
-            1180,
-            1181,
-            1184,
-            1186,
-            1187,
-            1190,
-            1191,
-            1198,
-            1199,
-            1200,
-            1201,
-            1203,
-            1204,
-            1205,
-            1206,
-            1207,
-            1208,
-            1209,
-            1210,
-            1211,
-            1212,
-            1213,
-            1214,
-            1215,
-            1216,
-            1218,
-            1219,
-            1220,
-            1221,
-            1222,
-            1223,
-            1228,
-            1229,
-            1237,
-            1249,
-            1253,
-            1256,
-            1257,
-            1264,
-            1266,
-            1280,
-            1282,
-            1290,
-            1293,
-            1294,
-            1295,
-            1300,
-            1307,
-            1308,
-            1309,
-            1310,
-            1311,
-            1321,
-            1323,
-            1325,
-            1327,
-            1328,
-            1337,
-            1346,
-            1348,
-            1352,
-            1366,
-            1367,
-            1371,
-            1376,
-            1383,
-            1384,
-            1402,
-            1411,
-            1422,
-            1453,
-            1454,
-            1455,
-            1456,
-            1463,
-            1467,
-            1468,
-            1469,
-            1471,
-            1478,
-            1479,
-            1482,
-            1483,
-            1487,
-            1488,
-            1490,
-            1492,
-            1494,
-            1496,
-            1500,
-            1505,
-            1506,
-            1509,
-            1513,
-            1517,
-            1520,
-            1522,
-            1523,
-            1528,
-            1533,
-            1536,
-            1538,
-            1545,
-            1546,
-            1547,
-            1551,
-            1554,
-            1556,
-            1557,
-            1559,
-            1560,
-            1561,
-            1565,
-            1566,
-            1568,
-            1569,
-            1572,
-            1580,
-            1584,
-            1587,
-            1588,
-            1592,
-            1595,
-            1607,
-            1612,
-            1614,
-            1615,
-            1616,
-            1620,
-            1626,
-            1627,
-            1670,
-            1695,
-            1696,
-            1707,
-            1711,
-            1724,
-            1725,
-            1727,
-            1728,
-            1730,
-            1731,
-            1748,
-            1749,
-            1757,
-            1760,
-            1765,
-            1770,
-            1788,
-            1789,
-            1791,
-            1793,
-            1794,
-            1831,
-            1835,
-            1837,
-            1838,
-            1845,
-            1849,
-            1853,
-            1856,
-            1857,
-            1879,
-            1883,
-            1886,
-            1889,
-            1893,
-            1895,
-            1908,
-            1914,
-            1915,
-            1916,
-            1917,
-            1919,
-            1924,
-            1942,
-            1948,
-            1956,
-            1958,
-            1967,
-            1975,
-            1980,
-            1985,
-            1986,
-            2002,
-            2003,
-            2022,
-            2031,
-            2053,
-            2056,
-            2062,
-            2073,
-            2099,
-            2103,
-            2113,
-            2115,
-            2131,
-            2132,
-            2133,
-            2134,
-            2135,
-            2171,
-            2172,
-            2184,
-            2194,
-            2203,
-            2211,
-            2237,
-            2254,
-            2259,
-            2262,
-            2269,
-            2278,
-            2300,
-            2303,
-            2312,
-            2320,
-            2333,
-            2340,
-            2350,
-            2355,
-            2362,
-            2376,
-            2388,
-            2395,
-            2400,
-            2408,
-            2413,
-            2446,
-            2452,
-            2454,
-            2461,
-            2467,
-            2468,
-            2470,
-            2495,
-            2498,
-            2501,
-            2504,
-            2512,
-            2513,
-            2518,
-            2521,
-            2524,
-            2530,
-            2534,
-            2543,
-            2546,
-            2548,
-            2553,
-            2555,
-            2562,
-            2563,
-            2577,
-            2578,
-            2580,
-            2585,
-            2588,
-            2605,
-            2607,
-            2610,
-            2616,
-            2618,
-            2620,
-            2634,
-            2636,
-            2711,
-            2714,
-            2716,
-            2718,
-            2719,
-            2728,
-            2729,
-            2730,
-            2731,
-            2734,
-            2736,
-            2738,
-            2740,
-            2741,
-            2742,
-            2743,
-            2744,
-            2747,
-            2749,
-            2750,
-            2751,
-            2752,
-            2760,
-            2767,
-            2768,
-            2773,
-            2778,
-            2791,
-            2807,
-            2810,
-            2811,
-            2815,
-            2836,
-            2837,
-            2840,
-            2841,
-            2846,
-            2852,
-            2855,
-            2859,
-            2873,
-            2895,
-            2896,
-            2898,
-            2899,
-            2900,
-            2905,
-            2906,
-            2913,
-            2918,
