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May. 17, 2019
Feb. 19, 2018


Computes features for images using various pretrained Tensorflow models. For each model, this will output the final fully connected layer (e.g. fc7 for Alexnet, fc8 for VGG_19, etc.). These can be used within various applications such as classification, clustering, etc.


Download one of the checkpoint files below, for example Inception V1.

tar -xvf inception_v1_2016_08_28.tar.gz
python --data_dir=test_images/ --checkpoint_file=inception_v1.ckpt --model=inception_v1

The output will be in inception_v1_features.pkl, which contains a dictionary of the form {image_path:feature}.

Look at for an example of how to use the features that were computed. For example,

python features/inception_v1_features.pkl

Some of these aren't working, such as inception_v4 due to differences in the model checkpoint and the model defined in the nets/ directory. Still working on that.

Pre-trained Models

You can download all models here. Otherwise links for individual models can be found below.

These CNNs have been trained on the ILSVRC-2012-CLS image classification dataset.

In the table below, we list each model, the corresponding TensorFlow model file, the link to the model checkpoint, and the top 1 and top 5 accuracy (on the imagenet test set). Note that the VGG and ResNet V1 parameters have been converted from their original caffe formats (here and here), whereas the Inception and ResNet V2 parameters have been trained internally at Google. Also be aware that these accuracies were computed by evaluating using a single image crop. Some academic papers report higher accuracy by using multiple crops at multiple scales.

Model TF-Slim File Checkpoint Top-1 Accuracy Top-5 Accuracy
Inception V1 Code inception_v1_2016_08_28.tar.gz 69.8 89.6
Inception V2 Code inception_v2_2016_08_28.tar.gz 73.9 91.8
Inception V3 Code inception_v3_2016_08_28.tar.gz 78.0 93.9
Inception V4 Code inception_v4_2016_09_09.tar.gz 80.2 95.2
Inception-ResNet-v2 Code inception_resnet_v2_2016_08_30.tar.gz 80.4 95.3
ResNet V1 50 Code resnet_v1_50_2016_08_28.tar.gz 75.2 92.2
ResNet V1 101 Code resnet_v1_101_2016_08_28.tar.gz 76.4 92.9
ResNet V1 152 Code resnet_v1_152_2016_08_28.tar.gz 76.8 93.2
ResNet V2 50 Code resnet_v2_50_2017_04_14.tar.gz 75.6 92.8
ResNet V2 101 Code resnet_v2_101_2017_04_14.tar.gz 77.0 93.7
ResNet V2 152 Code resnet_v2_152_2017_04_14.tar.gz 77.8 94.1
VGG 16 Code vgg_16_2016_08_28.tar.gz 71.5 89.8
VGG 19 Code vgg_19_2016_08_28.tar.gz 71.1 89.8