How to evaluate a pic saved in my desktop

Research & Models
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#1

I created a model that works fine. It’s a model of clothes classification, unfortunately, I don’t know how to use it with pictures that I download from the internet and they don’t share the same size. Do you know how to do it?
This is what I tried and it does not work.
from PIL import Image

import numpy as np

image = Image.open(r’C:\Users\tripa\OneDrive\desktop\remera.jpg’)

new_image = image.resize((28, 28))

pred_probs=model_14.predict(new_image)

print(pred_probs)

#3

There are the following six steps to determine what object does the image contains?

  • Load an image
  • Resize it to a predefined size since pre-trained models expect the input to be of a specific size
  • Scale the value of the pixels to the range [0, 255]
  • Select a pre-trained model
  • Run the pre-trained model
  • Display the results

Complete working code to predict new image using resnet-50 pre-trained model

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

def classify_image(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    img_array = image.img_to_array(img)

    img_batch = np.expand_dims(img_array, axis=0)

    img_preprocessed = preprocess_input(img_batch)

    model = tf.keras.applications.resnet50.ResNet50()
    prediction = model.predict(img_preprocessed)

    print(decode_predictions(prediction, top=3)[0])

classify("/content/jersey.jpeg")

Output:

2.8.2
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
102973440/102967424 [==============================] - 1s 0us/step
102981632/102967424 [==============================] - 1s 0us/step
Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json
40960/35363 [==================================] - 0s 0us/step
49152/35363 [=========================================] - 0s 0us/step
[('n03595614', 'jersey', 0.99856997), ('n03710637', 'maillot', 0.00064364297), ('n04370456', 'sweatshirt', 0.00043315586)]