how to create outputs for key points of bounding boxes on image in Neural network in Python I've made my first convolutional neural network which can classify one object on image (220, 220). The objects classes are only two - dogs and cats. I have 3385 '.jpg' pictures of dogs and cats on my PC. I have information about class of each object (1 or 0). Also I have information about two corners coordinates (from 0.0 to 1.0) of bounding boxes (0.0 is left/top corner of image and 1.0 is right/bottom corner of image) around of the objects. Convolutional net must have 5 outputs (1 output for classes and 4 outputs for two corners coordinates: x1,y1,x2,y2).
I have 3047 train images 'x_train' shape (3047, 220, 220, 3) and labels information in 'y_train' shape (3047, 5). Also 338 test images in 'x_test' shape (338, 220, 220, 3) and test labels in 'y_test'. For example, y_test[0] has such elements array([1.        , 0.555     , 0.18      , 0.70833333, 0.395     ]).
But my neural network have such prediction results prediction[0] is array([276.47525 ,  51.961666,  20.63266 , 125.419945, 106.816345],dtype=float32). And np.sum(prediction) is 338.0 which is number of test images. During the training parameters increased from  'loss: 4.8534 - accuracy: 0.5938' to 'loss: 533785.6875 - accuracy: 1.0000'. Could anyone help me with an advice what do I do wrong?
#Summary:
#Model: &quot;sequential_1&quot;
#conv2d_2 (Conv2D)            (None, 220, 220, 32)      896       
#max_pooling2d_2 (MaxPooling2 (None, 110, 110, 32)      0 
#conv2d_3 (Conv2D)            (None, 110, 110, 64)      18496     
#max_pooling2d_3 (MaxPooling2 (None, 55, 55, 64)        0         
#flatten_1 (Flatten)          (None, 193600)            0         
#dense_2 (Dense)              (None, 128)               24780928  
#dense_3 (Dense)              (None, 5)                 645 
#Total params: 24,800,965
#Trainable params: 24,800,965
#Non-trainable params: 0

import os
import cv2
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras.layers import Dense, Flatten, Dropout, Conv2D, MaxPooling2D
import time

model = keras.Sequential([
    Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(220, 220, 3)),
    MaxPooling2D((2, 2), strides=2),
    Conv2D(64, (3, 3), padding='same', activation='relu'),
    MaxPooling2D((2, 2), strides=2),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(5, activation='relu')

])

print(model.summary())
model.compile(optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy'])
his = model.fit(x_train, y_train, batch_size=32, epochs=2, validation_split=0.5)
model.evaluate(x_test, y_test)
Your network can be improved in many ways :
Adding a few convolutional layers could help reduce the dimension as you have a lot a weights on your first FC layer, and this will improve the speed of your training.
But your main problem is your loss function, cross entropy is used for classification, which means your network is considering all your 5 ouputs as classes (a softmax function is applied to all 5 of them before computing cross entropy).
For your task, i would feed the outputs of the convolutional network into 2 networks:

One is the classifier that tells you if it is a cat or a dog (1 output).
The second outputs the bounding box (4 outputs).

These networks can be trained independently using different losses and will give you coherent results.