대충 버전이 맞았는지 돌아는 간다.
Epoch 1/25 52/755 [=>............................] - ETA: 1:04:39 - loss: 0.3203 - accuracy: 0.8534 |
주요 설치 패키지 버전은 아래와 같고
| keras 2.14.0 mobilenet-v3 0.1.2 numpy 1.24.4 tensorflow 2.14.0 |
수정된 소스는 다음과 같다.
그런데 voc 디렉토리를 통채로 넣었는데 어찌 돌아는 가는데.. 어떤 파일로 학습을 하는거냐.. -_-
| from keras.applications import MobileNet from keras.models import Sequential,Model from keras.layers import Dense,Dropout,Activation,Flatten,GlobalAveragePooling2D from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D from keras.layers.normalization import BatchNormalization from tensorflow.keras.preprocessing.image import ImageDataGenerator # MobileNet is designed to work with images of dim 224,224 img_rows,img_cols = 224,224 MobileNet = MobileNet(weights='imagenet',include_top=False,input_shape=(img_rows,img_cols,3)) # Here we freeze the last 4 layers # Layers are set to trainable as True by default for layer in MobileNet.layers: layer.trainable = True # Let's print our layers for (i,layer) in enumerate(MobileNet.layers): print(str(i),layer.__class__.__name__,layer.trainable) def addTopModelMobileNet(bottom_model, num_classes): """creates the top or head of the model that will be placed ontop of the bottom layers""" top_model = bottom_model.output top_model = GlobalAveragePooling2D()(top_model) top_model = Dense(1024,activation='relu')(top_model) top_model = Dense(1024,activation='relu')(top_model) top_model = Dense(512,activation='relu')(top_model) top_model = Dense(num_classes,activation='softmax')(top_model) return top_model num_classes = 5 # ['Angry','Happy','Neutral','Sad','Surprise'] FC_Head = addTopModelMobileNet(MobileNet, num_classes) model = Model(inputs = MobileNet.input, outputs = FC_Head) print(model.summary()) train_data_dir = 'VOC2012_train_val/VOC2012_train_val' validation_data_dir = 'VOC2012_test/VOC2012_test' train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=30, width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, fill_mode='nearest' ) validation_datagen = ImageDataGenerator(rescale=1./255) batch_size = 32 train_generator = train_datagen.flow_from_directory( train_data_dir, target_size = (img_rows,img_cols), batch_size = batch_size, class_mode = 'categorical' ) validation_generator = validation_datagen.flow_from_directory( validation_data_dir, target_size=(img_rows,img_cols), batch_size=batch_size, class_mode='categorical') from keras.optimizers import RMSprop,Adam from keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau checkpoint = ModelCheckpoint( 'emotion_face_mobilNet.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) earlystop = EarlyStopping( monitor='val_loss', min_delta=0, patience=10, verbose=1,restore_best_weights=True) learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=5, verbose=1, factor=0.2, min_lr=0.0001) callbacks = [earlystop,checkpoint,learning_rate_reduction] model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=['accuracy'] ) nb_train_samples = 24176 nb_validation_samples = 3006 epochs = 25 history = model.fit( train_generator, steps_per_epoch=nb_train_samples//batch_size, epochs=epochs, callbacks=callbacks, validation_data=validation_generator, validation_steps=nb_validation_samples//batch_size) |
| Epoch 1/25 718/755 [===========================>..] - ETA: 3:28 - loss: 0.1569 - accuracy: 0.9301WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 18875 batches). You may need to use the repeat() function when building your dataset. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error: Detected at node categorical_crossentropy/softmax_cross_entropy_with_logits defined at (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1832, in fit File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2272, in evaluate File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 4079, in run_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2042, in test_function File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2025, in step_function File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2013, in run_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1895, in test_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1185, in compute_loss File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/compile_utils.py", line 277, in __call__ File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 143, in __call__ File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 270, in call File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 2221, in categorical_crossentropy File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/backend.py", line 5581, in categorical_crossentropy logits and labels must be broadcastable: logits_size=[32,5] labels_size=[32,3] [[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_test_function_15346] >>> |
전체 pip 패키지들 버전 정보는 아래와 같다.