-            2920,
-            2923,
-            2930,
-            2932,
-            2934,
-            2935,
-            2939,
-            2941,
-            2944,
-            2947,
-            2949,
-            2952,
-            2954,
-            2959,
-            2960,
-            2961,
-            2962,
-            2964,
-            2966,
-            2967,
-            2968,
-            2970,
-            2973,
-            2990,
-            2991,
-            2994,
-            3042,
-            3046,
-            3055,
-            3080,
-            3082,
-            3085,
-            3100,
-            3101,
-            3115,
-            3181,
-            3220,
-            3221,
-            3228,
-            3230,
-            3248,
-            3249,
-            3258,
-            3262,
-            3293,
-            3306,
-            3308,
-            3332,
-            3337,
-            3358,
-            3392,
-            3399,
-            3415,
-            3416,
-            3417,
-            3474,
-            3532,
-            3615,
-            3630,
-            3739,
-            4335,
-            4338,
-            5247,
-            5618,
-            5641,
-            5689,
-            5748,
-            5792,
-            5812,
-            6404,
-            7504,
-            8025,
-            8730,
-            9594,
-            10626,
-        ]
+        VALID_CATEGORIES = [100022,100031,100049,100057,100059,100060,100063,100064,100067,100068,100069,100070,100072,100073,100076,100077,100078,100080,100082,100083,100084,100086,100089,100092,100093,100099,100101,100102,100104,100105,100107,100108,100111,100113,100114,100115,100123,100129,100130,100131,100132,100133,100135,100140,100141,100142,100143,100145,100146,100150,100151,100152,100156,100157,100158,100160,100161,100164,100171,100172,100173,100174,100175,100176,100177,100178,100179,100180,100181,100182,100183,100184,100185,100186,100190,100192,100195,100196,100200,100206,100224,100229,100234,100235,100243,100245,100249,100250,100264,100282,100300,100301,100302,100310,100314,100315,100318,100319,100321,100322,100324,100325,100332,100333,100334,100335,100337,100338,100346,100348,100349,100352,100355,100360,100364,100421,100423,100437,100442,100445,100456,100467,100475,100477,100486,100495,100523,100537,100546,100563,100594,100607,100645,100649,100652,100658,100674,100681,100687,100710,100719,100725,100742,100752,100757,100781,100790,100826,100834,100836,100838,100840,100842,100843,100844,100848,100859,100883,100895,100907,100909,100911,100916,100925,100926,100929,100936,100937,100942,100949,100951,100957,100958,100960,100962,100963,100966,100974,100978,100982,100992,100993,101006,101009,101012,101014,101022,101027,101029,101032,101038,101043,101077,101114,101118,101121,101123,101126,101129,101135,101138,101141,101144,101147,101148,101149,101150,101153,101156,101159,101164,101165,101166,101168,101170,101172,101173,101176,101177,101178,101180,101181,101182,101183,101185,101187,101188,101189,101190,101193,101194,101196,101197,101199,101200,101201,101208,101209,101210,101212,101213,101214,101215,101218,101219,101220,101228,101229,101231,101236,101237,101238,101240,101243,101244,101246,101247,101248,101249,101254,101255,101256,101257,101258,101260,101262,101265,101266,101268,101271,101272,101273,101274,101275,101279,101282,101283,101284,101285,101290,101291,101292,101294,101295,101296,101297,101298,101302,101303,101304,101305,101306,101307,101308,101309,101310,101311,101314,101317,101322,101323,101324,101325,101326,101327,101328,101329,101331,101335,101338,101340,101341,101343,101346,101347,101349,101354,101355,101358,101360,101361,101363,50]
         return VALID_CATEGORIES
diff --git a/evaluator/food_challenge_round_1.py b/evaluator/food_challenge_round_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..8593ed484f15d25365bb7862bdca5ee827a199af
--- /dev/null
+++ b/evaluator/food_challenge_round_1.py
@@ -0,0 +1,678 @@
+######################################################################################
+### This is a read-only file to allow participants to run their code locally.      ###
+### It will be over-writter during the evaluation, Please do not make any changes  ###
+### to this file.                                                                  ###
+######################################################################################
+
+import os
+import glob
+import json
+import traceback
+import numpy as np
+import pandas as pd
+from evaluator.utils import time_limit
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+
+class FoodChallengePredictor:
+    def __init__(self):
+        self.test_data_path = os.getenv(
+            "TEST_DATASET_PATH", os.getcwd() + "/data/images/"
+        )
+        self.results_data_path = os.getenv(
+            "RESULTS_DATASET_PATH", os.getcwd() + "/data/results/"
+        )
+        self.prediction_setup_timeout = int(
+            os.getenv("PREDICTION_SETUP_TIMEOUT_SECONDS", "120")
+        )
+        self.prediction_per_image_timeout = int(
+            os.getenv("PREDICTION_PER_IMAGE_TIMEOUT_SECONDS", "1")
+        )
+        self.is_online_run = False
+        self.predictions = []
+
+    def get_all_image_paths(self):
+        return glob.glob(os.path.join(self.test_data_path, "*.jpg"))
+
+    def update_status_to_evaluation_system(self, message):
+        pass
+
+    def evaluation(self):
+        """
+        Admin function: Runs the whole evaluation
+        """
+        self.update_status_to_evaluation_system("Running prediction_setup")
+        try:
+            with time_limit(self.prediction_setup_timeout):
+                self.prediction_setup()
+            self.update_status_to_evaluation_system("Completed prediction_setup")
+        except NotImplementedError:
+            self.update_status_to_evaluation_system(
+                "prediction_setup doesn't exist for this run, skipping..."
