Tensorflow 2 custom dataset Sequence

a_train.py

import tensorflow as tf
from data.data_generator import DataGenerator
from config import cfg

## Create train dataset
train_datagen = DataGenerator(file_path=cfg.TRAIN.DATA_PATH, config_path=cfg.TRAIN.ANNOTATION_PATH)

## Create validation dataset
val_generator = DataGenerator(file_path=cfg.TEST.DATA_PATH, config_path=cfg.TEST.ANNOTATION_PATH, debug=False)

model.fit_generator(generator=train_datagen,
                        epochs=cfg.TRAIN.EPOCHS,
                        callbacks=[# your callbacks for TF],
                        shuffle=True,
                        verbose=1)

config.py

from easydict import EasyDict

__C = EasyDict()

cfg = __C

# create NN dict
__C.NN = EasyDict()

__C.NN.INPUT_SIZE = 224

# create Train options dict
__C.TRAIN = EasyDict()
__C.TRAIN.DATA_PATH = "./data/WIDER_train/images/"
__C.TRAIN.ANNOTATION_PATH = "./data/wider_face_split/wider_face_train_bbx_gt.txt"
__C.TRAIN.BATCH_SIZE = 16

# create VAL options dict
__C.VAL = EasyDict()
__C.VAL.DATA_PATH = "./data/WIDER_val/images/"
__C.VAL.ANNOTATION_PATH = "./data/wider_face_split/wider_face_val_bbx_gt.txt"

dataset.py

import os
import sys
import math
import numpy as np
import tensorflow as tf
from config import cfg

# Input: [x0, y0, w, h, blur, expression, illumination, invalid, occlusion, pose]
# Output: x0, y0, w, h
def get_box(data):
    x0 = int(data[0])
    y0 = int(data[1])
    w = int(data[2])
    h = int(data[3])
    return x0, y0, w, h

class DataGenerator(tf.keras.utils.Sequence):

    def __init__(self, file_path, config_path, debug=False):
        self.boxes = []
        self.debug = debug
        self.data_path = file_path

        if not os.path.isfile(config_path):
            print("File path {} does not exist. Exiting...".format(config_path))
            sys.exit()
        
        if not os.path.isdir(file_path):
            print("Images folder path {} does not exist. Exiting...".format(file_path))
            sys.exit()
            
        with open(config_path) as fp:
            image_name = fp.readline()
            cnt = 1
            while image_name:
                num_of_obj = int(fp.readline())
                for i in range(num_of_obj):
                    obj_box = fp.readline().split(' ')
                    x0, y0, w, h = get_box(obj_box)
                    if w == 0:
                        # remove boxes with no width
                        continue
                    if h == 0:
                        # remove boxes with no height
                        continue
                    self.boxes.append((image_name.strip(), x0, y0, w, h))
                if num_of_obj == 0:
                    obj_box = fp.readline().split(' ')
                    x0, y0, w, h = get_box(obj_box)
                    self.boxes.append((image_name.strip(), x0, y0, w, h))
                image_name = fp.readline()
                cnt += 1
        
    def __len__(self):
        return math.ceil(len(self.boxes) / cfg.TRAIN.BATCH_SIZE)

    def __getitem__(self, idx):
        boxes = self.boxes[idx * cfg.TRAIN.BATCH_SIZE:(idx + 1) * cfg.TRAIN.BATCH_SIZE]
        batch_images = np.zeros((len(boxes), cfg.NN.INPUT_SIZE, cfg.NN.INPUT_SIZE, 3), dtype=np.float32)
        batch_boxes = np.zeros((len(boxes), cfg.NN.GRID_SIZE, cfg.NN.GRID_SIZE, 5), dtype=np.float32)
        for i, row in enumerate(boxes):
            path, x0, y0, w, h = row
            
            proc_image = tf.keras.preprocessing.image.load_img(self.data_path + path)
            
            image_width = proc_image.width
            image_height = proc_image.height
            
            proc_image = tf.keras.preprocessing.image.load_img(self.data_path + path,
                                                           target_size=(cfg.NN.INPUT_SIZE, cfg.NN.INPUT_SIZE))
            
            proc_image = tf.keras.preprocessing.image.img_to_array(proc_image)
            proc_image = np.expand_dims(proc_image, axis=0)
            proc_image - tf.keras.applications.mobilenet_v2.preprocess_input(proc_image)
            
            batch_images[i] = proc_image
            
            # make sure none of the points is out of image border
            x0 = max(x0, 0)
            y0 = max(y0, 0)
        
            x0 = min(x0, image_width)
            y0 = min(y0, image_height)
        
            x_c = (cfg.NN.GRID_SIZE / image_width) * x0
            y_c = (cfg.NN.GRID_SIZE / image_height) * y0
        
            floor_y = math.floor(y_c)  # handle case when x i on the corner
            floor_x = math.floor(x_c)  # handle case when y i on the corner
        
            batch_boxes[i, floor_y, floor_x, 0] = h / image_height
            batch_boxes[i, floor_y, floor_x, 1] = w / image_width
            batch_boxes[i, floor_y, floor_x, 2] = y_c - floor_y
            batch_boxes[i, floor_y, floor_x, 3] = x_c - floor_x
            batch_boxes[i, floor_y, floor_x, 4] = 1
        
        return batch_images, batch_boxes

Thank you very much for your help !

Hello,
I hope you are doing good.
I downloaded this app that you developed (https://github.com/burnpiro/tiny-face-detection-tensorflow2), the data and so on...
I trained a model without modifying the code and I have a very very poor val_iou value so that the algorithm is not able to detect faces.

Do you have some local changes that you did not push ?