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CALTECH256

CALTECH256

The CALTECH256 dataset

Dataset Statistics

  1. Color: RGB
  2. Sample Size:

Camprison with Caltech-101:

Caltech-256 v.s. Caltech-101

The Number of Samples per Category for Caltech-256

Samples

Dataset Usage

TensorFlow:

Git clone https://github.com/yukunchen113/ResnetCNN.git
   cd RestNetCNN
  1. Use caltech256_bin.py to convet caltech256 images to tfrecord files for faster reading.
  2. Use caltech256_input.py input functions to convert input iput functions to batch images and labels.
  3. Model.py contains resnet model.
  4. Train.py trains the model
  5. Evals.py evaluates the model.

Caffe:

 Git clone https://github.com/PramuPerera/DeepOneClass.git

Pre-processing

  1. This code is developed targeting pycaffe framework. Please make sure caffe and python 2.7 is installed.
  2. Download the code into caffe/examples folder.
  3. Download pre-trained models to caffe/models folder. For VGG16 visit : http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel For Alexnet visit : http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
  4. Download reference dataset to caffe/data. We use ImageNet validation set. It can be found at http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads
  5. Download target datasets to caffe/data. For novelty detection we use Caltech 256 : http://www.vision.caltech.edu/Image_Datasets/Caltech256/ For abnormal image detection, we use Abnormal 1001 as abnormal images : http://paul.rutgers.edu/~babaks/abnormality_detection.html Normal image classes are taken from PASCAL VOC dataset: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/
  6. Edit prototext files to reflect correct paths. Specifically, In solverVGG / solverdistance files, change ’net’ and ‘snapshot_prefix’ with correct file paths. In VGGjoint2 / joint2 files, change ‘source’ parameter in both data and data_c layers.
  7. Move distance_layer.py to caffe/python folder.

Training/ Testing

Abnormal image detection

Two sub directories ‘Abnormal_Object_Dataset’ and ‘Normal_Object_Dataset’ should exist in caffe/data. Each sub folder (of each class) should ne numbered started from 1.

There exists four modes of operation. To test just first class:

  1. Using Alexnet features $python examples/OneClass/src/src/run.py –dataset data/ –backbone Alex –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task abnormal –type feature

  2. Using VGG16 features $python examples/OneClass/src/src/run.py –dataset data/ –backbone VGG –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task abnormal –type feature

  3. Using Alexnet DOC (ours) $python examples/OneClass/src/src/run.py –dataset data/ –backbone Alex –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task abnormal

  4. Using VGG16 DOC (ours) $python examples/OneClass/src/src/run.py –dataset data/ –backbone VGG –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task abnormal

If all 6 classes needs to be tested replace –noneclass 6.

Novelty Detection

Novelty detection dataset should be stored in the caffe/data/novelty directory. Each subfolder (of each class) should ne numbered started from 1.

There exists four modes of operation. To test just first class:

  1. Using Alexnet features $python examples/OneClass/src/src/run.py –dataset data/ –backbone Alex –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task novelty –type feature

  2. Using VGG16 features $python examples/OneClass/src/src/run.py –dataset data/ –backbone VGG –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task novelty –type feature

  3. Using Alexnet DOC (ours) $python examples/OneClass/src/src/run.py –dataset data/ –backbone Alex –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task novelty

  4. Using VGG16 DOC (ours) $python examples/OneClass/src/src/run.py –dataset data/ –backbone VGG –cafferoot /home/labuser/caffe/ –nclass 6 –noneclass 1 –task novelty

If 40 classes needs to be tested instead of just the first, replace –noneclass 40.

Arguments

1.–name : Name of the network. Used to name the performance curve plot and text output containing match scores.

2.–type : Type of CNN : oneclass / feature. When oneclass is used classification is done using DOC. Otherwise pre-trained deep features are used.

3.–output : Output directory name.

4.–dataset : Specify the path to the training dataset. Eg: data/abnormal/

5.–cafferoot : Specify the path to the caffe installation. Default is : /home/labuser/caffe/

6.–backbone : Specify the backbone: VGG/Alex

7.–nclass : Number of total classes in the dataset. 256 for novelty detection and 6 for abnormal image detection.

8.–noneclass : Number of classes to be considered for one-class testing. We used 40 for novelty detection. 6 for abnormal image detection.

9.–task : Specify oneclass task novelty/ abnormal

10.–niter : Number of training iterations

11.–visualize : True/ False specifies whether it is required to generate ROC curve plot.

output

A text file with one-class score values will be written to the output folder. If ‘–visualize’ option is set to True, a ROC curve will also be generated.