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Spatial Pyramid Pooling in Deep Convolutional Networks using tensorflow
New updates
Instead of sppnet, you can use this block of code in Pytorch to train a neural network with variable-sized inputs:
#With these lines of code below, we can memorize the gradient for later updates using pytorch because the
#loss.backward()function accumulates the gradient. After 64 steps, we call optimizer.step() for updating the parameters.
#https://discuss.pytorch.org/t/how-are-optimizer-step-and-loss-backward-related/7350
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=1, num_workers=8, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=1, num_workers=8, shuffle=False)
for i, (seqs, labels) in enumerate(train_loader):
...
loss = criterion(outputs, labels)
loss.backward()
if i % 64 == 0 or i == len(train_loader) - 1:
optimizer.step()
optimizer.zero_grad()
...
Descriptions
I implemented a Spatial Pyramid Pooling on top of AlexNet in tensorflow. Then I applied it to 102 Category Flower identification task. I implemented for identification task only. If you are interested in this project, I will continue to develop it in object detection task. Do not hesitate to contact me at binhtd.hust@gmail.com. :)
More information: https://peace195.github.io/spatial-pyramid-pooling/
Data
Requirements
- python 2.7
- tensorflow 1.2
- pretrained parameters of AlexNet in ImageNet dataset: bvlc_alexnet.npy
Running
$ python alexnet_spp.py
Result
82% accuracy rate (the state-of-the-art is 94%).
Author
Binh Do