# Compute-Features
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.

# Usage
Download one of the checkpoint files below, for example [Inception V1](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz).

```
tar -xvf inception_v1_2016_08_28.tar.gz
python compute_features.py --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 `load_features.py` for an example of how to use the features that were computed. For example,

`python load_features.py 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](https://drive.google.com/file/d/13pyno-mdbazKs0o4N_Pk8ArDtk1RcE-U/view?usp=sharing).
Otherwise links for individual models can be found below.

These CNNs have been trained on the [ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/)
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](https://github.com/BVLC/caffe/wiki/Model-Zoo#models-used-by-the-vgg-team-in-ilsvrc-2014)
and [here](https://github.com/KaimingHe/deep-residual-networks)),
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](http://arxiv.org/abs/1409.4842v1)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v1.py)|[inception_v1_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz)|69.8|89.6|
[Inception V2](http://arxiv.org/abs/1502.03167)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v2.py)|[inception_v2_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz)|73.9|91.8|
[Inception V3](http://arxiv.org/abs/1512.00567)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py)|[inception_v3_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)|78.0|93.9|
[Inception V4](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py)|[inception_v4_2016_09_09.tar.gz](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz)|80.2|95.2|
[Inception-ResNet-v2](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py)|[inception_resnet_v2_2016_08_30.tar.gz](http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz)|80.4|95.3|
[ResNet V1 50](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)|75.2|92.2|
[ResNet V1 101](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz)|76.4|92.9|
[ResNet V1 152](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_152_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz)|76.8|93.2|
[ResNet V2 50](https://arxiv.org/abs/1603.05027)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_50_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz)|75.6|92.8|
[ResNet V2 101](https://arxiv.org/abs/1603.05027)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_101_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz)|77.0|93.7|
[ResNet V2 152](https://arxiv.org/abs/1603.05027)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_152_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz)|77.8|94.1|
[VGG 16](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_16_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz)|71.5|89.8|
[VGG 19](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_19_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz)|71.1|89.8|