+            )
+
+        image_paths = self.get_all_image_paths()
+
+        for image_path in image_paths:
+            with time_limit(self.prediction_per_image_timeout):
+                prediction = self.prediction(image_path=image_path)
+            if isinstance(prediction, (list, pd.core.series.Series, np.ndarray)):
+                for i in prediction:
+                    self.add_prediction(i)
+            else:
+                self.add_prediction(prediction)
+
+        self.update_status_to_evaluation_system("Predictions generated")
+        self.save_predictions()
+
+    def add_prediction(self, prediction):
+        if "image_id" not in prediction:
+            prediction["image_id"] = int(
+                os.path.splitext(os.path.basename(prediction["image_path"]))[0]
+            )
+        self.validate_prediction(prediction)
+        self.predictions.append(prediction)
+
+    def validate_prediction(self, prediction):
+        assert prediction["image_id"] is not None
+        assert (
+            prediction["category_id"] is not None
+            and prediction["category_id"] in self.valid_categories()
+        )
+        assert prediction["score"] is not None and 0.0 <= prediction["score"] <= 1.0
+        assert prediction["segmentation"] is not None
+        assert prediction["bbox"] is not None
+
+    def save_predictions(self):
+        fp = open(os.path.join(self.results_data_path, "predictions.json"), "w")
+        fp.write(json.dumps(self.predictions))
+        fp.close()
+
+    def run(self):
+        try:
+            self.evaluation()
+        except Exception as e:
+            error = traceback.format_exc()
+            print(error)
+            if self.is_online_run:
+                raise e
+
+    def prediction_setup(self):
+        """
+        You can do any preprocessing required for your codebase here :
+            like loading your models into memory, etc.
+        """
+        raise NotImplementedError
+
+    def prediction(self, image_path):
+        """
+        This function will be called for all the images one by one during the evaluation.
+        NOTE: In case you want to load your model, please do so in `prediction_setup` function.
+        """
+        raise NotImplementedError
+
+    def _report(self, message):
+        print(message)
+
+    def scoring(self, ground_truth_path, predictions_file_path):
+        if not os.path.exists(predictions_file_path):
+            raise Exception(
+                "Invalid predictions_file_path provided : ".format(
+                    predictions_file_path
+                )
+            )
+
+        self._report("Loading Ground Truth Annotations...")
+        ground_truth_annotations = COCO(ground_truth_path)
+
+        self._report(
+            "Loading Predicted Annotations from : {}".format(predictions_file_path)
+        )
+        predictions = json.loads(open(predictions_file_path).read())
+        self._report("Predictions JSON read into memoery...")
+        assert (
+            len(predictions) > 0
+        ), "Predictions JSON file should have atleast 1 annotation"
+
+        self._report("Parsing Predictions......")
+        print("Length of annotations : ", len(predictions))
+        print("Ground truth File PATH : ", ground_truth_path)
+        print("Predictions File Path : ", predictions_file_path)
+
+        results = ground_truth_annotations.loadRes(predictions)
+        self._report("Predicted Annotations Loaded Successfully...")
+
+        self._report(
+            "Initiating evaluation. This might take a few minutes. Hang in there :D "
+        )
+        cocoEval = COCOeval(ground_truth_annotations, results, "segm")
+        cocoEval.evaluate()
+
+        self._report("Accumulating evaluation results....")
+        cocoEval.accumulate()
+        cocoEval.summarize()
+        _result_object = {
+            "score": cocoEval.stats[0],
+            "score_secondary": cocoEval.stats[8],
+            "meta": {
+                "average_precision_iou_all_area_all_max_dets_100": cocoEval.stats[0],
+                "average_precision_iou_5_area_all_max_dets_100": cocoEval.stats[1],
+                "average_precision_iou_75_area_all_max_dets_100": cocoEval.stats[2],
+                "average_precision_iou_all_area_small_max_dets_100": cocoEval.stats[3],
+                "average_precision_iou_all_area_medium_max_dets_100": cocoEval.stats[4],
+                "average_precision_iou_all_area_large_max_dets_100": cocoEval.stats[5],
+                "average_recall_iou_all_area_all_max_dets_1": cocoEval.stats[6],
+                "average_recall_iou_all_area_all_max_dets_10": cocoEval.stats[7],
+                "average_recall_iou_all_area_all_max_dets_100": cocoEval.stats[8],
+                "average_recall_iou_all_area_small_max_dets_1": cocoEval.stats[9],
+                "average_recall_iou_all_area_medium_max_dets_1": cocoEval.stats[10],
+                "average_recall_iou_all_area_large_max_dets_1": cocoEval.stats[11],
+            },
+        }
+        return _result_object
+
+    def valid_categories(self):
+        # List of valid categories to choose from
+        # Description of categories is present in annotations.json file as part of dataset
+        VALID_CATEGORIES = [
+            50,
+            143,
+            158,
+            198,
+            232,
+            236,
+            259,
+            281,
+            282,
+            387,
+            483,
+            578,
+            629,
+            630,
+            633,
+            656,
+            727,
+            732,
+            733,
+            752,
+            780,
+            843,
+            870,
+            922,
+            929,
+            1004,
+            1007,
+            1009,
+            1010,
+            1013,
+            1014,
+            1019,
+            1020,
+            1021,
+            1022,
+            1024,
+            1026,
+            1032,
+            1033,
+            1038,
+            1040,
+            1050,
+            1054,
+            1055,
+            1056,
+            1058,
+            1060,
+            1061,
+            1062,
+            1065,
+            1068,
+            1069,
+            1070,
+            1074,
+            1075,
+            1076,
+            1078,
+            1082,
+            1084,
+            1085,
+            1086,