| $ pip list Package Version ---------------------------- ---------------- absl-py 2.3.1 appdirs 1.4.4 apturl 0.5.2 astunparse 1.6.3 attrs 21.2.0 bcrypt 3.2.0 beautifulsoup4 4.10.0 beniget 0.4.1 blinker 1.4 Brlapi 0.8.3 Brotli 1.0.9 cachetools 5.5.2 certifi 2020.6.20 chardet 4.0.0 click 8.0.3 colorama 0.4.4 command-not-found 0.3 cryptography 3.4.8 cupshelpers 1.0 cycler 0.11.0 dbus-python 1.2.18 decorator 4.4.2 defer 1.0.6 distro 1.7.0 distro-info 1.1+ubuntu0.2 duplicity 0.8.21 fasteners 0.14.1 flatbuffers 25.2.10 fonttools 4.29.1 fs 2.4.12 future 0.18.2 gast 0.6.0 google-auth 2.40.3 google-auth-oauthlib 1.0.0 google-pasta 0.2.0 grpcio 1.74.0 h5py 3.14.0 html5lib 1.1 httplib2 0.20.2 idna 3.3 importlib-metadata 4.6.4 jeepney 0.7.1 keras 2.14.0 keyring 23.5.0 kiwisolver 1.3.2 language-selector 0.1 launchpadlib 1.10.16 lazr.restfulclient 0.14.4 lazr.uri 1.0.6 libclang 18.1.1 lockfile 0.12.2 louis 3.20.0 lxml 4.8.0 lz4 3.1.3+dfsg macaroonbakery 1.3.1 Mako 1.1.3 Markdown 3.9 markdown-it-py 4.0.0 MarkupSafe 3.0.2 matplotlib 3.5.1 mdurl 0.1.2 meld 3.20.4 ml-dtypes 0.2.0 mobilenet-v3 0.1.2 monotonic 1.6 more-itertools 8.10.0 mpmath 0.0.0 namex 0.1.0 netifaces 0.11.0 numpy 1.24.4 oauthlib 3.2.0 olefile 0.46 opt_einsum 3.4.0 optree 0.17.0 packaging 21.3 paramiko 2.9.3 pexpect 4.8.0 Pillow 9.0.1 pip 22.0.2 Pivy 0.6.5 ply 3.11 protobuf 4.25.8 ptyprocess 0.7.0 pyasn1 0.6.1 pyasn1_modules 0.4.2 pycairo 1.20.1 pycups 2.0.1 Pygments 2.19.2 PyGObject 3.42.1 PyJWT 2.3.0 pymacaroons 0.13.0 PyNaCl 1.5.0 pyparsing 2.4.7 pyRFC3339 1.1 python-apt 2.4.0+ubuntu4 python-dateutil 2.8.1 python-debian 0.1.43+ubuntu1.1 pythran 0.10.0 pytz 2022.1 pyxdg 0.27 PyYAML 5.4.1 reportlab 3.6.8 requests 2.25.1 requests-oauthlib 2.0.0 rich 14.1.0 rsa 4.9.1 scipy 1.15.3 scour 0.38.2 SecretStorage 3.3.1 setuptools 59.6.0 six 1.16.0 soupsieve 2.3.1 ssh-import-id 5.11 sympy 1.9 systemd-python 234 tensorboard 2.14.1 tensorboard-data-server 0.7.2 tensorflow 2.14.0 tensorflow-estimator 2.14.0 tensorflow-io-gcs-filesystem 0.37.1 termcolor 3.1.0 typing_extensions 4.15.0 ubuntu-drivers-common 0.0.0 ubuntu-pro-client 8001 ufoLib2 0.13.1 ufw 0.36.1 unattended-upgrades 0.1 unicodedata2 14.0.0 urllib3 1.26.5 usb-creator 0.3.7 wadllib 1.3.6 webencodings 0.5.1 Werkzeug 3.1.3 wheel 0.37.1 wrapt 1.14.2 xdg 5 xkit 0.0.0 zipp 1.0.0 |
-------- 아래는 참고 안하는게 속 편할지도...?
