name: CaffeNet fine-tuned on the Oxford 102 category flower dataset
caffemodel: oxford102.caffemodel
caffemodel_url: https://s3.amazonaws.com/jgoode/oxford102.caffemodel
gist_id: 0179e52305ca768a601f
license: BSD-3
See https://github.com/jimgoo/caffe-oxford102 for full code.
The CNN is a BVLC reference CaffeNet fine-tuned for the Oxford 102 category flower dataset. The number of outputs in the inner product layer has been set to 102 to reflect the number of flower categories. Hyperparameter choices reflect those in Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data. The global learning rate is reduced while the learning rate for the final fully connected is increased relative to the other layers.
The split file (setid.mat) lists 6,149 images in the test set and 1,020 images in the training set. We have instead trained this model on the larger set of 6,149 images and tested against the smaller set of 1,020 images.
After 50,000 iterations, the top-1 error is 7% on the test set of 1,020 images:
I0215 15:28:06.417726 6585 solver.cpp:246] Iteration 50000, loss = 0.000120038
I0215 15:28:06.417789 6585 solver.cpp:264] Iteration 50000, Testing net (#0)
I0215 15:28:30.834987 6585 solver.cpp:315] Test net output #0: accuracy = 0.9326
I0215 15:28:30.835072 6585 solver.cpp:251] Optimization Done.
I0215 15:28:30.835083 6585 caffe.cpp:121] Optimization Done.
Note that this uses the mean file for ILSVRC 2012 instead of the mean for the actual Oxford dataset.
In case anyone else was interested, I inferred the classes by matching the thumbnail image names on this page against the labelled image set (imagelabels.mat). This list should match the model output:
['pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea', 'english marigold', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood', 'globe thistle', 'snapdragon', "colt's foot", 'king protea', 'spear thistle', 'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily', 'balloon flower', 'giant white arum lily', 'fire lily', 'pincushion flower', 'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy', 'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william', 'carnation', 'garden phlox', 'love in the mist', 'mexican aster', 'alpine sea holly', 'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip', 'lenten rose', 'barbeton daisy', 'daffodil', 'sword lily', 'poinsettia', 'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'oxeye daisy', 'common dandelion', 'petunia', 'wild pansy', 'primula', 'sunflower', 'pelargonium', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia', 'pink-yellow dahlia?', 'cautleya spicata', 'japanese anemone', 'black-eyed susan', 'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'bearded iris', 'windflower', 'tree poppy', 'gazania', 'azalea', 'water lily', 'rose', 'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily', 'anthurium', 'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose', 'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily', 'hippeastrum ', 'bee balm', 'ball moss', 'foxglove', 'bougainvillea', 'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower', 'trumpet creeper', 'blackberry lily']