Skip to content

Latest commit

 

History

History
112 lines (78 loc) · 3.5 KB

README.md

File metadata and controls

112 lines (78 loc) · 3.5 KB
Raw

Classification Example

This example shows how to use python to consume a DIGITS model and classify images. You should see the exact same numerical answers between DIGITS and these scripts (but take note of the "Limitations" section).

Limitations

There are a few "gotchas" which can lead to discrepancies between DIGITS classifications and the output from this example.

Image resizing

This example resizes images using the Squash method. In DIGITS, the same method will be used as was originally used when creating your dataset (Crop, Squash, Fill or HalfCrop).

Mean subtraction

This example subtracts a mean pixel rather than the whole mean file. In DIGITS, the same method will be used as was originally used when training your model (None, Image or Pixel).

Requirements

See BuildCaffe.md for instructions about installing caffe. Other requirements can be found in requirements.txt. You do not need as many packages to run this example as you do to run DIGITS.

Usage

Use one of the two python scripts provided to classify an image.

Using a model archive

Use use_archive.py to classify images using a model archive downloaded from DIGITS (e.g. 20150512-171624-d9a9_epoch_30.tar.gz).

$ ./use_archive.py -h
usage: use_archive.py [-h] [--nogpu] archive image

Classification example using an archive - DIGITS

positional arguments:
  archive     Path to a DIGITS model archive
  image       Path to an image

optional arguments:
  -h, --help  show this help message and exit
  --nogpu     Don't use the GPU

$ ./use_archive.py digits-model.tar.gz test-image.jpg
Extracting tarfile ...
Processed 1/1 images ...
Classification took 0.00310683250427 seconds.
--------------------------- Prediction for image.jpg ---------------------------
 96.1199% - "0"
  1.3588% - "6"
  0.7247% - "9"
  0.4695% - "2"
  0.3857% - "3"

Script took 0.270452022552 seconds.

Using individual model files

If you have already extracted your model, you can specify each of the files manually with example.py.

$ ./example.py -h
usage: example.py [-h] [-m MEAN] [-l LABELS] [--nogpu]
               caffemodel deploy_file image

Classification example - DIGITS

positional arguments:
  caffemodel            Path to a .caffemodel
  deploy_file           Path to the deploy file
  image                 Path to an image

optional arguments:
  -h, --help            show this help message and exit
  -m MEAN, --mean MEAN  Path to a mean file (*.npy)
  -l LABELS, --labels LABELS
                        Path to a labels file
  --nogpu               Don't use the GPU

$ ./example.py snapshot_iter_1000.caffemodel deploy.prototxt test-image.jpg --mean mean.binaryproto --labels labels.txt
Processed 1/1 images ...
Classification took 0.00309991836548 seconds.
--------------------------- Prediction for image.jpg ---------------------------
 96.1199% - "0"
  1.3588% - "6"
  0.7247% - "9"
  0.4695% - "2"
  0.3857% - "3"

Script took 0.269672870636 seconds.

Extensions

This example is kept pretty basic. The user is encouraged to extend it to suit their own purposes.

Multiple images

The code already supports classifying a list of images, you just have to provide your own code to give the list of filenames.

Batched inference

Already done, just provide a value other than 1 for batch_size in classify().

Filter visualization

See Caffe's example for extracting filters from a trained model: https://github.com/BVLC/caffe/blob/rc2/examples/filter_visualization.ipynb