You could download Microsoft COCO dataset from here
VGG 16 pretrained model could download from here
如果你是**人，可以从百度网盘的这里下载
Then unzip COCO and specify path in image_caption_keras.py :
vgg_model_weights = '/home/qhduan/Downloads/COCO/vgg16_weights.h5'
coco_train = '/home/qhduan/Downloads/COCO/train2014'
coco_caption = '/home/qhduan/Downloads/COCO/annotations/captions_train2014.json'
You could open preview.ipynb to view the train result and test result.
$ python3 image_caption_keras.py
Using Theano backend.
Using gpu device 0: GeForce GTX 1070 (CNMeM is disabled, cuDNN 5105)
/home/qhduan/.local/lib/python3.5/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.
warnings.warn(warn)
[nltk_data] Downloading package punkt to /home/qhduan/nltk_data...
[nltk_data] Package punkt is already up-to-date!
train_size 82783
100%|████████████████████████████████████████████████████████████████████████████████████████| 82783/82783 [00:06<00:00, 12363.22it/s]
vocabulary_size 8679
max_len 55
train_words_size 935568
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
(256, 4096) (256, 55) (256, 8679)
Epoch 1/20
935680/935680 [==============================] - 980s - loss: 4.1896 - acc: 0.2899
Epoch 2/20
935680/935680 [==============================] - 980s - loss: 3.1131 - acc: 0.4088
Epoch 3/20
935680/935680 [==============================] - 1024s - loss: 2.8843 - acc: 0.4295
Epoch 4/20
935680/935680 [==============================] - 1053s - loss: 2.7526 - acc: 0.4415
Epoch 5/20
935680/935680 [==============================] - 1053s - loss: 2.6622 - acc: 0.4504
Epoch 6/20
935680/935680 [==============================] - 1001s - loss: 2.5747 - acc: 0.4587
Epoch 7/20
935680/935680 [==============================] - 988s - loss: 2.4988 - acc: 0.4663
Epoch 8/20
935680/935680 [==============================] - 1060s - loss: 2.4339 - acc: 0.4740
Epoch 9/20
935680/935680 [==============================] - 1032s - loss: 2.3833 - acc: 0.4802
Epoch 10/20
935680/935680 [==============================] - 1005s - loss: 2.3305 - acc: 0.4866
Epoch 11/20
935680/935680 [==============================] - 1007s - loss: 2.2816 - acc: 0.4927
Epoch 12/20
935680/935680 [==============================] - 1063s - loss: 2.2408 - acc: 0.4987
Epoch 13/20
935680/935680 [==============================] - 1031s - loss: 2.1983 - acc: 0.5048
Epoch 14/20
935680/935680 [==============================] - 995s - loss: 2.1705 - acc: 0.5086
Epoch 15/20
935680/935680 [==============================] - 991s - loss: 2.1432 - acc: 0.5122
Epoch 16/20
935680/935680 [==============================] - 984s - loss: 2.1109 - acc: 0.5173
Epoch 17/20
935680/935680 [==============================] - 978s - loss: 2.0837 - acc: 0.5222
Epoch 18/20
935680/935680 [==============================] - 978s - loss: 2.0582 - acc: 0.5257
Epoch 19/20
935680/935680 [==============================] - 978s - loss: 2.0487 - acc: 0.5281
Epoch 20/20
935680/935680 [==============================] - 978s - loss: 2.0179 - acc: 0.5325