Bag of tricks for Image Classification with CNN

Large-batch training

1. Linear scaling learning rate
   • e.g. ResNet-50 SGD 256 batch size 0.1 learning rate
   • init learning . where b is the batch size
2. Learning rate warm up
   • at the beginning, paras are far from the final solution
   • e.g. we use first batch to warm up, the init learning rate is , at the batch where , set the learning rate to be
3. Zero
   • Batch Normalization: Normally, both elements and are initialized to 1s and 0s
   • Instead of setting them in a normal way, it set it as to all BN layers that sit at the end of the residual block (最后一层residual block的BN层).
   • easy to train at the initial stage
4. No bias decay
   • Weight decay will apply to both weight and bias
   • it recommended that only apply to weight regularization to avoid overfitting. BN parameters are left unregularized

Low-precision training (降低位数)

1. Normal setting: 32-bit floating point (FP32) precision
2. Trick switching it to larger batch size (1024) with FP16 and get higher accuracy

Model Tweaks

ResNet Architecture

Screen Shot 2019-02-01 at 3.32.42 PM.png

1. ResNet-B
   • 为了避免 1x1 conv stride=2 带来的information loss
2. ResNet-C
   • 为了避免计算量，使用两个3x3 conv代替一个7x7 conv
3. ResNet-D
   • ResNet-B中path B中的1x1 conv stride=2还是会带来信息丢失，在之前加一个avgpool stride=2 能够有效避免信息丢失

Screen Shot 2019-02-01 at 3.29.10 PM.png

Training Refinement

1. Cosine Learning Rate Decay

   • • where is the total number of batches (ignore warmup stage)
     • t is the current batch
     • is the init learning rate
   • potentially improve the training progress

Cosine Decay

2. Label Smoothing

3. Knowledge Distillation

   1. 训练一个复杂的网络（N1）
   2. 使用数据train N1网络并得到（M1）
   3. 根据复杂网络设计一个简单网络 （N0）
   4. 将M1 softmax 设T=20 预测数据得到 soft target
   5. soft target 和 hard target加权得出Target （推荐0.1:0.9）
   6. 使用 的数据集训练N0（T=20）得到 M0
   7. 设T=1，M0 模型为我们得到的训练好的精简模型

4. Mixup Training

   • Data Augmentation

   • Weighted linear interpolation (双线性插值)

   • In mixup training, we only use

5. Result

Large Batch Training

Refinements