+            1089,
+            1090,
+            1091,
+            1092,
+            1093,
+            1094,
+            1098,
+            1102,
+            1107,
+            1108,
+            1111,
+            1112,
+            1113,
+            1115,
+            1116,
+            1119,
+            1120,
+            1121,
+            1123,
+            1124,
+            1125,
+            1126,
+            1130,
+            1134,
+            1138,
+            1141,
+            1143,
+            1144,
+            1150,
+            1151,
+            1152,
+            1153,
+            1154,
+            1156,
+            1157,
+            1158,
+            1162,
+            1163,
+            1164,
+            1166,
+            1167,
+            1169,
+            1170,
+            1174,
+            1175,
+            1176,
+            1180,
+            1181,
+            1184,
+            1186,
+            1187,
+            1190,
+            1191,
+            1198,
+            1199,
+            1200,
+            1201,
+            1203,
+            1204,
+            1205,
+            1206,
+            1207,
+            1208,
+            1209,
+            1210,
+            1211,
+            1212,
+            1213,
+            1214,
+            1215,
+            1216,
+            1218,
+            1219,
+            1220,
+            1221,
+            1222,
+            1223,
+            1228,
+            1229,
+            1237,
+            1249,
+            1253,
+            1256,
+            1257,
+            1264,
+            1266,
+            1280,
+            1282,
+            1290,
+            1293,
+            1294,
+            1295,
+            1300,
+            1307,
+            1308,
+            1309,
+            1310,
+            1311,
+            1321,
+            1323,
+            1325,
+            1327,
+            1328,
+            1337,
+            1346,
+            1348,
+            1352,
+            1366,
+            1367,
+            1371,
+            1376,
+            1383,
+            1384,
+            1402,
+            1411,
+            1422,
+            1453,
+            1454,
+            1455,
+            1456,
+            1463,
+            1467,
+            1468,
+            1469,
+            1471,
+            1478,
+            1479,
+            1482,
+            1483,
+            1487,
+            1488,
+            1490,
+            1492,
+            1494,
+            1496,
+            1500,
+            1505,
+            1506,
+            1509,
+            1513,
+            1517,
+            1520,
+            1522,
+            1523,
+            1528,
+            1533,
+            1536,
+            1538,
+            1545,
+            1546,
+            1547,
+            1551,
+            1554,
+            1556,
+            1557,
+            1559,
+            1560,
+            1561,
+            1565,
+            1566,
+            1568,
+            1569,
+            1572,
+            1580,
+            1584,
+            1587,
+            1588,
+            1592,
+            1595,
+            1607,
+            1612,
+            1614,
+            1615,
+            1616,
+            1620,
+            1626,
+            1627,
+            1670,
+            1695,
+            1696,
+            1707,
+            1711,
+            1724,
+            1725,
+            1727,
+            1728,
+            1730,
+            1731,
+            1748,
+            1749,
+            1757,
+            1760,
+            1765,
+            1770,
+            1788,
+            1789,
+            1791,
+            1793,
+            1794,
+            1831,
+            1835,
+            1837,
+            1838,
+            1845,
+            1849,
+            1853,
+            1856,
+            1857,
+            1879,
+            1883,
+            1886,
+            1889,
+            1893,
+            1895,
+            1908,
+            1914,
+            1915,
+            1916,
+            1917,
+            1919,
+            1924,
+            1942,
+            1948,
+            1956,
+            1958,
+            1967,
+            1975,
+            1980,
+            1985,
+            1986,
+            2002,
+            2003,
+            2022,
+            2031,
+            2053,
+            2056,
+            2062,
+            2073,
+            2099,
+            2103,
+            2113,
+            2115,
+            2131,
+            2132,
+            2133,
+            2134,
+            2135,
+            2171,
+            2172,
+            2184,
+            2194,
+            2203,
+            2211,
+            2237,
+            2254,
+            2259,
+            2262,
+            2269,
+            2278,
+            2300,
+            2303,
+            2312,
+            2320,
+            2333,
+            2340,
+            2350,
+            2355,
+            2362,
+            2376,
+            2388,
+            2395,
+            2400,
+            2408,
+            2413,
+            2446,
+            2452,
+            2454,
+            2461,
+            2467,
+            2468,
+            2470,
+            2495,
+            2498,
+            2501,
+            2504,
+            2512,
+            2513,
+            2518,
+            2521,
+            2524,
+            2530,
+            2534,
+            2543,
+            2546,
+            2548,
+            2553,
+            2555,
+            2562,
+            2563,
+            2577,
+            2578,
+            2580,
+            2585,
+            2588,
+            2605,
+            2607,
+            2610,
+            2616,
+            2618,
+            2620,
+            2634,
+            2636,
+            2711,
+            2714,
+            2716,
+            2718,
+            2719,
+            2728,
+            2729,
+            2730,
+            2731,
+            2734,
+            2736,
+            2738,
+            2740,
+            2741,
+            2742,
+            2743,
+            2744,
+            2747,
+            2749,
+            2750,
+            2751,
+            2752,
+            2760,
+            2767,
+            2768,
+            2773,
