Good Old Pretraining for Landmark Recognition

Transcript

1. CVPR'19 Landmark Recognition Workshop
   5th Place: Good Old Pretraining (with New Tricks)
   Daniel Fernandez, Andrey Ponikar and Mikhail Fain (Cookpad Ltd.)
   1. Landmark-Not-Landmark classifier
   2. Pretraining SE-ResNeXt-101
   3. Training and inference for 140K common classes
   Manually tag 100
   images as landmarks of
   X different types, or not
   landmarks, using active
   learning techniques
   Retrain top layer of
   ResNet-50 with
   cross-entropy loss
   Landmark-Not-Landmark
   Classifier
   Repeat 10 times
   4MM training
   images,
   200K classes
   Filter out rare
   classes, with
   <25 images
   Filter non-landmarks
   and cluster the
   images using
   ImageNet-based
   features. For each
   class, keep only
   images belonging to
   largest cluster
   600K training
   images, 10K
   classes
   Train
   SE-ResNeXt-101
   with softmax
   cross-entropy
   Feature extractor
   (the layer before
   last)
   Image
   Feature extractors
   (fixed)
   (size 4000x1)
   FC
   (4000x1)
   FC (375x64)
   MatMul
   (375x375)
   FC (64x375)
   Reshape
   (140Kx1)
   FC
   (4000x1)
   Factorized version of FC (140Kx1), to
   regularize and allow larger batch sizes
   Landmark classifier
   (fixed)
   Softmax
   Splitting stage 2 and 3 allowed rapid
   experimentation since features are precomputed
   A bit of manual effort for landmark labeling goes a
   long way, helping with data cleanup and calibrating
   the probabilities for GAP metric
   With appropriate regularization, softmax
   cross-entropy on top of precomputed features is
   still competitive on this task
   The final model is a blend of models with the same
   pipeline as described but minor tweaks (different
   feature extractors, clustering etc.)
   Top-1
   Multiply
   probabilities
   Class and
   probability
   Main Takeaways
   Training: for Stage 3
   model we used
   logarithmic sampling
   scheme with softmax
   cross-entropy as a
   loss function
   

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