fastrcnn-models.torch icon indicating copy to clipboard operation
fastrcnn-models.torch copied to clipboard

verified publisher icon szagoruyko
→

Metadata

Fast-RCNN models in Torch-7 format

Fast-RCNN models in Torch-7 format

AlexNet and VGG-16 pretrained on ImageNet and finetuned on VOC-2007 converted to Torch-7 format.

Original models can be found here https://github.com/rbgirshick/fast-rcnn/tree/master/data

Files:

  1. imagenet/alexnet/features.t7 and imagenet/alexnet/top.t7: AlexNet pretrained on ImageNet
  2. imagenet/vgg/features.t7 and imagenet/vgg/top.t7: VGG-16 pretrained on ImageNet
  3. caffenet_fast_rcnn_iter_40000.t7 AlexNet pretrained on ImageNet and finetuned on VOC2007
  4. vgg16_fast_rcnn_iter_40000.t7 VGG-16 pretrained on ImageNet and finetuned on VOC2007

Download link

The files are in Yandex cloud storage:

https://yadi.sk/d/R6K_3Dk9nHXn7

Requirements

luarocks install cudnn
luarocks install inn

Loading

Models were cleaned with nn.Module.clearState and gradWeight, gradBias removed so don't forget to run unpack() to restore them if needed.

require 'cudnn'
require 'inn'
net = torch.load'caffenet_fast_rcnn_iter_40000.t7':unpack()

Model printout

Finetuned

AlexNet

nn.Sequential {
  (1): nn.ParallelTable {
    input
      |`-> (1): nn.Sequential {
      |      (1): cudnn.SpatialConvolution(3 -> 96, 11x11, 4,4, 5,5)
      |      (2): cudnn.ReLU
      |      (3): cudnn.SpatialMaxPooling(3,3,2,2,1,1)
      |      (4): cudnn.SpatialCrossMapLRN
      |      (5): cudnn.SpatialConvolution(96 -> 256, 5x5, 1,1, 2,2)
      |      (6): cudnn.ReLU
      |      (7): cudnn.SpatialMaxPooling(3,3,2,2,1,1)
      |      (8): cudnn.SpatialCrossMapLRN
      |      (9): cudnn.SpatialConvolution(256 -> 384, 3x3, 1,1, 1,1)
      |      (10): cudnn.ReLU
      |      (11): cudnn.SpatialConvolution(384 -> 384, 3x3, 1,1, 1,1)
      |      (12): cudnn.ReLU
      |      (13): cudnn.SpatialConvolution(384 -> 256, 3x3, 1,1, 1,1)
      |      (14): cudnn.ReLU
      |    }
      |`-> (2): nn.Identity
       ... -> output
  }
  (2): inn.ROIPooling
  (3): nn.View
  (4): nn.Linear(9216 -> 4096)
  (5): cudnn.ReLU
  (6): nn.Dropout(0.500000)
  (7): nn.Linear(4096 -> 4096)
  (8): cudnn.ReLU
  (9): nn.Dropout(0.500000)
  (10): nn.ConcatTable {
    input
      |`-> (1): nn.Linear(4096 -> 21)
      |`-> (2): nn.Linear(4096 -> 84)
       ... -> output
  }
}