+            2778,
+            2791,
+            2807,
+            2810,
+            2811,
+            2815,
+            2836,
+            2837,
+            2840,
+            2841,
+            2846,
+            2852,
+            2855,
+            2859,
+            2873,
+            2895,
+            2896,
+            2898,
+            2899,
+            2900,
+            2905,
+            2906,
+            2913,
+            2918,
+            2920,
+            2923,
+            2930,
+            2932,
+            2934,
+            2935,
+            2939,
+            2941,
+            2944,
+            2947,
+            2949,
+            2952,
+            2954,
+            2959,
+            2960,
+            2961,
+            2962,
+            2964,
+            2966,
+            2967,
+            2968,
+            2970,
+            2973,
+            2990,
+            2991,
+            2994,
+            3042,
+            3046,
+            3055,
+            3080,
+            3082,
+            3085,
+            3100,
+            3101,
+            3115,
+            3181,
+            3220,
+            3221,
+            3228,
+            3230,
+            3248,
+            3249,
+            3258,
+            3262,
+            3293,
+            3306,
+            3308,
+            3332,
+            3337,
+            3358,
+            3392,
+            3399,
+            3415,
+            3416,
+            3417,
+            3474,
+            3532,
+            3615,
+            3630,
+            3739,
+            4335,
+            4338,
+            5247,
+            5618,
+            5641,
+            5689,
+            5748,
+            5792,
+            5812,
+            6404,
+            7504,
+            8025,
+            8730,
+            9594,
+            10626,
+        ]
+        return VALID_CATEGORIES
diff --git a/predict_mmdetection.py b/predict_mmdetection.py
index 2056126f83d5278702034d6c970e6a561ecd90b9..ddec065bdf810387026b09b1d0086c492bdb5dd0 100644
--- a/predict_mmdetection.py
+++ b/predict_mmdetection.py
@@ -1,23 +1,21 @@
 #!/usr/bin/env python
 #
-# This file uses Detectron2 for instance segmentation.
+# This file uses MMdetection for instance segmentation.
 # It is one of the official baselines for the Food Recognition benchmark 2022 challenge.
 #
-# NOTE: Detectron2 needs the model and **its** aicrowd.json file to be submitted along with your code.
+# NOTE: MMdetection needs the model and **its** aicrowd.json file to be submitted along with your code.
 #
 # Making submission using Detectron2:
-# 1. Copy the aicrowd_detectron2.json from utils to home directory:
-#    #> cp utils/aicrowd_detectron2_example.json aicrowd.json
-# 2. Change the model in `predict.py` to Detectron2Predictor.
+# 1. Copy the aicrowd_mmdetection_example.json from utils to home directory:
+#    #> cp utils/aicrowd_mmdetection_example.json aicrowd.json
+# 2. Change the model in `predict.py` to MMDetectionPredictor.
 # 3. Download the pre-trained model from google drive into the folder `./models` using:
 #    #> mkdir models
 #    #> cd models
 #    #> pip install gdown
-#    ## To download model trained with "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml" architecture and score of 0.03 on leaderboard
-#    #> gdown --id 1ylaOzaI6qBfZbICA844uD74dKxLwcd0K --output model_final_mrcnn_x101.pth
-#    ## Next line will download "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" achitecture and score of 0.08
-#    #> gdown --id 1p5babyX6H80Rt8P8O2ts4g7SJihN2KtV --output model_final_mrcnn_resnet50.pth
-# 3. Submit your code using git-lfs
+#    ## To download model trained with "htc_without_semantic_r50_fpn_1x" architecture and score of 0.11 on leaderboard
+#    #> gdown --id 1dgsE-efNK8JlpwH911bSXCi9r6lXZAJq --output model_final_mrcnn_x101.pth
+# 4. Submit your code using git-lfs
 #    #> git lfs install
 #    #> git lfs track "*.pth"
 #    #> git add .gitattributes
@@ -51,7 +49,6 @@ from mmdet.apis import init_detector, inference_detector
 from mmdet.apis import multi_gpu_test
 from mmdet.datasets import build_dataloader, build_dataset, replace_ImageToTensor
 from mmdet.models import build_detector
-
 import pycocotools.mask as mask_util
 
 from utils.mmdet_inference import inference
@@ -86,14 +83,24 @@ class MMDetectionPredictor(FoodChallengePredictor):
         if self.cfg.get("cudnn_benchmark", False):
             torch.backends.cudnn.benchmark = True
         self.cfg.data.samples_per_gpu = 1
-        self.cfg.data.workers_per_gpu = 0
+        self.cfg.data.workers_per_gpu = 2
         self.cfg.model.pretrained = None
+        # For RFP based models we have another pretrained to set to None
+        if self.cfg.model.get("neck"):
+            if isinstance(self.cfg.model.neck, list):
+                for neck_cfg in self.cfg.model.neck:
+                    if neck_cfg.get("rfp_backbone"):
+                        if neck_cfg.rfp_backbone.get("pretrained"):
+                            neck_cfg.rfp_backbone.pretrained = None
+            elif self.cfg.model.neck.get("rfp_backbone"):
+                if self.cfg.model.neck.rfp_backbone.get("pretrained"):
+                    self.cfg.model.neck.rfp_backbone.pretrained = None
+
         self.cfg.data.test.test_mode = True
         self.cfg.data.test.ann_file = self.test_predictions_file.name
         self.cfg.data.test.img_prefix = self.test_data_path
 
-        self.model = init_detector(self.cfg_name, self.checkpoint_name, device="cuda:0")
-        # self.model = fuse_conv_bn(self.model)
+        self.model = init_detector(self.cfg, self.checkpoint_name, device="cuda:0")
 
         fp16_cfg = self.cfg.get("fp16", None)
         if fp16_cfg is not None:
@@ -148,10 +155,17 @@ class MMDetectionPredictor(FoodChallengePredictor):
                 data["bbox"] = self.xyxy2xywh(bboxes[i])
                 data["score"] = float(mask_score[i])
                 data["category_id"] = self.cat_ids[label]
+
                 if isinstance(segms[i]["counts"], bytes):
                     segms[i]["counts"] = segms[i]["counts"].decode()
                 data["segmentation"] = segms[i]
-                segm_json_results.append(data)
+
+                # This is only provided for participants to submit their v2.0 dataset models easily
+                # with v2.1 dataset.
