تصنيف الصور باستخدام TensorFlow Hub

في هذا colab ، ستجرب نماذج تصنيف صور متعددة من TensorFlow Hub وتقرر أيها أفضل لحالة الاستخدام الخاصة بك.

لأن TF محور يشجع على اتفاقية مساهمة متسقة لنماذج التي تعمل على الصور، فإنه من السهل لهذه التجربة مع أبنية مختلفة للعثور على واحد الذي يناسب احتياجاتك.

عرض على TensorFlow.org

تشغيل في Google Colab

عرض على جيثب

تحميل دفتر

شاهد موديلات TF Hub

import tensorflow as tf
import tensorflow_hub as hub

import requests
from PIL import Image
from io import BytesIO

import matplotlib.pyplot as plt
import numpy as np

وظائف المساعد لتحميل الصورة (مخفية)

original_image_cache = {}

def preprocess_image(image):
  image = np.array(image)
  # reshape into shape [batch_size, height, width, num_channels]
  img_reshaped = tf.reshape(image, [1, image.shape[0], image.shape[1], image.shape[2]])
  # Use `convert_image_dtype` to convert to floats in the [0,1] range.
  image = tf.image.convert_image_dtype(img_reshaped, tf.float32)
  return image

def load_image_from_url(img_url):
  """Returns an image with shape [1, height, width, num_channels]."""
  user_agent = {'User-agent': 'Colab Sample (https://tensorflow.org)'}
  response = requests.get(img_url, headers=user_agent)
  image = Image.open(BytesIO(response.content))
  image = preprocess_image(image)
  return image

def load_image(image_url, image_size=256, dynamic_size=False, max_dynamic_size=512):
  """Loads and preprocesses images."""
  # Cache image file locally.
  if image_url in original_image_cache:
    img = original_image_cache[image_url]
  elif image_url.startswith('https://'):
    img = load_image_from_url(image_url)
  else:
    fd = tf.io.gfile.GFile(image_url, 'rb')
    img = preprocess_image(Image.open(fd))
  original_image_cache[image_url] = img
  # Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1].
  img_raw = img
  if tf.reduce_max(img) > 1.0:
    img = img / 255.
  if len(img.shape) == 3:
    img = tf.stack([img, img, img], axis=-1)
  if not dynamic_size:
    img = tf.image.resize_with_pad(img, image_size, image_size)
  elif img.shape[1] > max_dynamic_size or img.shape[2] > max_dynamic_size:
    img = tf.image.resize_with_pad(img, max_dynamic_size, max_dynamic_size)
  return img, img_raw

def show_image(image, title=''):
  image_size = image.shape[1]
  w = (image_size * 6) // 320
  plt.figure(figsize=(w, w))
  plt.imshow(image[0], aspect='equal')
  plt.axis('off')
  plt.title(title)
  plt.show()

حدد نموذج تصنيف الصورة. بعد ذلك ، يتم تعيين بعض المتغيرات الداخلية ويتم تنزيل ملف الملصقات وإعداده للاستخدام.

توجد بعض الاختلافات الفنية بين النماذج ، مثل حجم الإدخال المختلف ، وحجم النموذج ، والدقة ، ووقت الاستدلال. هنا يمكنك تغيير النموذج الذي تستخدمه حتى تجد النموذج الأكثر ملاءمة لحالة الاستخدام الخاصة بك.

تتم طباعة مقبض (عنوان url) الخاص بالنموذج من أجل راحتك. يتوفر المزيد من الوثائق حول كل نموذج هناك.

حدد نموذج تصنيف الصورة

image_size = 224
dynamic_size = False

model_name = "efficientnetv2-s" # @param ['efficientnetv2-s', 'efficientnetv2-m', 'efficientnetv2-l', 'efficientnetv2-s-21k', 'efficientnetv2-m-21k', 'efficientnetv2-l-21k', 'efficientnetv2-xl-21k', 'efficientnetv2-b0-21k', 'efficientnetv2-b1-21k', 'efficientnetv2-b2-21k', 'efficientnetv2-b3-21k', 'efficientnetv2-s-21k-ft1k', 'efficientnetv2-m-21k-ft1k', 'efficientnetv2-l-21k-ft1k', 'efficientnetv2-xl-21k-ft1k', 'efficientnetv2-b0-21k-ft1k', 'efficientnetv2-b1-21k-ft1k', 'efficientnetv2-b2-21k-ft1k', 'efficientnetv2-b3-21k-ft1k', 'efficientnetv2-b0', 'efficientnetv2-b1', 'efficientnetv2-b2', 'efficientnetv2-b3', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'bit_s-r50x1', 'inception_v3', 'inception_resnet_v2', 'resnet_v1_50', 'resnet_v1_101', 'resnet_v1_152', 'resnet_v2_50', 'resnet_v2_101', 'resnet_v2_152', 'nasnet_large', 'nasnet_mobile', 'pnasnet_large', 'mobilenet_v2_100_224', 'mobilenet_v2_130_224', 'mobilenet_v2_140_224', 'mobilenet_v3_small_100_224', 'mobilenet_v3_small_075_224', 'mobilenet_v3_large_100_224', 'mobilenet_v3_large_075_224']

