title: How to train new fast-rcnn imageset
body: <p>I am using <strong>fast-rcnn</strong> and try to train the system for new class (label)
  I followed this: <a href="https://github.com/EdisonResearch/fast-rcnn/tree/master/help/train" rel="nofollow noreferrer">https://github.com/EdisonResearch/fast-rcnn/tree/master/help/train</a> </p>
  
  <ol>
  <li><p>Placed the images</p></li>
  <li><p>Placed the annotations </p></li>
  <li><p>Prepare the ImageSet with all the image name prefix</p></li>
  <li><p>Prepared selective search output: train.mat</p></li>
  </ol>
  
  <p><strong>I failed while running the train_net.py with the following error:</strong></p>
  
  <pre><code>./tools/train_net.py --gpu 0 --solver models/VGG_1024_pascal2007/solver.prototxt --imdb voc_2007_train_top_5000 
  
  Called with args: Namespace(cfg_file=None, gpu_id=0, imdb_name='voc_2007_train_top_5000', max_iters=40000, pretrained_model=None, randomize=False, solver='models/VGG_1024_pascal2007/solver.prototxt') Using config: {'DEDUP_BOXES': 0.0625,  'EPS': 1e-14,  'EXP_DIR': 'default',  'PIXEL_MEANS': array([[[ 102.9801,  115.9465,  122.7717]]]),  'RNG_SEED': 3,  'ROOT_DIR': '/home/hagay/fast-rcnn',  'TEST': {'BBOX_REG': True,
            'MAX_SIZE': 1000,
            'NMS': 0.3,
            'SCALES': [600],
            'SVM': False},  'TRAIN': {'BATCH_SIZE': 128,
             'BBOX_REG': True,
             'BBOX_THRESH': 0.5,
             'BG_THRESH_HI': 0.5,
             'BG_THRESH_LO': 0.1,
             'FG_FRACTION': 0.25,
             'FG_THRESH': 0.5,
             'IMS_PER_BATCH': 2,
             'MAX_SIZE': 1000,
             'SCALES': [600],
             'SNAPSHOT_INFIX': '',
             'SNAPSHOT_ITERS': 10000,
             'USE_FLIPPED': True,
             'USE_PREFETCH': False}} Loaded dataset `voc_2007_train` for training Appending horizontally-flipped training examples... voc_2007_train gt roidb loaded from /home/hagay/fast-rcnn/data/cache/voc_2007_train_gt_roidb.pkl /usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:2507: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.   VisibleDeprecationWarning) wrote ss roidb to /home/hagay/fast-rcnn/data/cache/voc_2007_train_selective_search_IJCV_top_5000_roidb.pkl Traceback (most recent call last):   File "./tools/train_net.py", line 80, in &lt;module&gt;
      roidb = get_training_roidb(imdb)   File "/home/hagay/fast-rcnn/tools/../lib/fast_rcnn/train.py", line 107, in get_training_roidb
      imdb.append_flipped_images()   File "/home/hagay/fast-rcnn/tools/../lib/datasets/imdb.py", line 104, in append_flipped_images
      assert (boxes[:, 2] &gt;= boxes[:, 0]).all() AssertionError
  </code></pre>
  
  <p>My Questions is:</p>
  
  <ol>
  <li>Why am I having this error?</li>
  <li>Do I need to rescale the images to fix: 256x256 before training?</li>
  <li>Do I need to prepare something in order to set the <code>__background__</code> class?</li>
  </ol>