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Fine-tune Inception v3 for muli-label classification on HICO dataset in TensorFlow

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chiawen/multi-label-classification-hico

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multi-label-classification-hico

Requirements

  • Python
  • NumPy
  • Tensorflow 1.0

Model

Inception V3 + logistic sigmoid layer

Data

HICO dataset: HICO version 20150920

Please extract the above file and store extracted files inside the ./hico_data directory.

For the CNN, I use Inception v3, pre-trained on ImageNet.

Extract the compressed file and put inception_v3.ckpt into the ./checkpoints directory.

Usage

First, extract the filenames and labels of the training set and the testing set.

$ python process_hico_labels.py

Second, convert the image files and annotations to TFRecords.

$ python hico_to_tfrecords.py

To fine-tune the last layer of Inception v3 for 10 epochs:

$ python finetune.py

To evaluate mAP scores on the testing set:

$ python eval.py

Evaluation

Model mAP
Inception v3 + finetune 0.263

Related works

  • Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, and Jia Deng. HICO: A Benchmark for Recognizing Human-Object Interactions in Images. In ICCV, 2015.
  • Arun Mallya and Svetlana Lazebnik. Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering. In ECCV, 2016.

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Fine-tune Inception v3 for muli-label classification on HICO dataset in TensorFlow

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