model_handle_map = {
  "efficientnetv2-s": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_s/classification/2",
  "efficientnetv2-m": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_m/classification/2",
  "efficientnetv2-l": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_l/classification/2",
  "efficientnetv2-s-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_s/classification/2",
  "efficientnetv2-m-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_m/classification/2",
  "efficientnetv2-l-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_l/classification/2",
  "efficientnetv2-xl-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_xl/classification/2",
  "efficientnetv2-b0-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b0/classification/2",
  "efficientnetv2-b1-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b1/classification/2",
  "efficientnetv2-b2-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b2/classification/2",
  "efficientnetv2-b3-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b3/classification/2",
  "efficientnetv2-s-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_s/classification/2",
  "efficientnetv2-m-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_m/classification/2",
  "efficientnetv2-l-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_l/classification/2",
  "efficientnetv2-xl-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_xl/classification/2",
  "efficientnetv2-b0-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b0/classification/2",
  "efficientnetv2-b1-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b1/classification/2",
  "efficientnetv2-b2-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b2/classification/2",
  "efficientnetv2-b3-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b3/classification/2",
  "efficientnetv2-b0": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/classification/2",
  "efficientnetv2-b1": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b1/classification/2",
  "efficientnetv2-b2": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b2/classification/2",
  "efficientnetv2-b3": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b3/classification/2",
  "efficientnet_b0": "https://tfhub.dev/tensorflow/efficientnet/b0/classification/1",
  "efficientnet_b1": "https://tfhub.dev/tensorflow/efficientnet/b1/classification/1",
  "efficientnet_b2": "https://tfhub.dev/tensorflow/efficientnet/b2/classification/1",
  "efficientnet_b3": "https://tfhub.dev/tensorflow/efficientnet/b3/classification/1",
  "efficientnet_b4": "https://tfhub.dev/tensorflow/efficientnet/b4/classification/1",
  "efficientnet_b5": "https://tfhub.dev/tensorflow/efficientnet/b5/classification/1",
  "efficientnet_b6": "https://tfhub.dev/tensorflow/efficientnet/b6/classification/1",
  "efficientnet_b7": "https://tfhub.dev/tensorflow/efficientnet/b7/classification/1",
  "bit_s-r50x1": "https://tfhub.dev/google/bit/s-r50x1/ilsvrc2012_classification/1",
  "inception_v3": "https://tfhub.dev/google/imagenet/inception_v3/classification/4",
  "inception_resnet_v2": "https://tfhub.dev/google/imagenet/inception_resnet_v2/classification/4",
  "resnet_v1_50": "https://tfhub.dev/google/imagenet/resnet_v1_50/classification/4",
  "resnet_v1_101": "https://tfhub.dev/google/imagenet/resnet_v1_101/classification/4",
  "resnet_v1_152": "https://tfhub.dev/google/imagenet/resnet_v1_152/classification/4",
  "resnet_v2_50": "https://tfhub.dev/google/imagenet/resnet_v2_50/classification/4",
  "resnet_v2_101": "https://tfhub.dev/google/imagenet/resnet_v2_101/classification/4",
  "resnet_v2_152": "https://tfhub.dev/google/imagenet/resnet_v2_152/classification/4",
  "nasnet_large": "https://tfhub.dev/google/imagenet/nasnet_large/classification/4",
  "nasnet_mobile": "https://tfhub.dev/google/imagenet/nasnet_mobile/classification/4",
  "pnasnet_large": "https://tfhub.dev/google/imagenet/pnasnet_large/classification/4",
  "mobilenet_v2_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4",
  "mobilenet_v2_130_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4",
  "mobilenet_v2_140_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/4",
  "mobilenet_v3_small_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/classification/5",
  "mobilenet_v3_small_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_075_224/classification/5",
  "mobilenet_v3_large_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/classification/5",
  "mobilenet_v3_large_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_075_224/classification/5",
}