VGG-16

nn.Sequential {
  (1): nn.ParallelTable {
    input
      |`-> (1): nn.Sequential {
      |      (1): cudnn.SpatialConvolution(3 -> 64, 3x3, 1,1, 1,1)
      |      (2): cudnn.ReLU
      |      (3): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
      |      (4): cudnn.ReLU
      |      (5): cudnn.SpatialMaxPooling(2,2,2,2)
      |      (6): cudnn.SpatialConvolution(64 -> 128, 3x3, 1,1, 1,1)
      |      (7): cudnn.ReLU
      |      (8): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
      |      (9): cudnn.ReLU
      |      (10): cudnn.SpatialMaxPooling(2,2,2,2)
      |      (11): cudnn.SpatialConvolution(128 -> 256, 3x3, 1,1, 1,1)
      |      (12): cudnn.ReLU
      |      (13): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
      |      (14): cudnn.ReLU
      |      (15): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
      |      (16): cudnn.ReLU
      |      (17): cudnn.SpatialMaxPooling(2,2,2,2)
      |      (18): cudnn.SpatialConvolution(256 -> 512, 3x3, 1,1, 1,1)
      |      (19): cudnn.ReLU
      |      (20): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
      |      (21): cudnn.ReLU
      |      (22): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
      |      (23): cudnn.ReLU
      |      (24): cudnn.SpatialMaxPooling(2,2,2,2)
      |      (25): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
      |      (26): cudnn.ReLU
      |      (27): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
      |      (28): cudnn.ReLU
      |      (29): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
      |      (30): cudnn.ReLU
      |    }
      |`-> (2): nn.Identity
       ... -> output
  }
  (2): inn.ROIPooling
  (3): nn.View
  (4): nn.Linear(25088 -> 4096)
  (5): cudnn.ReLU
  (6): nn.Dropout(0.500000)
  (7): nn.Linear(4096 -> 4096)
  (8): cudnn.ReLU
  (9): nn.Dropout(0.500000)
  (10): nn.ConcatTable {
    input
      |`-> (1): nn.Linear(4096 -> 21)
      |`-> (2): nn.Linear(4096 -> 84)
       ... -> output
  }
}

Imagenet pretrained

Alexnet features

nn.Sequential {
  (1): cudnn.SpatialConvolution(3 -> 96, 11x11, 4,4, 5,5)
  (2): cudnn.ReLU
  (3): cudnn.SpatialMaxPooling(3,3,2,2,1,1)
  (4): cudnn.SpatialCrossMapLRN
  (5): cudnn.SpatialConvolution(96 -> 256, 5x5, 1,1, 2,2)
  (6): cudnn.ReLU
  (7): cudnn.SpatialMaxPooling(3,3,2,2,1,1)
  (8): cudnn.SpatialCrossMapLRN
  (9): cudnn.SpatialConvolution(256 -> 384, 3x3, 1,1, 1,1)
  (10): cudnn.ReLU
  (11): cudnn.SpatialConvolution(384 -> 384, 3x3, 1,1, 1,1)
  (12): cudnn.ReLU
  (13): cudnn.SpatialConvolution(384 -> 256, 3x3, 1,1, 1,1)
  (14): cudnn.ReLU
}

Alexnet top

nn.Sequential {
  (1): nn.Linear(9216 -> 4096)
  (2): cudnn.ReLU
  (3): nn.Dropout(0.500000)
  (4): nn.Linear(4096 -> 4096)
  (5): cudnn.ReLU
  (6): nn.Dropout(0.500000)
}

VGG-16 features

nn.Sequential {
  (1): cudnn.SpatialConvolution(3 -> 64, 3x3, 1,1, 1,1)
  (2): cudnn.ReLU
  (3): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
  (4): cudnn.ReLU
  (5): cudnn.SpatialMaxPooling(2,2,2,2)
  (6): cudnn.SpatialConvolution(64 -> 128, 3x3, 1,1, 1,1)
  (7): cudnn.ReLU
  (8): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
  (9): cudnn.ReLU
  (10): cudnn.SpatialMaxPooling(2,2,2,2)
  (11): cudnn.SpatialConvolution(128 -> 256, 3x3, 1,1, 1,1)
  (12): cudnn.ReLU
  (13): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
  (14): cudnn.ReLU
  (15): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
  (16): cudnn.ReLU
  (17): cudnn.SpatialMaxPooling(2,2,2,2)
  (18): cudnn.SpatialConvolution(256 -> 512, 3x3, 1,1, 1,1)
  (19): cudnn.ReLU
  (20): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (21): cudnn.ReLU
  (22): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (23): cudnn.ReLU
  (24): cudnn.SpatialMaxPooling(2,2,2,2)
  (25): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (26): cudnn.ReLU
  (27): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (28): cudnn.ReLU
  (29): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (30): cudnn.ReLU
}

VGG-16 top

nn.Sequential {
  [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
  (1): nn.Linear(25088 -> 4096)
  (2): cudnn.ReLU
  (3): nn.Dropout(0.500000)
  (4): nn.Linear(4096 -> 4096)
  (5): cudnn.ReLU
  (6): nn.Dropout(0.500000)
}

Credits

Converted by @szagoruyko 2015

← Metadata

18
Stars
4
Forks
Watchers

Owner

verified publisher icon szagoruyko

Metadata

Fast-RCNN models in Torch-7 format

Back