Cannot even make the demo work...help...

I am pretty disappointed in myself. Appologies for my stupid question in advance. I am pretty new to all these detection stuff and pytorch thing.

I went through the way that you explained to run the demo. I guess i have everything set as you said. I have a data folder, inside of it i have two folders (pretrained_model and VOCdevkit2007). the VOCdevkit2007 is created via softlink. In the pretrained_model folder I have resnet101_caffe.pth and vgg16_caffe.pth.

I am trying to run the demo to run it on the images that you provided on the images folder. So I am going to run this command:

python demo.py --net vgg16 \ --checksession $SESSION --checkepoch $EPOCH --checkpoint $CHECKPOINT \ --cuda --load_dir path/to/model/directoy

and i run it like this:

python demo.py --net vgg16 \ --checksession 1 --checkepoch 6 --checkpoint 416 \ --cuda --load_dir /data/pretrained_model/vgg16_caffe.pth

am i doing anything wrong so far? This is what happened and it is not working:

Namespace(batch_size=1, cfg_file='cfgs/vgg16.yml', checkepoch=6, checkpoint=416, checksession=1, class_agnostic=False, cuda=True, dataset='pascal_voc', image_dir='images', load_dir='/data/pretrained_model/vgg16_caffe.pth', mGPUs=False, net='vgg16', parallel_type=0, set_cfgs=None, vis=False, webcam_num=-1)
Using config:
{'ANCHOR_RATIOS': [0.5, 1, 2],
 'ANCHOR_SCALES': [8, 16, 32],
 'CROP_RESIZE_WITH_MAX_POOL': False,
 'CUDA': False,
 'DATA_DIR': '/home/alireza/Desktop/Code/VID/FRCNN2/faster-rcnn.pytorch/data',
 'DEDUP_BOXES': 0.0625,
 'EPS': 1e-14,
 'EXP_DIR': 'vgg16',
 'FEAT_STRIDE': [16],
 'GPU_ID': 0,
 'MATLAB': 'matlab',
 'MAX_NUM_GT_BOXES': 20,
 'MOBILENET': {'DEPTH_MULTIPLIER': 1.0,
               'FIXED_LAYERS': 5,
               'REGU_DEPTH': False,
               'WEIGHT_DECAY': 4e-05},
 'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),
 'POOLING_MODE': 'align',
 'POOLING_SIZE': 7,
 'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False},
 'RNG_SEED': 3,
 'ROOT_DIR': '/home/alireza/Desktop/Code/VID/FRCNN2/faster-rcnn.pytorch',
 'TEST': {'BBOX_REG': True,
          'HAS_RPN': True,
          'MAX_SIZE': 1000,
          'MODE': 'nms',
          'NMS': 0.3,
          'PROPOSAL_METHOD': 'gt',
          'RPN_MIN_SIZE': 16,
          'RPN_NMS_THRESH': 0.7,
          'RPN_POST_NMS_TOP_N': 300,
          'RPN_PRE_NMS_TOP_N': 6000,
          'RPN_TOP_N': 5000,
          'SCALES': [600],
          'SVM': False},
 'TRAIN': {'ASPECT_GROUPING': False,
           'BATCH_SIZE': 256,
           'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
           'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0],
           'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2],
           'BBOX_NORMALIZE_TARGETS': True,
           'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True,
           'BBOX_REG': True,
           'BBOX_THRESH': 0.5,
           'BG_THRESH_HI': 0.5,
           'BG_THRESH_LO': 0.0,
           'BIAS_DECAY': False,
           'BN_TRAIN': False,
           'DISPLAY': 10,
           'DOUBLE_BIAS': True,
           'FG_FRACTION': 0.25,
           'FG_THRESH': 0.5,
           'GAMMA': 0.1,
           'HAS_RPN': True,
           'IMS_PER_BATCH': 1,
           'LEARNING_RATE': 0.01,
           'MAX_SIZE': 1000,
           'MOMENTUM': 0.9,
           'PROPOSAL_METHOD': 'gt',
           'RPN_BATCHSIZE': 256,
           'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
           'RPN_CLOBBER_POSITIVES': False,
           'RPN_FG_FRACTION': 0.5,
           'RPN_MIN_SIZE': 8,
           'RPN_NEGATIVE_OVERLAP': 0.3,
           'RPN_NMS_THRESH': 0.7,
           'RPN_POSITIVE_OVERLAP': 0.7,
           'RPN_POSITIVE_WEIGHT': -1.0,
           'RPN_POST_NMS_TOP_N': 2000,
           'RPN_PRE_NMS_TOP_N': 12000,
           'SCALES': [600],
           'SNAPSHOT_ITERS': 5000,
           'SNAPSHOT_KEPT': 3,
           'SNAPSHOT_PREFIX': 'res101_faster_rcnn',
           'STEPSIZE': [30000],
           'SUMMARY_INTERVAL': 180,
           'TRIM_HEIGHT': 600,
           'TRIM_WIDTH': 600,
           'TRUNCATED': False,
           'USE_ALL_GT': True,
           'USE_FLIPPED': True,
           'USE_GT': False,
           'WEIGHT_DECAY': 0.0005},
 'USE_GPU_NMS': True}
Traceback (most recent call last):
  File "demo.py", line 163, in <module>
    raise Exception('There is no input directory for loading network from ' + input_dir)
Exception: There is no input directory for loading network from /data/pretrained_model/vgg16_caffe.pth/vgg16/pascal_voc

Can anyone please help me … Thanks

[cbasavaraj]

These models are pre-trained on ImageNet images for classification only. So you use them as the backbone for faster-rcnn which is an object detection architecture that can use many different underlying CNNs. Faster-RCNN itself has to be trained by you now for object detection.

[isalirezag]

@cbasavaraj Thank you for your help. I will try google cloud. but in the repository what they mean when they said:

Pretrained Model
We used two pretrained models in our experiments, VGG and ResNet101. You can download these two models from:
VGG16: Dropbox, VT Server
ResNet101: Dropbox, VT Server
Download them and put them into the data/pretrained_model/.

what are these pretrained model useful for?