Dalam colab ini, Anda akan mencoba beberapa model klasifikasi gambar dari TensorFlow Hub dan memutuskan mana yang terbaik untuk kasus penggunaan Anda.
Karena TF Hub mendorong konvensi masukan yang konsisten untuk model yang beroperasi pada gambar, mudah untuk bereksperimen dengan arsitektur yang berbeda untuk menemukan satu yang paling sesuai dengan kebutuhan Anda.
 Lihat di TensorFlow.org |  Jalankan di Google Colab |  Lihat di GitHub |  Unduh buku catatan |  Lihat model 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
Fungsi pembantu untuk memuat gambar (tersembunyi)
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()
Pilih Model Klasifikasi Gambar. Setelah itu, beberapa variabel internal ditetapkan dan file label diunduh dan disiapkan untuk digunakan.
Ada beberapa perbedaan teknis antara model, seperti ukuran input yang berbeda, ukuran model, akurasi, dan waktu inferensi. Di sini Anda dapat mengubah model yang Anda gunakan sampai Anda menemukan yang paling cocok untuk kasus penggunaan Anda.
Pegangan (url) model dicetak untuk kenyamanan Anda. Dokumentasi lebih lanjut tentang setiap model tersedia di sana.
Pilih model Klasifikasi Gambar
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
Anda dapat memilih salah satu gambar di bawah ini, atau menggunakan gambar Anda sendiri. Ingatlah bahwa ukuran input untuk model bervariasi dan beberapa di antaranya menggunakan ukuran input dinamis (mengaktifkan inferensi pada gambar yang tidak diskalakan). Mengingat bahwa, metode load_image sudah akan rescale gambar ke format yang diharapkan.
Pilih Gambar Masukan
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')
Sekarang setelah model dipilih, memuatnya dengan TensorFlow Hub menjadi mudah.
Ini juga menyebut model dengan input acak sebagai lari "pemanasan". Panggilan berikutnya seringkali jauh lebih cepat, dan Anda dapat membandingkannya dengan latensi di bawah ini.
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
Semuanya siap untuk inferensi. Di sini Anda dapat melihat 5 hasil teratas dari model untuk gambar yang dipilih.
# 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
Belajarlah lagi
Jika Anda ingin mempelajari lebih lanjut dan mencoba bagaimana melakukan Belajar transfer dengan model ini Anda dapat mencoba tutorial ini: Belajar transfer untuk klasifikasi Gambar
Jika Anda ingin memeriksa pada model gambar yang lebih Anda dapat memeriksa mereka di tfhub.dev