Custum Dataset MOT

[athairrahman]

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Question

Based on this issue #927 (comment) where is the location of the file that I have to adjust again and also want to do a custom dataset, where the classes that I want to track are 5, namely bus, motorcycle, car, motorized-trishaws, truck, I want to do an evaluation using Yolov10n with BoostTrack,

the following is the gt.txt file

and this is the image file

[mikel-brostrom]

You need a dataset in the following format:

Then you can run:

boxmot eval --yolo-model yolov10n.pt --tracking-method boosttrack --reid-model lmbn_n_duke.pt  --source /path/to/boxmot/engine/trackeval/data/custom-data/train  --verbose

[athairrahman]

Thank you in advance for your help.
However, regarding the dataset configuration, what files are contained in the det/det.txt folder?

I want to evaluate a total of five classes: car, motorcycle, bus, motorized_trishaws, and truck.

Do I need to configure the mot_challenge_2d_box.py file as below? And are there other files, such as val.py or track.py, so that the tracking performance evaluation process works according to each class?

please provide input

import os
import csv
import configparser
import numpy as np
from scipy.optimize import linear_sum_assignment
from ._base_dataset import _BaseDataset
from .. import utils
from .. import _timing
from ..utils import TrackEvalException


class MotChallenge2DBox(_BaseDataset):
    """Dataset class for MOT Challenge 2D bounding box tracking"""

    @staticmethod
    def get_default_dataset_config():
        """Default class config values"""
        code_path = utils.get_code_path()
        default_config = {
            'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data
            # 'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location
            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location
            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
            'TRACKERS_TO_EVAL': ['labels'],  # Filenames of trackers to eval (if None, all in folder)
            # 'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']
            'CLASSES_TO_EVAL': ['car', 'motorcycle', 'bus', 'motorized-trishaws', 'truck'],  # Valid: ['pedestrian']
            
            # 'CLASSES_TO_EVAL': ['pedestrian', 'car', 'motorcycle', 'bus', 'truck'],
            
            'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'
            'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'
            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
            'PRINT_CONFIG': True,  # Whether to print current config
            'DO_PREPROC': True,  # Whether to perform preprocessing (never done for MOT15)
            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)
            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/benchmark-split_to_eval)
            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps
            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'
            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/BENCHMARK-SPLIT_TO_EVAL/ and in
                                      # TRACKERS_FOLDER/BENCHMARK-SPLIT_TO_EVAL/tracker/
                                      # If True, then the middle 'benchmark-split' folder is skipped for both.
        }
        return default_config

    def __init__(self, config=None):
        """Initialise dataset, checking that all required files are present"""
        super().__init__()
        # Fill non-given config values with defaults
        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())

        self.benchmark = self.config['BENCHMARK']
        gt_set = self.config['BENCHMARK'] + '-' + self.config['SPLIT_TO_EVAL']
        self.gt_set = gt_set
        if not self.config['SKIP_SPLIT_FOL']:
            split_fol = gt_set
        else:
            split_fol = ''
        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)
        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], split_fol)
        self.should_classes_combine = False
        self.use_super_categories = False
        self.data_is_zipped = self.config['INPUT_AS_ZIP']
        self.do_preproc = self.config['DO_PREPROC']

        self.output_fol = self.config['OUTPUT_FOLDER']
        if self.output_fol is None:
            self.output_fol = self.tracker_fol

        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']
        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']

        # Get classes to eval
        # self.valid_classes = ['pedestrian']
        self.valid_classes = ['car', 'motorcycle', 'bus', 'motorized-trishaws', 'truck']
        # self.valid_classes = ['pedestrian', 'car', 'motorcycle', 'bus', 'truck'],
        
        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None
                           for cls in self.config['CLASSES_TO_EVAL']]
        # if not all(self.class_list):
        #     raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')
        
        # self.class_name_to_class_id = {'pedestrian': 1, 'person_on_vehicle': 2, 'car': 3, 'bicycle': 4, 'motorbike': 5,
        #                                'non_mot_vehicle': 6, 'static_person': 7, 'distractor': 8, 'occluder': 9,
        #                                'occluder_on_ground': 10, 'occluder_full': 11, 'reflection': 12, 'crowd': 13}
        