model_image_size_map = {
  "efficientnetv2-s": 384,
  "efficientnetv2-m": 480,
  "efficientnetv2-l": 480,
  "efficientnetv2-b0": 224,
  "efficientnetv2-b1": 240,
  "efficientnetv2-b2": 260,
  "efficientnetv2-b3": 300,
  "efficientnetv2-s-21k": 384,
  "efficientnetv2-m-21k": 480,
  "efficientnetv2-l-21k": 480,
  "efficientnetv2-xl-21k": 512,
  "efficientnetv2-b0-21k": 224,
  "efficientnetv2-b1-21k": 240,
  "efficientnetv2-b2-21k": 260,
  "efficientnetv2-b3-21k": 300,
  "efficientnetv2-s-21k-ft1k": 384,
  "efficientnetv2-m-21k-ft1k": 480,
  "efficientnetv2-l-21k-ft1k": 480,
  "efficientnetv2-xl-21k-ft1k": 512,
  "efficientnetv2-b0-21k-ft1k": 224,
  "efficientnetv2-b1-21k-ft1k": 240,
  "efficientnetv2-b2-21k-ft1k": 260,
  "efficientnetv2-b3-21k-ft1k": 300, 
  "efficientnet_b0": 224,
  "efficientnet_b1": 240,
  "efficientnet_b2": 260,
  "efficientnet_b3": 300,
  "efficientnet_b4": 380,
  "efficientnet_b5": 456,
  "efficientnet_b6": 528,
  "efficientnet_b7": 600,
  "inception_v3": 299,
  "inception_resnet_v2": 299,
  "mobilenet_v2_100_224": 224,
  "mobilenet_v2_130_224": 224,
  "mobilenet_v2_140_224": 224,
  "nasnet_large": 331,
  "nasnet_mobile": 224,
  "pnasnet_large": 331,
  "resnet_v1_50": 224,
  "resnet_v1_101": 224,
  "resnet_v1_152": 224,
  "resnet_v2_50": 224,
  "resnet_v2_101": 224,
  "resnet_v2_152": 224,
  "mobilenet_v3_small_100_224": 224,
  "mobilenet_v3_small_075_224": 224,
  "mobilenet_v3_large_100_224": 224,
  "mobilenet_v3_large_075_224": 224,
}

model_handle = model_handle_map[model_name]

print(f"Selected model: {model_name} : {model_handle}")


max_dynamic_size = 512
if model_name in model_image_size_map:
  image_size = model_image_size_map[model_name]
  dynamic_size = False
  print(f"Images will be converted to {image_size}x{image_size}")
else:
  dynamic_size = True
  print(f"Images will be capped to a max size of {max_dynamic_size}x{max_dynamic_size}")

labels_file = "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt"

#download labels and creates a maps
downloaded_file = tf.keras.utils.get_file("labels.txt", origin=labels_file)

classes = []

with open(downloaded_file) as f:
  labels = f.readlines()
  classes = [l.strip() for l in labels]

Selected model: efficientnetv2-s : https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_s/classification/2
Images will be converted to 384x384
Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt
16384/10484 [==============================================] - 0s 0us/step
24576/10484 [======================================================================] - 0s 0us/step

يمكنك اختيار إحدى الصور أدناه ، أو استخدام صورتك الخاصة. فقط تذكر أن حجم الإدخال للنماذج يختلف وأن بعضها يستخدم حجم إدخال ديناميكي (يتيح الاستدلال على الصورة غير المقاسة). وبالنظر إلى أن طريقة load_image وإعادة مقياس بالفعل الصورة إلى شكل من المتوقع.

حدد صورة الإدخال

image_name = "turtle" # @param ['tiger', 'bus', 'car', 'cat', 'dog', 'apple', 'banana', 'turtle', 'flamingo', 'piano', 'honeycomb', 'teapot']

