Train CaffeNet model on custom dataset
How to train CaffeNet on custom dataset
This is short description of training your own custom Net based on your image dataset using pre-trained CaffeNet model.
This repo contains all required Python and shell scripts for pretraining and training stage, not optimized and could be cleaner code.
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Collect all your images for test and valuation process and put them in Test and Train folder, each class of images should be placed as subfolder in these two folders. Example, two classes used for your model, cars and bikes : Train/cars, Train/bikes etc.
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First, create your own train.txt and val.txt files using train_val.py script. The content of these txt files will be as the example:
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+-- train.txt
| +-- Train/bike/bike.jpg 0
| +-- Train/car/car.jpg 1
+-- test.txt
| +-- Test/bike/bike.jpg 0
| +-- Test/car/car.jpg 1
The images are labeled as class numbers. If names of images contain spaces, could be removed using remove_spaces.py.
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The second stage is to create leveldb libraries using create_leveldb.sh, just change the required paths inside the script.
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After that, compute the mean using make_imagenet_mean.sh, the imagenet_mean.binaryproto file will be created.
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Create your proto files, examples are provided, you should have train & val proto files and solver proto file for setting training parameters (momentum, decay, snapshots, no. of iterations etc.). Change the num_output in fully-connected layer for custom number of classes, for example if you are recognizing only bikes and cars num_output = 2.
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The next step is to start training the Net, using finetune_imagenet.sh, gpu or cpu, the log of training will be placed in output_finetune.txt.
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After the training is done, caffemodel files will be generated, snapshots after every 1000th iteration.
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To test the net you can write simpe Python script, bunch of examples are provided, example.
There are few more Python scripts for plotting errors and accuracy out of the log file, for example progres_plot.py to plot accuracy, usage from terminal python progress_plot.py /path/to/output_finetune.txt.
Also, there is example of plotting top-1 & top-5 accuracy using error_plot.py, plots examples in the Plots directory.
Example visualizing filtered images throughout the layers, visualize_layers.py, usage of script provided with example of leaf as an input image. All layers in the Net model could be visualize using visual.py, example in Plots directory.
Further improvements and suggestions are welcome.