+                # Please disable if you are submitting model trained on v2.1 dataset
+                data["category_id"] = self.v2_0_to_v2_1_mapping(data["category_id"])
+                if data["category_id"] is not None:
+                    segm_json_results.append(data)
         return segm_json_results
 
     def create_test_predictions(self, images_path):
@@ -180,6 +194,17 @@ class MMDetectionPredictor(FoodChallengePredictor):
         # config = Config.fromfile(config_fname)
         return (config_fname, checkpoint_fname)
 
+    def v2_0_to_v2_1_mapping(self, id):
+        if not hasattr(self, "mapping"):
+            self.old_to_new_mapping = json.loads(
+                open("utils/v2.1_breaking_class_mapping.json").read()
+            )
+        if str(id) in self.old_to_new_mapping:
+            id = self.old_to_new_mapping[str(id)]
+        if id not in self.valid_categories():
+            id = None
+        return id
+
 
 if __name__ == "__main__":
     submission = MMDetectionPredictor()
diff --git a/predict_mmdetection_round_1.py b/predict_mmdetection_round_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..2056126f83d5278702034d6c970e6a561ecd90b9
--- /dev/null
+++ b/predict_mmdetection_round_1.py
@@ -0,0 +1,187 @@
+#!/usr/bin/env python
+#
+# This file uses Detectron2 for instance segmentation.
+# It is one of the official baselines for the Food Recognition benchmark 2022 challenge.
+#
+# NOTE: Detectron2 needs the model and **its** aicrowd.json file to be submitted along with your code.
+#
+# Making submission using Detectron2:
+# 1. Copy the aicrowd_detectron2.json from utils to home directory:
+#    #> cp utils/aicrowd_detectron2_example.json aicrowd.json
+# 2. Change the model in `predict.py` to Detectron2Predictor.
+# 3. Download the pre-trained model from google drive into the folder `./models` using:
+#    #> mkdir models
+#    #> cd models
+#    #> pip install gdown
+#    ## To download model trained with "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml" architecture and score of 0.03 on leaderboard
+#    #> gdown --id 1ylaOzaI6qBfZbICA844uD74dKxLwcd0K --output model_final_mrcnn_x101.pth
+#    ## Next line will download "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" achitecture and score of 0.08
+#    #> gdown --id 1p5babyX6H80Rt8P8O2ts4g7SJihN2KtV --output model_final_mrcnn_resnet50.pth
+# 3. Submit your code using git-lfs
+#    #> git lfs install
+#    #> git lfs track "*.pth"
+#    #> git add .gitattributes
+#    #> git add models
+#
+
+import os
+import json
+import glob
+from PIL import Image
+import importlib
+import numpy as np
+import cv2
+import torch
+import traceback
+import pickle
+import shutil
+import glob
+import tempfile
+import time
+import mmcv
+import torch.distributed as dist
+from mmcv.image import tensor2imgs
+from mmdet.core import encode_mask_results
+from mmcv import Config, DictAction
+from mmcv.cnn import fuse_conv_bn
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import get_dist_info, init_dist, load_checkpoint, wrap_fp16_model
+from mmdet.apis import init_detector, inference_detector
+
+from mmdet.apis import multi_gpu_test
+from mmdet.datasets import build_dataloader, build_dataset, replace_ImageToTensor
+from mmdet.models import build_detector
+
+import pycocotools.mask as mask_util
+
+from utils.mmdet_inference import inference
+from evaluator.food_challenge import FoodChallengePredictor
+
+
+"""
+Expected ENVIRONMENT Variables
+* AICROWD_TEST_IMAGES_PATH : abs path to  folder containing all the test images
+* AICROWD_PREDICTIONS_OUTPUT_PATH : path where you are supposed to write the output predictions.json
+"""
+
+
+class MMDetectionPredictor(FoodChallengePredictor):
+
+    """
+    PARTICIPANT_TODO:
+    You can do any preprocessing required for your codebase here like loading up models into memory, etc.
