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色々使い勝手の良いものに更新
実装すべき内容
- データセットをAnimeFaceからFood101に
- AnimeFaceだとクラス偏りがあり, 精度がサチってるので量子化の比較が上手くできないから
- Pytorch v1.6.0に更新
- apexを別レポジトリからの導入からpytorch準拠のものに
- 量子化モジュールも更新
- nn.DataParallelからnn.DistributedDataParallelに
- DataParallelはマルチスレッド, DistributedDataParallelはマルチプロセスになるからlink
- torchvisionのmask-rcnnの学習がDataParallelに対応しないから
- MLflow導入
- 実験管理を入れる習慣を入れたほうが良い
- docker環境導入
- dockerに慣れたい
レポジトリ構成
- 1_classification_animeface
- フォルダ分けされてる画像で識別する最小バージョン
- MobileNetv2, tensorboard, nn.DataParallel, apexくらいの短期用
- 2_classification_food101
- classificationでMLFlow, distribution等の大規模化, 実験管理等長期用
- 3_classification_food101
- quantization等高速化用
- 4-MSCOCO2014
- Detection, Segmentation
- notebook
- プロファイル, 枝刈り等のnotebook
pytorch 1.1対応
諸々バージョンアップ
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