        #self.class_name_to_class_id = {'car':2,'motorcycle':3,'bus':5,'truck':7,'motorized-trishaws':18, 'person_on_vehicle': 1, 'non_mot_vehicle': 6,'static_person': 8,'distractor': 9,'occluder': 10,'occluder_on_ground': 11,'occluder_full': 12,'reflection': 13,'crowd': 14,}
        
        self.class_name_to_class_id = {'car': 0, 'motorcycle': 1, 'bus': 2, 'motorized-trishaws': 3, 'truck': 4}
        
        self.valid_class_numbers = list(self.class_name_to_class_id.values())

        # Get sequences to eval and check gt files exist
        self.seq_list, self.seq_lengths = self._get_seq_info()
        if len(self.seq_list) < 1:
            raise TrackEvalException('No sequences are selected to be evaluated.')

        # Check gt files exist
        for seq in self.seq_list:
            if not self.data_is_zipped:
                curr_file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
                if not os.path.isfile(curr_file):
                    print('GT file not found ' + curr_file)
                    raise TrackEvalException('GT file not found for sequence: ' + seq)
        if self.data_is_zipped:
            curr_file = os.path.join(self.gt_fol, 'data.zip')
            if not os.path.isfile(curr_file):
                print('GT file not found ' + curr_file)
                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))

        # Get trackers to eval
        if self.config['TRACKERS_TO_EVAL'] is None:
            self.tracker_list = os.listdir(self.tracker_fol)
        else:
            self.tracker_list = self.config['TRACKERS_TO_EVAL']

        if self.config['TRACKER_DISPLAY_NAMES'] is None:
            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (
                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):
            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))
        else:
            raise TrackEvalException('List of tracker files and tracker display names do not match.')

        for tracker in self.tracker_list:
            if self.data_is_zipped:
                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
                if not os.path.isfile(curr_file):
                    print('Tracker file not found: ' + curr_file)
                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))
            else:
                for seq in self.seq_list:
                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
                    if not os.path.isfile(curr_file):
                        print('Tracker file not found: ' + curr_file)
                        raise TrackEvalException(
                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(
                                curr_file))

    def get_display_name(self, tracker):
        return self.tracker_to_disp[tracker]

    def _get_seq_info(self):
        seq_list = []
        seq_lengths = {}
        if self.config["SEQ_INFO"]:
            seq_list = list(self.config["SEQ_INFO"].keys())
            seq_lengths = self.config["SEQ_INFO"]

            # If sequence length is 'None' tries to read sequence length from .ini files.
            for seq, seq_length in seq_lengths.items():
                if seq_length is None:
                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
                    if not os.path.isfile(ini_file):
                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
                    ini_data = configparser.ConfigParser()
                    ini_data.read(ini_file)
                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])

        else:
            if self.config["SEQMAP_FILE"]:
                seqmap_file = self.config["SEQMAP_FILE"]
            else:
                if self.config["SEQMAP_FOLDER"] is None:
                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')
                else:
                    seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], self.gt_set + '.txt')
            if not os.path.isfile(seqmap_file):
                print('no seqmap found: ' + seqmap_file)
                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
            with open(seqmap_file) as fp:
                reader = csv.reader(fp)
                for i, row in enumerate(reader):
                    if i == 0 or row[0] == '':
                        continue
                    seq = row[0]
                    seq_list.append(seq)
                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
                    if not os.path.isfile(ini_file):
                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
                    ini_data = configparser.ConfigParser()
                    ini_data.read(ini_file)
                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])
        return seq_list, seq_lengths

    def _load_raw_file(self, tracker, seq, is_gt):
        """Load a file (gt or tracker) in the MOT Challenge 2D box format

        If is_gt, this returns a dict which contains the fields:
        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).
        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.
        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).

        if not is_gt, this returns a dict which contains the fields:
        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).
        [tracker_dets]: list (for each timestep) of lists of detections.
        """
        # File location
        if self.data_is_zipped:
            if is_gt:
                zip_file = os.path.join(self.gt_fol, 'data.zip')
            else:
                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
            file = seq + '.txt'
        else:
            zip_file = None
            if is_gt:
                file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
            else:
                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')

        # Load raw data from text file
        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file)

        # Convert data to required format
        num_timesteps = self.seq_lengths[seq]
        data_keys = ['ids', 'classes', 'dets']
        if is_gt:
            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']
        else:
            data_keys += ['tracker_confidences']
        raw_data = {key: [None] * num_timesteps for key in data_keys}

