NOTE:

This code allows mini-batch greater than 1, not provided in original py-faster-rcnn implementation. Code has been validated to perform as expected (ToConcoctPellucid @ ILSVRC16).
ImageNet images are scaled to 480x480 and allows mini-batch = 4 with ResNet-50 on Nvidia Titan X (12 GB).
Same cannot be done for COCO, why? because COCO dataset has really small objects and a higher resolution is required.
Check this commit for necessary changes and let me know for improvements.
.. ice-pice


##OLDER README

Hope this tutorial will help you a lot! and I appreciate that you can star it!

Training Faster RCNN on Imagenet

preparing data

ILSVRC13 
└─── LSVRC2013_DET_val
    │   *.JPEG (Image files, ex:ILSVRC2013_val_00000565.JPEG)
└─── data
    │   meta_det.mat
    └─── det_lists
             │  val1.txt, val2.txt

Load the meta_det.mat file by

classes = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat'))

Construct IMDB file

There's are several file you need to modify.

factory_imagenet.py

This file is in the directory $FRCNN_ROOT/lib/datasets($FRCNN_ROOT is the where your faster rcnn locate) and is called by train_net_imagenet.py.
It is the interface loading the imdb file.

for split in ['train', 'val', 'val1', 'val2', 'test']:
    name = 'imagenet_{}'.format(split)
    devkit_path = '/media/VSlab2/imagenet/ILSVRC13'
    __sets[name] = (lambda split=split, devkit_path=devkit_path:datasets.imagenet.imagenet(split,devkit_path))

imagenet.py

In function __ init __(self, image_set, devkit_path)

we have to enlarge the number of category from 20+1 into 200+1 categories. Note that in imagenet dataset, the object category is something like "n02691156", instead of "airplane"

self._data_path = os.path.join(self._devkit_path, 'ILSVRC2013_DET_' +     self._image_set[:-1])
synsets = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat'))
self._classes = ('__background__',)
self._wnid = (0,)
for i in xrange(200):
    self._classes = self._classes + (synsets['synsets'][0][i][2][0],)
    self._wnid = self._wnid + (synsets['synsets'][0][i][1][0],)
self._wnid_to_ind = dict(zip(self._wnid, xrange(self.num_classes)))
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))

self._class denotes the class name
self._wnid denotes the id of the category

In function _load_imagenet_annotation(self, index)

This is because in the pascal voc dataset, all coordinates start from one, so in order to make them start from 0, we need to minus 1. But this is not true for imagenet, so we should not minus 1.
So we need to modify these lines to:

for ix, obj in enumerate(objs):
    x1 = float(get_data_from_tag(obj, 'xmin'))
    y1 = float(get_data_from_tag(obj, 'ymin'))
    x2 = float(get_data_from_tag(obj, 'xmax'))
    y2 = float(get_data_from_tag(obj, 'ymax'))
    cls = self._wnid_to_ind[str(get_data_from_tag(obj, "name")).lower().strip()]

Noted that in faster rcnnn, we don't need to run the selective-search, which is the main difference from fast rcnn.

Modify the prototxt

Under the directory $FRCNN_ROOT/

train.prototxt

Change the number of classes into 200+1

param_str: "'num_classes': 201"

In layer "bbox_pred", change the number of output into (200+1)*4

num_output: 804

You can modify the test.prototxt in the same way.

[Last step] Modify the shell script

Under the dircetory $FRCNN_ROOT/experiments/scripts

faster_rcnn_end2end_imagenet.sh

You can specify which dataset to train/test on and your what pre-trainded model is

ITERS=100000
DATASET_TRAIN=imagenet_val1
DATASET_TEST=imagenet_val2
NET_INIT=data/imagenet_models/${NET}.v2.caffemodel

Start to Train Faster RCNN On Imagenet!

Run the $FRCNN/experiments/scripts/faster_rcnn_end2end_imagenet.sh.
The use of .sh file is just the same as the original faster rcnn

Demo

Just run the demo.py to visualize pictures!

faster rcnn with tracker on videos

Original video "https://www.jukinmedia.com/videos/view/5655"

Reference

How to train fast rcnn on imagenet

Others

If you have any advance question, feel free to contact me by [email protected]