Bu ortak çalışmada, TensorFlow Hub'dan birden çok görüntü sınıflandırma modelini deneyecek ve kullanım durumunuz için hangisinin en iyi olduğuna karar vereceksiniz.
TF Hub bir teşvik Çünkü tutarlı giriş kongre görüntülerde faaliyet modeller için, ihtiyaçlarınıza uyan en iyi olduğunu bulmak için farklı mimarileri denemeler kolaydır.
 TensorFlow.org'da görüntüleyin |  Google Colab'da çalıştırın |  GitHub'da görüntüle |  Not defterini indir |  TF Hub modellerine bakın |
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
Görüntü yüklemek için yardımcı işlevler (gizli)
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()
Bir Görüntü Sınıflandırma Modeli seçin. Bundan sonra bazı dahili değişkenler ayarlanır ve etiketler dosyası indirilir ve kullanıma hazırlanır.
Modeller arasında farklı giriş boyutu, model boyutu, doğruluk ve çıkarım süresi gibi bazı teknik farklılıklar vardır. Burada, kullanım durumunuza en uygun olanı bulana kadar kullandığınız modeli değiştirebilirsiniz.
Modelin tutamacı (url) size kolaylık olması için yazdırılmıştır. Her model hakkında daha fazla belge burada mevcuttur.
Bir Görüntü Sınıflandırma modeli seçin
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
Aşağıdaki görsellerden birini seçebilir veya kendi görselinizi kullanabilirsiniz. Modeller için giriş boyutunun değiştiğini ve bazılarının dinamik bir giriş boyutu kullandığını (ölçeklenmemiş görüntü üzerinde çıkarsamaya olanak tanıyan) unutmayın. Olduğu göz önüne alındığında, yöntem load_image zaten beklenen bir biçime görüntü rescale olacaktır.
Bir Giriş Resmi Seçin
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')
Model seçildiğine göre, onu TensorFlow Hub ile yüklemek artık çok kolay.
Bu aynı zamanda rasgele girdili modeli "ısınma" çalışması olarak da adlandırır. Sonraki aramalar genellikle çok daha hızlıdır ve bunu aşağıdaki gecikmeyle karşılaştırabilirsiniz.
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
Her şey çıkarım için hazır. Burada, seçilen görüntü için modelden en iyi 5 sonucu görebilirsiniz.
# 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
Daha fazla bilgi edin
Daha fazla bilgi edinmek ve bu modellerle Transferi Öğrenme nasıl denemek istiyorsanız bu öğretici deneyebilirsiniz: Görüntü sınıflandırma için Aktarım Öğrenme
Daha görüntü modellerde kontrol etmek istiyorsanız size bunları kontrol edebilirsiniz tfhub.dev