+    """
+
+    def prediction_setup(self):
+        # self.PADDING = 50
+        # self.SEGMENTATION_LENGTH = 10
+        # self.MAX_NUMBER_OF_ANNOTATIONS = 10
+
+        # set the config parameters, including the architecture which was previously used
+        self.cfg_name, self.checkpoint_name = self.get_mmdetection_config()
+        self.cfg = Config.fromfile(self.cfg_name)
+        # self.test_img_path = os.getenv("AICROWD_TEST_IMAGES_PATH", os.getcwd() + "/data/images/")
+        self.test_predictions_file = self.create_test_predictions(self.test_data_path)
+
+        if self.cfg.get("cudnn_benchmark", False):
+            torch.backends.cudnn.benchmark = True
+        self.cfg.data.samples_per_gpu = 1
+        self.cfg.data.workers_per_gpu = 0
+        self.cfg.model.pretrained = None
+        self.cfg.data.test.test_mode = True
+        self.cfg.data.test.ann_file = self.test_predictions_file.name
+        self.cfg.data.test.img_prefix = self.test_data_path
+
+        self.model = init_detector(self.cfg_name, self.checkpoint_name, device="cuda:0")
+        # self.model = fuse_conv_bn(self.model)
+
+        fp16_cfg = self.cfg.get("fp16", None)
+        if fp16_cfg is not None:
+            wrap_fp16_model(self.model)
+
+        # Load annotations
+        with open(self.test_predictions_file.name) as f:
+            self.annotations = json.loads(f.read())
+        self.cat_ids = [category["id"] for category in self.annotations["categories"]]
+
+        self.model.CLASSES = [
+            category["name"] for category in self.annotations["categories"]
+        ]
+
+    """
+    PARTICIPANT_TODO:
+    During the evaluation all image file path will be provided one by one.
+    NOTE: In case you want to load your model, please do so in `predict_setup` function.
+    """
+
+    def prediction(self, image_path):
+        print("Generating for", image_path)
+        # read the image
+        result = inference(self.model, image_path)
+        # RLE Encode the masks
+        result = (result[0], encode_mask_results(result[1]))
+        result = self.segm2jsonformat(result, image_path)
+        return result
+
+    def xyxy2xywh(self, bbox):
+        _bbox = bbox.tolist()
+        return [
+            _bbox[0],
+            _bbox[1],
+            _bbox[2] - _bbox[0] + 1,
+            _bbox[3] - _bbox[1] + 1,
+        ]
+
+    def segm2jsonformat(self, result, image_path):
+        segm_json_results = []
+        img_id = int(os.path.basename(image_path).split(".")[0])
+        det, seg = result
+        # print("image:",img_id)
+        for label in range(len(det)):
+            bboxes = det[label]
+            # print(type(bboxes))
+            segms = seg[label]
+            mask_score = [bbox[4] for bbox in bboxes]
+            for i in range(len(bboxes)):
+                data = dict()
+                data["image_id"] = img_id
+                data["bbox"] = self.xyxy2xywh(bboxes[i])
+                data["score"] = float(mask_score[i])
+                data["category_id"] = self.cat_ids[label]
+                if isinstance(segms[i]["counts"], bytes):
+                    segms[i]["counts"] = segms[i]["counts"].decode()
+                data["segmentation"] = segms[i]
+                segm_json_results.append(data)
+        return segm_json_results
+
+    def create_test_predictions(self, images_path):
+        test_predictions_file = tempfile.NamedTemporaryFile(mode="w+", suffix=".json")
+        annotations = {"categories": [], "info": {}, "images": []}
+        for item in glob.glob(images_path + "/*.jpg"):
+            image_dict = dict()
+            img = mmcv.imread(item)
+            height, width, __ = img.shape
+            id = int(os.path.basename(item).split(".")[0])
+            image_dict["image_id"] = id
+            image_dict["file_name"] = os.path.basename(item)
+            image_dict["width"] = width
+            image_dict["height"] = height
+            annotations["images"].append(image_dict)
+        annotations["categories"] = json.loads(open("utils/classes.json").read())
+        json.dump(annotations, open(test_predictions_file.name, "w"))
+
+        return test_predictions_file
+
+    def get_mmdetection_config(self):
+        with open("aicrowd.json") as f:
+            content = json.load(f)
+            config_fname = content["model_config_file"]
+            checkpoint_fname = content["model_path"]
+        # config = Config.fromfile(config_fname)
+        return (config_fname, checkpoint_fname)
+
+
+if __name__ == "__main__":
+    submission = MMDetectionPredictor()
+    submission.run()
+    print("Successfully generated predictions!")