        # Check for any extra time keys
        current_time_keys = [str( t+ 1) for t in range(num_timesteps)]
        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]
        if len(extra_time_keys) > 0:
            if is_gt:
                text = 'Ground-truth'
            else:
                text = 'Tracking'
            raise TrackEvalException(
                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(
                    [str(x) + ', ' for x in extra_time_keys]))

        for t in range(num_timesteps):
            time_key = str(t+1)
            if time_key in read_data.keys():
                try:
                    time_data = np.asarray(read_data[time_key], dtype=float)
                except ValueError:
                    if is_gt:
                        raise TrackEvalException(
                            'Cannot convert gt data for sequence %s to float. Is data corrupted?' % seq)
                    else:
                        raise TrackEvalException(
                            'Cannot convert tracking data from tracker %s, sequence %s to float. Is data corrupted?' % (
                                tracker, seq))
                try:
                    raw_data['dets'][t] = np.atleast_2d(time_data[:, 2:6])
                    raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
                except IndexError:
                    if is_gt:
                        err = 'Cannot load gt data from sequence %s, because there is not enough ' \
                              'columns in the data.' % seq
                        raise TrackEvalException(err)
                    else:
                        err = 'Cannot load tracker data from tracker %s, sequence %s, because there is not enough ' \
                              'columns in the data.' % (tracker, seq)
                        raise TrackEvalException(err)
                if time_data.shape[1] >= 8:
                    raw_data['classes'][t] = np.atleast_1d(time_data[:, 7]).astype(int)
                else:
                    if not is_gt:
                        raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])
                    else:
                        raise TrackEvalException(
                            'GT data is not in a valid format, there is not enough rows in seq %s, timestep %i.' % (
                                seq, t))
                if is_gt:
                    gt_extras_dict = {'zero_marked': np.atleast_1d(time_data[:, 6].astype(int))}
                    raw_data['gt_extras'][t] = gt_extras_dict
                else:
                    raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 6])
            else:
                raw_data['dets'][t] = np.empty((0, 4))
                raw_data['ids'][t] = np.empty(0).astype(int)
                raw_data['classes'][t] = np.empty(0).astype(int)
                if is_gt:
                    gt_extras_dict = {'zero_marked': np.empty(0)}
                    raw_data['gt_extras'][t] = gt_extras_dict
                else:
                    raw_data['tracker_confidences'][t] = np.empty(0)
            if is_gt:
                raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))

        if is_gt:
            key_map = {'ids': 'gt_ids',
                       'classes': 'gt_classes',
                       'dets': 'gt_dets'}
        else:
            key_map = {'ids': 'tracker_ids',
                       'classes': 'tracker_classes',
                       'dets': 'tracker_dets'}
        for k, v in key_map.items():
            raw_data[v] = raw_data.pop(k)
        raw_data['num_timesteps'] = num_timesteps
        raw_data['seq'] = seq
        return raw_data

    @_timing.time
    def get_preprocessed_seq_data(self, raw_data, cls):
        """ Preprocess data for a single sequence for a single class ready for evaluation.
        Inputs:
             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().
             - cls is the class to be evaluated.
        Outputs:
             - data is a dict containing all of the information that metrics need to perform evaluation.
                It contains the following fields:
                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.
                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).
                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.
                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
        Notes:
            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.
                1) Extract only detections relevant for the class to be evaluated (including distractor detections).
                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a
                    distractor class, or otherwise marked as to be removed.
                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain
                    other criteria (e.g. are too small).
                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.
            After the above preprocessing steps, this function also calculates the number of gt and tracker detections
                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are
                unique within each timestep.

