MATRIX的人工智能研究及相关内容_C语言_代码_下载

## iNaturalist Challenge(2018) with resnet ## Introduction This release focuses on AI based graphic classification. We train resnet(152/101/50 layers) for iNaturalist Challenge at FGVC 2018 with [tensorpack](https://github.com/ppwwyyxx/tensorpack#toc0), which is a training interface based on TensorFlow. Please note that we have utilised the open-source TensorFlow framework, but based on this we provided our unique algorithm to gain a better performance, which can be indicated by parameters and results. ## Result On inaturalist-2018 Dataset, we train resnet(50/101/152) respectively，the result is as follows: |Model Name|_{train-error-top1}|_{train-error-top3}|_{val-error-top1}|_{val-error-top3}| |--------------------|---------|----------|---------|---------| |_Resnet50| 0.13361 | 0.061188 | 0.399 | 0.24171 | |_Resnet101| 0.105 | 0.061306 | 0.37014 | 0.21371 | |_Resnet152| 0.11464 | 0.059394 | 0.35454 | 0.20024 | ## Installation, Data Preparation, Training and Testing ## Disclaimer * The code is tested on a server with `188.00` GB memory, and `40` core cpu. Data storages in SSD. * we train the model with 8 Pascal Titian XP gpu, for resnet50 the batch is `32*8=256`, for resnet101/152 the batch is `24*8=192` ## Install Dependencies: + python3. We recommend using Anaconda as it already includes many common packages. + Python bindings for OpenCV (Optional, but required by a lot of features) + TensorFlow >= 1.3.0 (Optional if you only want to use `tensorpack.dataflow` alone as a data processing library) ``` # install git, then: pip install -U git+https://github.com/ppwwyyxx/tensorpack.git # or add `--user` to avoid system-wide installation. ``` ## Prepare data data should be organized as follows(set path in config.py): ``` $HOME/DataSet/iNaturalist2018/ |->ground_truth | |train2018.json | |val2018.json | |test2018.json |->train_val2018 |... |->test2018 |... ``` ## Train and Eval * train script example: ``` python iNaturalist-resnet.py --data /home/huzhikun/DataSet/iNaturalist2018/ --batch 192 --mode resnet --gpu 0,1,2,3,4,5,6,7 -d 152 ``` * eval script example: ``` python iNaturalist-resnet.py --eval --data /home/huzhikun/DataSet/iNaturalist2018/ --mode resnet --gpu 0,1,2,3 -d 152 --load train_log/iNaturalist-resnet-d152/model-205065 ``` ## Test with kaggle submit file(.csv) * test script example: ``` python iNaturalist-resnet.py --test --data /home/huzhikun/DataSet/iNaturalist2018/ --mode resnet --gpu 7 -d 152 --load ./train_log/iNaturalist-resnet-d152/model-239190 ``` ## Citing ``` @misc{wu2016tensorpack, title={Tensorpack}, author={Wu, Yuxin and others}, howpublished={\url{https://github.com/tensorpack/}}, year={2016} } ``` Credits ======= +Thanks to everyone who directly contributed to this: - Steve Deng - Bill Lee - Xuyang Wang - Zhikun Hu