images_for_test_map = {
    "tiger": "https://upload.wikimedia.org/wikipedia/commons/b/b0/Bengal_tiger_%28Panthera_tigris_tigris%29_female_3_crop.jpg",
    #by Charles James Sharp, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons
    "bus": "https://upload.wikimedia.org/wikipedia/commons/6/63/LT_471_%28LTZ_1471%29_Arriva_London_New_Routemaster_%2819522859218%29.jpg",
    #by Martin49 from London, England, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
    "car": "https://upload.wikimedia.org/wikipedia/commons/4/49/2013-2016_Toyota_Corolla_%28ZRE172R%29_SX_sedan_%282018-09-17%29_01.jpg",
    #by EurovisionNim, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons
    "cat": "https://upload.wikimedia.org/wikipedia/commons/4/4d/Cat_November_2010-1a.jpg",
    #by Alvesgaspar, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "dog": "https://upload.wikimedia.org/wikipedia/commons/archive/a/a9/20090914031557%21Saluki_dog_breed.jpg",
    #by Craig Pemberton, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "apple": "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg",
    #by Abhijit Tembhekar from Mumbai, India, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
    "banana": "https://upload.wikimedia.org/wikipedia/commons/1/1c/Bananas_white_background.jpg",
    #by fir0002  flagstaffotos [at] gmail.com       Canon 20D + Tamron 28-75mm f/2.8, GFDL 1.2 <http://www.gnu.org/licenses/old-licenses/fdl-1.2.html>, via Wikimedia Commons
    "turtle": "https://upload.wikimedia.org/wikipedia/commons/8/80/Turtle_golfina_escobilla_oaxaca_mexico_claudio_giovenzana_2010.jpg",
    #by Claudio Giovenzana, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "flamingo": "https://upload.wikimedia.org/wikipedia/commons/b/b8/James_Flamingos_MC.jpg",
    #by Christian Mehlführer, User:Chmehl, CC BY 3.0 <https://creativecommons.org/licenses/by/3.0>, via Wikimedia Commons
    "piano": "https://upload.wikimedia.org/wikipedia/commons/d/da/Steinway_%26_Sons_upright_piano%2C_model_K-132%2C_manufactured_at_Steinway%27s_factory_in_Hamburg%2C_Germany.png",
    #by "Photo: © Copyright Steinway & Sons", CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "honeycomb": "https://upload.wikimedia.org/wikipedia/commons/f/f7/Honey_comb.jpg",
    #by Merdal, CC BY-SA 3.0 <http://creativecommons.org/licenses/by-sa/3.0/>, via Wikimedia Commons
    "teapot": "https://upload.wikimedia.org/wikipedia/commons/4/44/Black_tea_pot_cropped.jpg",
    #by Mendhak, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons
}

img_url = images_for_test_map[image_name]
image, original_image = load_image(img_url, image_size, dynamic_size, max_dynamic_size)
show_image(image, 'Scaled image')

بي إن جي

الآن بعد أن تم اختيار النموذج ، أصبح تحميله باستخدام TensorFlow Hub أمرًا بسيطًا.

يستدعي هذا أيضًا النموذج الذي يحتوي على إدخال عشوائي باعتباره عملية تشغيل "إحماء". غالبًا ما تكون المكالمات اللاحقة أسرع بكثير ، ويمكنك مقارنتها بوقت الاستجابة أدناه.

classifier = hub.load(model_handle)

input_shape = image.shape
warmup_input = tf.random.uniform(input_shape, 0, 1.0)
%time warmup_logits = classifier(warmup_input).numpy()

CPU times: user 2.88 s, sys: 470 ms, total: 3.35 s
Wall time: 3.41 s

كل شيء جاهز للاستدلال. هنا يمكنك رؤية أفضل 5 نتائج من النموذج للصورة المحددة.

# Run model on image
%time probabilities = tf.nn.softmax(classifier(image)).numpy()

top_5 = tf.argsort(probabilities, axis=-1, direction="DESCENDING")[0][:5].numpy()
np_classes = np.array(classes)

# Some models include an additional 'background' class in the predictions, so
# we must account for this when reading the class labels.
includes_background_class = probabilities.shape[1] == 1001

for i, item in enumerate(top_5):
  class_index = item if includes_background_class else item + 1
  line = f'({i+1}) {class_index:4} - {classes[class_index]}: {probabilities[0][top_5][i]}'
  print(line)

show_image(image, '')

CPU times: user 27.4 ms, sys: 9 µs, total: 27.4 ms
Wall time: 25.9 ms
(1)   35 - leatherback turtle: 0.7747752666473389
(2)   34 - loggerhead: 0.10644760727882385
(3)   37 - terrapin: 0.005874828901141882
(4)  148 - grey whale: 0.002594555728137493
(5)   36 - mud turtle: 0.0025599468499422073

بي إن جي

يتعلم أكثر

إذا كنت ترغب في معرفة المزيد ومحاولة كيفية القيام التعلم نقل مع هذه النماذج يمكنك محاولة هذا البرنامج التعليمي: التعلم نقل لتصنيف صورة

إذا كنت تريد أن تحقق على المزيد من النماذج الصورة التي يمكن التحقق منها على tfhub.dev