2020년 3월의 문서를 keras와 tensorflow로 2025년에 다시 시도해봄
일단은 아래처럼 설치하니 어찌 되는 느낌
| $ pip install mobilenet-v3 $ pip install tensorflow $ pip install numpy==1.26.4 |
상세로그
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termcolor-3.1.0-py3-none-any.whl (7.7 kB) Collecting typing_extensions>=3.6.6 Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 44.6/44.6 KB 8.2 MB/s eta 0:00:00 Collecting tensorboard~=2.20.0 Downloading tensorboard-2.20.0-py3-none-any.whl (5.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.5/5.5 MB 10.9 MB/s eta 0:00:00 Collecting astunparse>=1.6.0 Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB) Collecting opt_einsum>=2.3.2 Downloading opt_einsum-3.4.0-py3-none-any.whl (71 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 71.9/71.9 KB 8.4 MB/s eta 0:00:00 Collecting h5py>=3.11.0 Downloading h5py-3.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.6/4.6 MB 9.4 MB/s eta 0:00:00 Collecting flatbuffers>=24.3.25 Downloading flatbuffers-25.2.10-py2.py3-none-any.whl (30 kB) Collecting keras>=3.10.0 Downloading keras-3.11.3-py3-none-any.whl (1.4 MB) 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Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The scripts f2py and numpy-config are installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script markdown_py is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script markdown-it is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script tensorboard is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The scripts import_pb_to_tensorboard, saved_model_cli, tensorboard, tf_upgrade_v2, tflite_convert and toco are installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. Successfully installed MarkupSafe-3.0.2 absl-py-2.3.1 astunparse-1.6.3 flatbuffers-25.2.10 gast-0.6.0 google_pasta-0.2.0 grpcio-1.74.0 h5py-3.14.0 keras-3.11.3 libclang-18.1.1 markdown-3.9 markdown-it-py-4.0.0 mdurl-0.1.2 ml_dtypes-0.5.3 namex-0.1.0 numpy-2.2.6 opt_einsum-3.4.0 optree-0.17.0 protobuf-6.32.1 pygments-2.19.2 rich-14.1.0 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorflow-2.20.0 termcolor-3.1.0 typing_extensions-4.15.0 werkzeug-3.1.3 wrapt-1.17.3 minimonk@minimonk-HP-EliteBook-2760p:~$ pip install keras Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: keras in ./.local/lib/python3.10/site-packages (3.11.3) Requirement already satisfied: absl-py in ./.local/lib/python3.10/site-packages (from keras) (2.3.1) Requirement already satisfied: numpy in ./.local/lib/python3.10/site-packages (from keras) (2.2.6) Requirement already satisfied: rich in ./.local/lib/python3.10/site-packages (from keras) (14.1.0) Requirement already satisfied: ml-dtypes in ./.local/lib/python3.10/site-packages (from keras) (0.5.3) Requirement already satisfied: namex in ./.local/lib/python3.10/site-packages (from keras) (0.1.0) Requirement already satisfied: optree in ./.local/lib/python3.10/site-packages (from keras) (0.17.0) Requirement already satisfied: h5py in ./.local/lib/python3.10/site-packages (from keras) (3.14.0) Requirement already satisfied: packaging in /usr/lib/python3/dist-packages (from keras) (21.3) Requirement already satisfied: typing-extensions>=4.6.0 in ./.local/lib/python3.10/site-packages (from optree->keras) (4.15.0) Requirement already satisfied: markdown-it-py>=2.2.0 in ./.local/lib/python3.10/site-packages (from rich->keras) (4.0.0) Requirement already satisfied: pygments<3.0.0,>=2.13.0 in ./.local/lib/python3.10/site-packages (from rich->keras) (2.19.2) Requirement already satisfied: mdurl~=0.1 in ./.local/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2) $ pip install numpy==1.26.4 |
numpy 1.26.4를 깔게 된 에러메시지
| $ python3 Python 3.10.12 (main, Aug 15 2025, 14:32:43) [GCC 11.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf 2025-09-15 15:28:06.544207: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used. /usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 2.2.6 warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.6 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/__init__.py", line 49, in <module> from tensorflow._api.v2 import __internal__ File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/_api/v2/__internal__/__init__.py", line 13, in <module> from tensorflow._api.v2.