Before you begin, make sure to save the required data files for training, validation, and testing. To do this, run the contents of create_input_files.py after pointing it to the the Karpathy JSON file and the image folder containing the extracted train2014 and val2014 folders from your downloaded data.
See train.py.
The parameters for the model (and training it) are at the beginning of the file, so you can easily check or modify them should you wish to.
To train your model from scratch, simply run this file –
python train.py
To resume training at a checkpoint, point to the corresponding file with the checkpoint parameter at the beginning of the code.
Note that we perform validation at the end of every training epoch.
To caption an image from the command line, point to the image, model checkpoint, word map (and optionally, the beam size) as follows –
python caption.py --img='path/to/image.jpeg' --model='path/to/BEST_checkpoint_coco_5_cap_per_img_5_min_word_freq.pth.tar' --word_map='path/to/WORDMAP_coco_5_cap_per_img_5_min_word_freq.json' --beam_size=5