Path: blob/master/site/ja/hub/tutorials/image_classification.ipynb
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Kernel: Python 3
Copyright 2021 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
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# Copyright 2021 The TensorFlow Hub Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================
TensorFlow Hub での画像分類
この colab では、TensorFlow Hub からの複数の画像分類モデルを試して、ユースケースに最適なものを決定します。
TF Hub は画像で操作するモデルの一貫性のある入力変換 を推奨するため、ニーズに最適なものを見つけるためにさまざまなアーキテクチャで簡単に実験できます。
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TF Hub モデルを参照In [ ]:
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
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#@title Helper functions for loading image (hidden) 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()
画像分類モデルを選択します。その後、内部変数がいくつか設定され、ラベルファイルがダウンロードされ、使用するために準備されます。
異なる入力サイズ、モデルサイズ、精度、推論時間など、モデル間には技術的な違いがいくつかあります。ここでは、ユースケースに最適なものが見つかるまで、使用しているモデルを変更できます。
参考までに、モデルのハンドル (url) を表示しました。各モデルについての詳細な資料はハンドルで入手できます。
注: すべてのこれらのモデルは ImageNet データセットでトレーニングされました
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#@title Select an Image Classification model 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]
以下の画像のいずれかを選択するか、独自の画像を使用できます。モデルの入力サイズはさまざまで、ダイナミックな入力サイズ（スケールされていない画像の推論を可能にします）を使用するものもあることを覚えておいてください。そのため、メソッド load_image は画像サイズをすでに必要なフォーマットに調整しています。
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#@title Select an Input Image 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')
モデルが選択されたため、TensorFlow Hub への読み込みは簡単です。
同様に、これはランダムな入力を持つモデルを「ウォームアップ」実行として呼び出します。後続の呼び出しは多くの場合より高速になります。これを以下のレイテンシと比較することができます。
注: ダイナミックなサイズを使用するモデルは、画像サイズごとにフレッシュな「ウォームアップ」実行を必要とする場合があります。
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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()
推論のための準備ができました。ここに選択した画像モデルからの結果上位 5 件があります。
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# 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, '')
詳細情報
これらのモデルを使用した転移学習の詳細情報と使用方法については、こちらのチュートリアル、画像分類のための転移学習をお試しください。
画像モデルの詳細については、tfhub.dev でご確認ください。