__internal__ import feature_column File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/_api/v2/__internal__/feature_column/__init__.py", line 8, in <module> from tensorflow.python.feature_column.feature_column_v2 import DenseColumn # line: 1777 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/feature_column/feature_column_v2.py", line 38, in <module> from tensorflow.python.feature_column import feature_column as fc_old File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/feature_column/feature_column.py", line 41, in <module> from tensorflow.python.layers import base File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/layers/base.py", line 16, in <module> from tensorflow.python.keras.legacy_tf_layers import base File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/__init__.py", line 25, in <module> from tensorflow.python.keras import models File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/models.py", line 25, in <module> from tensorflow.python.keras.engine import training_v1 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/engine/training_v1.py", line 46, in <module> from tensorflow.python.keras.engine import training_arrays_v1 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/engine/training_arrays_v1.py", line 37, in <module> from scipy.sparse import issparse # pylint: disable=g-import-not-at-top File "/usr/lib/python3/dist-packages/scipy/sparse/__init__.py", line 267, in <module> from ._csr import * File "/usr/lib/python3/dist-packages/scipy/sparse/_csr.py", line 10, in <module> from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks, AttributeError: _ARRAY_API not found |
[링크 : https://mhui123.tistory.com/143]
그런데 mobilenet이 ssd가 없으면 classification만 되는 놈이었나?
| from keras.applications import MobileNet from keras.layers import Dense,Dropout,Activation, Flatten, GlobalAveragePooling2D from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D from tensorflow.keras.preprocessing.image import ImageDataGenerator from keras.optimizers import RMSprop, Adam from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau img_rows,img_cols = 224,224 MobileNet = MobileNet(weights='imagenet', include_top=False, input_shape=(img_rows, img_cols, 3)) 2025-09-15 16:00:19.852870: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303) Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5 17225924/17225924 ━━━━━━━━━━━━━━━━━━━━ 1s 0us/step >>> for layer in MobileNet.layers: ... layer.trainable = True ... >>> for (i, layer) in enumerate(MobileNet.layers): ... print(str(i), layer.__class__.__name__, layer.trainable) ... 0 InputLayer True 1 Conv2D True 2 BatchNormalization True 3 ReLU True 4 DepthwiseConv2D True 5 BatchNormalization True 6 ReLU True 7 Conv2D True 8 BatchNormalization True 9 ReLU True 10 ZeroPadding2D True 11 DepthwiseConv2D True 12 BatchNormalization True 13 ReLU True 14 Conv2D True 15 BatchNormalization True 16 ReLU True 17 DepthwiseConv2D True 18 BatchNormalization True 19 ReLU True 20 Conv2D True 21 BatchNormalization True 22 ReLU True 23 ZeroPadding2D True 24 DepthwiseConv2D True 25 BatchNormalization True 26 ReLU True 27 Conv2D True 28 BatchNormalization True 29 ReLU True 30 DepthwiseConv2D True 31 BatchNormalization True 32 ReLU True 33 Conv2D True 34 BatchNormalization True 35 ReLU True 36 ZeroPadding2D True 37 DepthwiseConv2D True 38 BatchNormalization True 39 ReLU True 40 Conv2D True 41 BatchNormalization True 42 ReLU True 43 DepthwiseConv2D True 44 BatchNormalization True 45 ReLU True 46 Conv2D True 47 BatchNormalization True 48 ReLU True 49 DepthwiseConv2D True 50 BatchNormalization True 51 ReLU True 52 Conv2D True 53 BatchNormalization True 54 ReLU True 55 DepthwiseConv2D True 56 BatchNormalization True 57 ReLU True 58 Conv2D True 59 BatchNormalization True 60 ReLU True 61 DepthwiseConv2D True 62 BatchNormalization True 63 ReLU True 64 Conv2D True 65 BatchNormalization True 66 ReLU True 67 DepthwiseConv2D True 68 BatchNormalization True 69 ReLU True 70 Conv2D True 71 BatchNormalization True 72 ReLU True 73 ZeroPadding2D True 74 DepthwiseConv2D True 75 BatchNormalization True 76 ReLU True 77 Conv2D True 78 BatchNormalization True 79 ReLU True 80 DepthwiseConv2D True 81 BatchNormalization True 82 ReLU True 83 Conv2D True 84 BatchNormalization True 85 ReLU True >>> MobileNet.output <KerasTensor shape=(None, 7, 7, 1024), dtype=float32, sparse=False, ragged=False, name=keras_tensor_85> >>> MobileNet.input <KerasTensor shape=(None, 224, 224, 3), dtype=float32, sparse=False, ragged=False, name=keras_tensor> >>> MobileNet.summary() Model: "mobilenet_1.00_224" ┏--------------------------------------┳-----------------------------┳-----------------┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡--------------------------------------╇-----------------------------╇-----------------┩ │ input_layer (InputLayer) │ (None, 224, 224, 3) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1 (Conv2D) │ (None, 112, 112, 32) │ 864 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1_bn (BatchNormalization) │ (None, 112, 112, 32) │ 128 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1_relu (ReLU) │ (None, 112, 112, 32) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1 (DepthwiseConv2D) │ (None, 112, 112, 32) │ 288 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1_bn (BatchNormalization) │ (None, 112, 112, 32) │ 128 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1_relu (ReLU) │ (None, 112, 112, 32) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1 (Conv2D) │ (None, 112, 112, 64) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1_bn (BatchNormalization) │ (None, 112, 112, 64) │ 256 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1_relu (ReLU) │ (None, 112, 112, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_2 (ZeroPadding2D) │ (None, 113, 113, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2 (DepthwiseConv2D) │ (None, 56, 56, 64) │ 576 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2_bn (BatchNormalization) │ (None, 56, 56, 64) │ 256 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2_relu (ReLU) │ (None, 56, 56, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2 (Conv2D) │ (None, 56, 56, 128) │ 8,192 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3 (DepthwiseConv2D) │ (None, 56, 56, 128) │ 1,152 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3 (Conv2D) │ (None, 56, 56, 128) │ 16,384 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_4 (ZeroPadding2D) │ (None, 57, 57, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4 (DepthwiseConv2D) │ (None, 28, 28, 128) │ 1,152 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4_bn (BatchNormalization) │ (None, 28, 28, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4_relu (ReLU) │ (None, 28, 28, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4 (Conv2D) │ (None, 28, 28, 256) │ 32,768 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5 (DepthwiseConv2D) │ (None, 28, 28, 256) │ 2,304 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5 (Conv2D) │ (None, 28, 28, 256) │ 65,536 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_6 (ZeroPadding2D) │ (None, 29, 29, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6 (DepthwiseConv2D) │ (None, 14, 14, 256) │ 2,304 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6_bn (BatchNormalization) │ (None, 14, 14, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6_relu (ReLU) │ (None, 14, 14, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6 (Conv2D) │ (None, 14, 14, 512) │ 131,072 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_12 (ZeroPadding2D) │ (None, 15, 15, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12 (DepthwiseConv2D) │ (None, 7, 7, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12_bn (BatchNormalization) │ (None, 7, 7, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12_relu (ReLU) │ (None, 7, 7, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12 (Conv2D) │ (None, 7, 7, 1024) │ 524,288 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13 (DepthwiseConv2D) │ (None, 7, 7, 1024) │ 9,216 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13 (Conv2D) │ (None, 7, 7, 1024) │ 1,048,576 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ └--------------------------------------┴-----------------------------┴-----------------┘ Total params: 3,228,864 (12.32 MB) Trainable params: 3,206,976 (12.23 MB) Non-trainable params: 21,888 (85.50 KB) |
[링크 : https://kau-deeperent.tistory.com/m/59]
| # from keras.preprocessing.image import ImageDataGenerator # 에러났음 from tensorflow.keras.preprocessing.image import ImageDataGenerator |
[링크 : https://sugyeong0425.tistory.com/151]
voc2012 데이터셋설명
[링크 : https://bo-10000.tistory.com/38]
[링크 : https://velog.io/@kyungmin1029/CV-OpenCV]
2024.8 월 이니 한번 시도해볼 만할 듯?
[링크 : https://velog.io/@choonsik_mom/MobileNet-SSD-object-detector-커스텀-데이터-학습하기-m3j5d0xh]
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