keras-inception-resnet-v2

The Inception-ResNet v2 model using Keras (with weight files)

Tested with tensorflow-gpu==1.8.0 and Keras==2.2.0 under Python 2.7 and 3.6.

Layers and namings follow the TF-slim implementation:https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py

News

This implementation has been merged into the keras.applications module!

Install the latest version Keras on GitHub and import it with:

from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input

Usage

Basically the same with the keras.applications.InceptionV3 model.

from inception_resnet_v2 import InceptionResNetV2# ImageNet classificationmodel = InceptionResNetV2()
model.predict(...)# Finetuning on another 100-class datasetbase_model = InceptionResNetV2(include_top=False, pooling='avg')
outputs = Dense(100, activation='softmax')(base_model.output)
model = Model(base_model.inputs, outputs)
model.compile(...)
model.fit(...)

Extract layer weights from TF checkpoint

python extract_weights.py

By default, the TF checkpoint file will be downloaded to ./models folder, and the layer weights (.npy files) will be saved to ./weights folder.

Load NumPy weight files and save to a Keras HDF5 weights file

python load_weights.py

The following weight files:

• models/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5

• models/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5

will be generated.

Test model prediction on single image

To test whether this implementation gives the same prediction as TF-slim implementation:

PYTHONPATH=../tensorflow-models/research/slim python test_inception_resnet_v2.py

PYTHONPATH should point to the research/slim folder under the https://github.com/tensorflow/models repo.

The image file elephant.jpg (and basically the entire idea of converting weights from TF-slim to Keras) comes from:https://github.com/kentsommer/keras-inception-resnetV2

Evaluate the model on ImageNet 2012 dataset

First, follow theinstructionsfrom TF-slim to download and process the data.

Suppose that the dataset is saved to the imagenet_2012 directory, to evaluate:

PYTHONPATH=../tensorflow-models/research/slim python evaluate_imagenet.py ../tensorflow-models/research/slim/datasets/imagenet_2012 --verbose

The script should print out top-1 and top-5 accuracy on validation set:

Current status

• Extract weights from TF-slim

• Convert weights to HDF5 files

• Test weight loading and image prediction (elephant.jpg)

• Release weight files

• Evaluate accuracy on ImageNet benchmark dataset