Submission for AI for SEA competition for grab (computer vision challenge)
Finetune Resnet_v1_50 architecture for stanford cars dataset classification. (architecture base code on tf-slim model zoo)
Original stanford train set were split 0.8% for training and 0.2% for validation. Fine-tuning the architecture to the dataset results with ~0.9 train accuracy and ~0.75 validation accuracy.
(note: this was run and tested natively on my alienware aurora R6 with GTX1080)
- Download and extract dataset
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http://imagenet.stanford.edu/internal/car196/cars_train.tgz extract it to folder cars_train
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Download and extract labels https://ai.stanford.edu/~jkrause/cars/car_devkit.tgz extract it to folder car_devkit
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Clean dataset (some images are grayscale) run script filter_out_bad_data.py
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Augment dataset run augment_flip.py, augment_croping.py and augment_rotating.py
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Create categorical_folder run generate_categorical_folder.py
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Generate tf.record follow this repo to generate tf.record https://github.com/cannedbot/create_tfrecords
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. run finetune_resnetv1_50.py