        MOT Challenge:
            In MOT Challenge, the 4 preproc steps are as follow:
                1) There is only one class (pedestrian) to be evaluated, but all other classes are used for preproc.
                2) Predictions are matched against all gt boxes (regardless of class), those matching with distractor
                    objects are removed.
                3) There is no crowd ignore regions.
                4) All gt dets except pedestrian are removed, also removes pedestrian gt dets marked with zero_marked.
        """
        # Check that input data has unique ids
        self._check_unique_ids(raw_data)

        distractor_class_names = ['person_on_vehicle', 'static_person', 'distractor', 'reflection']
        if self.benchmark == 'MOT20':
            distractor_class_names.append('non_mot_vehicle')
        distractor_classes = [self.class_name_to_class_id[x] for x in distractor_class_names]
        cls_id = self.class_name_to_class_id[cls]

        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']
        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}
        unique_gt_ids = []
        unique_tracker_ids = []
        num_gt_dets = 0
        num_tracker_dets = 0
        for t in range(raw_data['num_timesteps']):

            # Get all data
            gt_ids = raw_data['gt_ids'][t]
            gt_dets = raw_data['gt_dets'][t]
            gt_classes = raw_data['gt_classes'][t]
            gt_zero_marked = raw_data['gt_extras'][t]['zero_marked']

            tracker_ids = raw_data['tracker_ids'][t]
            tracker_dets = raw_data['tracker_dets'][t]
            tracker_classes = raw_data['tracker_classes'][t]
            tracker_confidences = raw_data['tracker_confidences'][t]
            similarity_scores = raw_data['similarity_scores'][t]
            # tracker_classes = [0,1,2,3,4]

            # Evaluation is ONLY valid for pedestrian class
            # if len(tracker_classes) > 0 and np.max(tracker_classes) > 1:
            #     raise TrackEvalException(
            #         'Evaluation is only valid for pedestrian class. Non pedestrian class (%i) found in sequence %s at '
            #         'timestep %i.' % (np.max(tracker_classes), raw_data['seq'], t))

            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
            # which are labeled as belonging to a distractor class.
            to_remove_tracker = np.array([], int)
            if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:

                # Check all classes are valid:
                invalid_classes = np.setdiff1d(np.unique(gt_classes), self.valid_class_numbers)
                if len(invalid_classes) > 0:
                    print(' '.join([str(x) for x in invalid_classes]))
                    raise(TrackEvalException('Attempting to evaluate using invalid gt classes. '
                                             'This warning only triggers if preprocessing is performed, '
                                             'e.g. not for MOT15 or where prepropressing is explicitly disabled. '
                                             'Please either check your gt data, or disable preprocessing. '
                                             'The following invalid classes were found in timestep ' + str(t) + ': ' +
                                             ' '.join([str(x) for x in invalid_classes])))

                matching_scores = similarity_scores.copy()
                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0
                match_rows, match_cols = linear_sum_assignment(-matching_scores)
                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
                match_rows = match_rows[actually_matched_mask]
                match_cols = match_cols[actually_matched_mask]

                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)
                to_remove_tracker = match_cols[is_distractor_class]

            # Apply preprocessing to remove all unwanted tracker dets.
            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)
            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)
            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)

            # Remove gt detections marked as to remove (zero marked), and also remove gt detections not in pedestrian
            # class (not applicable for MOT15)
            if self.do_preproc and self.benchmark != 'MOT15':
                gt_to_keep_mask = (np.not_equal(gt_zero_marked, 0)) & \
                                  (np.equal(gt_classes, cls_id))
            else:
                # There are no classes for MOT15
                gt_to_keep_mask = np.not_equal(gt_zero_marked, 0)
            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]
            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]
            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]

            unique_gt_ids += list(np.unique(data['gt_ids'][t]))
            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))
            num_tracker_dets += len(data['tracker_ids'][t])
            num_gt_dets += len(data['gt_ids'][t])

        # Re-label IDs such that there are no empty IDs
        if len(unique_gt_ids) > 0:
            unique_gt_ids = np.unique(unique_gt_ids)
            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
            for t in range(raw_data['num_timesteps']):
                if len(data['gt_ids'][t]) > 0:
                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
        if len(unique_tracker_ids) > 0:
            unique_tracker_ids = np.unique(unique_tracker_ids)
            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
            for t in range(raw_data['num_timesteps']):
                if len(data['tracker_ids'][t]) > 0:
                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

        # Record overview statistics.
        data['num_tracker_dets'] = num_tracker_dets
        data['num_gt_dets'] = num_gt_dets
        data['num_tracker_ids'] = len(unique_tracker_ids)
        data['num_gt_ids'] = len(unique_gt_ids)
        data['num_timesteps'] = raw_data['num_timesteps']
        data['seq'] = raw_data['seq']

        # Ensure again that ids are unique per timestep after preproc.
        self._check_unique_ids(data, after_preproc=True)

        return data

    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='xywh')
        return similarity_scores

and this is an example of a dataset that I have tried to export in MOT 1.1 format using the CVAT tool

