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Install all relevant packages pip install -r requirements.txt
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All codes run by jupyter notebook ./example_images contains some example images The training data could be downoload by http://host.robots.ox.ac.uk/pascal/VOC/voc2007/.` ./thesis contains the latex and pdf thesis ./example_reults contains the results of some examples
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For convience, I make each demo for each approach demo_cross.ipynb detects object with cross filter demo_sum.ipynb detects object with sum filter demo_surf.ipynb detects object with surf
All demos could directly run and the output is generated in ./tmp
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Model.py contains the function about the network
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utils.py contains all function for object detection
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feature map visualize.ipynb is used to visualize feature maps of each channel. The output is stored in ./feature/n (n is the current layer).
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feature map visualize multichannels.ipynb is used to visualize feature maps of all channels. The output is stored in ./all.
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detection.ipynb is used to generate the output of test data for evaluation. The output is stored in ./eval/2. ./eval contains an sample
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eval.ipynb is used to calculate the mAP. The output is stored in ./output. ./output contain an sample.
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transfer_learning.ipynb is used to retrain a model for my dataset. The weights is stored in ./weights.