圖像分類：13個(gè)Kaggle項(xiàng)目的經(jīng)驗(yàn)總結(jié)

來(lái)源：數(shù)據(jù)派THU

• 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

• 數(shù)據(jù)
• 模型
• 損失函數(shù)

數(shù)據(jù)

• 圖像預(yù)處理 + 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

• 數(shù)據(jù)增強(qiáng)

• 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

模型

• 開(kāi)發(fā)一個(gè)基線
  

• 開(kāi)發(fā)一個(gè)足夠大可以過(guò)擬合的模型

• 添加更多層
• 使用更好的結(jié)構(gòu)
• 更完善的訓(xùn)練流程

結(jié)構(gòu)

• Residual Networks
• Wide Residual Networks
• Inception
• EfficientNet
• Swish activation
• Residual Attention Network

訓(xùn)練過(guò)程

• 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

超參數(shù)調(diào)試

正則化

• 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

損失函數(shù)

• 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

• 評(píng)估 + 錯(cuò)誤分析

• 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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