图像分类:来自13个Kaggle项目的经验总结
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2020-11-26 23:16
Intel Image Classification:https://www.kaggle.com/puneet6060/intel-image-classification
Recursion Cellular Image Classification:https://www.kaggle.com/c/recursion-cellular-image-classification SIIM-ISIC Melanoma Classification:https://www.kaggle.com/c/siim-isic-melanoma-classification APTOS 2019 Blindness Detection:https://www.kaggle.com/c/aptos2019-blindness-detection/notebooks Diabetic Retinopathy Detection:https://www.kaggle.com/c/diabetic-retinopathy-detection ML Project — Image Classification:https://www.kaggle.com/c/image-classification-fashion-mnist/notebooks Cdiscount’s Image Classification Challenge:https://www.kaggle.com/c/cdiscount-image-classification-challenge/notebooks Plant seedlings classifications: https://www.kaggle.com/c/plant-seedlings-classification/notebooks Aesthetic Visual Analysis: https://www.kaggle.com/c/aesthetic-visual-analysis/notebooks
数据 模型 损失函数
图像预处理 + EDA
Visualisation: https://www.kaggle.com/allunia/protein-atlas-exploration-and-baseline#Building-a-baseline-model- Dealing with Class imbalance:https://www.kaggle.com/rohandeysarkar/ultimate-image-classification-guide-2020 Fill missing values (labels, features and, etc.):https://www.kaggle.com/datafan07/analysis-of-melanoma-metadata-and-effnet-ensemble Normalisation : https://www.kaggle.com/vincee/intel-image-classification-cnn-keras Pre-processing: https://www.kaggle.com/ratthachat/aptos-eye-preprocessing-in-diabetic-retinopathy#3.A-Important-Update-on-Color-Version-of-Cropping-&-Ben's-Preprocessing
数据增强
Horizontal Flip: https://www.kaggle.com/datafan07/analysis-of-melanoma-metadata-and-effnet-ensemble Random Rotate and Random Dihedral:https://www.kaggle.com/iafoss/pretrained-resnet34-with-rgby-0-460-public-lb Hue, Saturation, Contrast, Brightness, Crop:https://www.kaggle.com/cdeotte/triple-stratified-kfold-with-tfrecords Colour jitter: https://www.kaggle.com/nroman/melanoma-pytorch-starter-efficientnet
开发一个基线
开发一个足够大可以过拟合的模型
添加更多层 使用更好的结构 更完善的训练流程
Residual Networks Wide Residual Networks Inception EfficientNet Swish activation Residual Attention Network
Mixed-Precision Training Large Batch-Size Training Cross-Validation Set Weight Initialization Self-Supervised Training (Knowledge Distillation) Learning Rate Scheduler Learning Rate Warmup Early Stopping Differential Learning Rates Ensemble Transfer Learning Fine-Tuning
Adding Dropout: https://www.kaggle.com/allunia/protein-atlas-exploration-and-baseline Adding or changing the position of Batch Norm:https://www.kaggle.com/allunia/protein-atlas-exploration-and-baseline Data augmentation: https://www.kaggle.com/cdeotte/triple-stratified-kfold-with-tfrecords Mixup:https://arxiv.org/abs/1710.09412 Weight regularization: https://www.kaggle.com/allunia/protein-atlas-exploration-and-baseline Gradient clipping:https://www.kaggle.com/allunia/protein-atlas-exploration-and-baseline
Label smoothing Focal loss SparseMax loss and Weighted cross-entropy BCE loss, BCE with logits loss and Categorical cross-entropy loss Additive Angular Margin Loss for Deep Face Recognition
评估 + 错误分析
Tracking metrics and Confusion matrix:https://www.kaggle.com/vincee/intel-image-classification-cnn-keras Grad CAM: https://arxiv.org/pdf/1610.02391v1.pdf Test Time Augmentation (TTA):https://www.kaggle.com/iafoss/pretrained-resnet34-with-rgby-0-460-public-lb
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