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 [email protected]. :)

More information: https://peace195.github.io/spatial-pyramid-pooling/

Data

102 Category Flower Dataset

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