diff --git a/utils/v2.1_breaking_class_mapping.json b/utils/v2.1_breaking_class_mapping.json
new file mode 100644
index 0000000000000000000000000000000000000000..e2739678c264b71997b33b8e46d940d843a85c1c
--- /dev/null
+++ b/utils/v2.1_breaking_class_mapping.json
@@ -0,0 +1 @@
+{"1565": 101246, "2099": 100546, "2578": 101129, "1556": 101243, "1154": 100133, "1352": 101306, "1893": 101126, "1310": 101305, "1264": 100206, "1536": 101243, "2512": 101178, "1166": 101150, "2949": 101185, "2498": 101166, "2973": 100966, "1056": 100078, "1111": 100107, "2524": 101181, "1886": 101335, "2022": 100523, "1915": 101291, "1013": 100063, "2446": 101219, "1788": 101308, "1069": 100089, "1085": 101183, "259": 101279, "50": 50, "1078": 101188, "1143": 101311, "2743": 101214, "1487": 100321, "2730": 101275, "8025": 101100, "1483": 100319, "2738": 100838, "1311": 101302, "1157": 101347, "1213": 100184, "1210": 100182, "1144": 101282, "1022": 101144, "1203": 100175, "1151": 100130, "1169": 100145, "1061": 100083, "2171": 100593, "3332": 101355, "2115": 100552, "3181": 100989, "2053": 101194, "2555": 101166, "2750": 101284, "2836": 101314, "2618": 100790, "2620": 101165, "387": 100022, "1924": 101354, "2939": 101172, "1879": 100456, "1098": 101236, "3739": 101177, "2350": 100710, "2543": 101166, "1468": 101197, "2521": 101180, "1222": 100191, "1208": 100180, "1889": 101335, "1068": 101294, "1070": 101193, "1123": 101138, "2751": 100843, "1595": 100352, "1060": 100082, "3630": 101043, "5641": 101355, "3358": 101022, "2837": 101314, "2815": 100886, "1967": 100486, "2923": 100929, "1914": 101291, "1187": 100161, "1494": 101265, "1376": 101361, "2303": 100674, "1505": 101260, "1229": 100196, "1323": 100245, "1482": 100318, "1050": 101210, "1040": 101208, "1010": 101148, "1094": 100102, "1309": 100235, "1075": 100093, "1538": 101248, "727": 101358, "1327": 100249, "1102": 101236, "1566": 101255, "870": 101326, "1517": 100326, "732": 101215, "2840": 101314, "2859": 101123, "1727": 101292, "2967": 100963, "1055": 100077, "1522": 101243, "3399": 101026, "1121": 100112, "1321": 100243, "1496": 101258, "3415": 101027, "1180": 100156, "1176": 100152, "1058": 100080, "4335": 101050, "2918": 100925, "1757": 101349, "2964": 100960, "1032": 100072, "1569": 100338, "1500": 101299, "2320": 100687, "1980": 100495, "2131": 100563, "2711": 100826, "1199": 100172, "2470": 100757, "1174": 100150, "2395": 100731, "2461": 100752, "2269": 100652, "1837": 101310, "2966": 100962, "1942": 101177, "5792": 101082, "1958": 101312, "1086": 100099, "2930": 101176, "2841": 101317, "2944": 100942, "1204": 100176, "2513": 101178, "2807": 100883, "2742": 101212, "1506": 101262, "2736": 100836, "1113": 101340, "3221": 100993, "1009": 100060, "1731": 101292, "1453": 100300, "2553": 101166, "2530": 101166, "1554": 101254, "1116": 101285, "1112": 100108, "2952": 100949, "633": 101168, "2634": 101297, "3080": 100974, "1791": 100422, "1024": 100069, "1214": 101187, "8730": 101104, "1084": 101322, "733": 101215, "1228": 100195, "1218": 100188, "2991": 100967, "1547": 100333, "1513": 100325, "2728": 101272, "2031": 101344, "1455": 100302, "2714": 101268, "2607": 101289, "2899": 100910, "1560": 100335, "1985": 101279, "1367": 100278, "1090": 100100, "3262": 101009, "2504": 101182, "3042": 101156, "2454": 101220, "2577": 100779, "1607": 100355, "1293": 100224, "1592": 100350, "2172": 100594, "1348": 100264, "2580": 101129, "1215": 100185, "1917": 101291, "1545": 101243, "2340": 100703, "2846": 101125, "1190": 101346, "1164": 100142, "1728": 101292, "2811": 101343, "1469": 101197, "2935": 100937, "2959": 100955, "1065": 100086, "1120": 100111, "1092": 101360, "282": 101200, "3248": 101005, "3100": 100978, "1627": 101324, "2388": 101201, "1520": 101243, "1845": 100442, "1300": 100229, "2103": 101296, "1019": 100067, "2773": 100859, "2254": 100645, "1402": 101363, "2534": 101166, "2333": 100698, "2003": 100511, "2741": 101213, "1853": 100445, "1789": 100421, "1026": 100070, "1294": 101190, "2073": 100537, "2562": 101166, "1212": 100183, "780": 101141, "2947": 100945, "1089": 101338, "1126": 100115, "1091": 101298, "1916": 101291, "1307": 100234, "1454": 100301, "1198": 100171, "1760": 101349, "2194": 100607, "2501": 101114, "232": 101193, "1612": 101247, "1557": 101256, "2968": 100964, "1014": 100064, "1181": 100157, "2941": 101198, "1670": 101325, "281": 101199, "1956": 100477, "2970": 101135, "5618": 101069, "2259": 100647, "2905": 100916, "1748": 101349, "1152": 100131, "483": 101147, "1422": 101329, "2954": 100951, "5812": 101085, "1223": 100192, "2855": 101123, "10626": 101149, "1119": 101328, "1707": 101341, "1546": 101243, "2900": 100911, "1325": 100247, "158": 101236, "656": 101295, "2133": 100564, "1220": 101121, "1614": 100360, "1295": 101331, "2729": 101273, "2548": 101166, "1163": 100141, "2563": 101166, "3101": 101229, "3115": 100982, "1062": 100084, "1021": 101144, "1561": 101257, "1074": 100092, "2278": 100658, "1138": 100123, "1093": 100101, "2810": 101343, "1007": 100059, "2734": 100834, "1490": 100324, "1793": 101309, "2376": 100725, "3293": 101012, "1559": 100334, "1308": 101304, "2062": 100530, "3615": 101038, "1256": 101237, "2452": 101218, "2906": 101287, "2135": 101163, "2132": 101238, "1533": 100332, "9594": 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