| import os | |
| import tensorflow as tf | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from tensorflow import keras | |
| import requests | |
| import PIL | |
| import io | |
| import matplotlib.pyplot as plt | |
| def download_image(url): | |
| resp = requests.get(url) | |
| resp.raise_for_status() | |
| return PIL.Image.open(io.BytesIO(resp.content)) | |
| from keras_cv_attention_models import convnext | |
| mm = convnext.ConvNeXtBase() | |
| downloaded_image = download_image( | |
| "https://www.popsci.com/uploads/2021/09/21/Tortoise-on-ground-surrounded-by-plants.jpg?auto=webp" | |
| ) | |
| downloaded_image_np = np.array(downloaded_image) | |
| img = downloaded_image_np | |
| imm = keras.applications.imagenet_utils.preprocess_input(img, mode='torch') | |
| image_input = tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0) | |
| pred = mm(image_input) | |
| pred_np = pred.numpy() |