[mikel-brostrom]

I am working on a final solution for this here: #2128
It partially works. But I am unable to match the eval results to what is in master. It can now evaluate per class. It basically patches trackeval at runtime

[mikel-brostrom]

I get these metrics:

Single class

Format      Status❔  HOTA    MOTA    IDF1
Deepocsort  ✅        43.511  21.462  37.757
Bytetrack   ✅        42.073  20.283  35.741
Boosttrack  ✅        40.377  17.925  38.078
Ocsort      ✅        39.125  17.217  31.579
Botsort     ✅        34.367  12.264  24.39
Strongsort  ✅        27.971  12.028  23.409

Multi-class eval

Format      Status❔  HOTA    MOTA    IDF1
Deepocsort  ✅        43.411  20.991  37.616
Bytetrack   ✅        42.073  20.283  35.741
Boosttrack  ✅        40.774  19.811  38.182
Ocsort      ✅        39.125  17.217  31.579
Botsort     ✅        34.367  12.264  24.39
Strongsort  ✅        null    null    null

Need to look into the details of this

[mikel-brostrom]

Fully working now in this branch

patch-trackeval-for-enabling-multi-class-eval

[mikel-brostrom]

In order to get this to work with your model:

1. Adapt to your model classes
   self.class_name_to_class_id
2. Adapt if any in your dataset
   distractor_classes = [12, 8, 6, 7, 2] if self.benchmark == 'MOT20' else [12, 8, 7, 2], set to [] if none
3. Adapt to the classes you want to evaluate out of all your classes
   "'CLASSES_TO_EVAL': [\n \"person\"\n ], # Valid: any class names (patched)",

In here:

https://github.com/mikel-brostrom/boxmot/blob/patch-trackeval-for-enabling-multi-class-eval/boxmot/utils/trackeval_patch.py

[athairrahman]

Thanks for the input. So, what are the steps to evaluate the performance of a custom class after configuring trackeval_patch.py?

python 3.10.0

[blaisefinix]

Perhaps here I would like to clarify the use of custom datasets. After trying it in Boxmot version 11.0.8, which worked, I have now upgraded to version 15.0.2.

I have seen many cases where there have been failures in evaluating custom datasets for tracking in generating HOTA, MOTA, and IDF1.

I see that you update the library regularly. However, the custom dataset issue persists. Here, I would like to help resolve it. Based on the previous case when I used version 11.0.8, it still applies when using version 15.0.2 or the latest version. This is because the trackeval used is essentially the same as before (without significant updates).

Therefore, to resolve the issue, I will thoroughly explain the solution following the same pattern:
#927 (comment)

which requires modifying mot_challenge_2d_box.py in several parts of the class to make it valid for multi-class by following the instructions above. Here is an example of the configuration

in here , you must empty disctractor class to be able evaluate the custom dataset

and just comment out this line of code

then for dataset configuration with the same structure as MOT17 or the latest version of the dataset that has the same configuration. However, the dataset generated from the CVAT tool does not produce a det folder. Therefore, the custom dataset implementation only uses img1 and gt with the same content as in MOT. Here is the configuration

Then, here is the configuration of one seqinfo.ini that does not require all the same content as in the popular MOT17 dataset if you want to actually run a custom dataset.

After that, you must note that the img1 folder containing the frame sequence cannot use the PNG format. In my experience, using this format causes errors. So, after conducting various experiments, I found that the only custom dataset that can be evaluated is in JPG format. Therefore, try to convert to this format.

After configuring all of that, the evaluation should be able to run. the following are the result obtained

To run multiclass, you may need to adjust it to the updated CLI.
NOTE: Try to follow the steps very slowly and carefully. Otherwise, you will experience continuous failed experiments. even though it's a simple problem, that's where the key to success lies

Hopefully, this can help answer all questions regarding the use of custom datasets. Thank you @mikel-brostrom for providing this modular repository. cheer up @athairrahman for this research 💫

[mikel-brostrom]

Thank you for your thorough explanation @blaisefinix. Indeed this is the case. The approach I am following, given that trackeval is downloaded and not part of this repo, is that I patch trackeval just as you presented here. The draft for this is here: #2128. The way of working with this would be that you perform modification to the patcher.