Multi-class Image Classification using deep convolutional networks on extremely large dataset
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This document summarizes research on using deep convolutional networks for multi-class image classification on a large dataset from a product image classification Kaggle competition. The dataset contains over 5 million images across 5270 categories. Several CNN models were tested including ResNet, ResNext, DenseNet, and WideResNet. WideResNet achieved the best results with over 40% accuracy, while ResNext was the slowest. Training the models required significant computing resources and time due to the large dataset size. Future work includes submitting results to the Kaggle competition after more training epochs.
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Multi-class Image Classification using deep convolutional networks on extremely large dataset
- 1. Multi-class Image Classification using Deep Convolutional Networks on extremely large dataset Marija Stanojevic Ashis Chanda CIS Department, Temple University 5th December 2017
- 2. Problem Definition ❖ Kaggle competition: Cdiscount’s Image Classification Challenge ❖ Classify user posted images into 5270 categories ❖ Challenges of dataset: ➢ Huge amount of data and categories ➢ Background clutter (objects blend into environment) ➢ Viewpoint and data scale variation ➢ Deformation ➢ Occlusion ➢ Illumination conditions ❖ Deep learning models seem suitable Data sets Size # products Train data 58.2 GB 7,069,896 Test data 14.5 GB 1,768,182 Category # Labels Category1 49 Category2 483 Category3 5270
- 4. Dataset: Categories A sample list of product categories:
- 5. Our used models ● CNN ● DenseNet ● ResNet ● ResNext ● Wide-res Net
- 6. Models: CNN ❖ Our CNN ➢ 3 Conv, 2 FC layers, 3 Pool layers ➢ thinner layers ➢ downsampling three times ❖ Tried to use for three types of categories
- 7. ❖ l th layer has connection with all previous layers (l -1) ❖ Normally, each dense block contains 40, 100, 160 layers ❖ We used 3 blocks, 3 layers. Total connection: l x (l -1) /2 = 15 ❖ Each layer has BN, ReLU, Conv ❖ No transition layer (downsampling), but used dropout Models: DenseNet
- 8. Models: ResNet ❖ 34, 50, 101, 152 layers ❖ Residual block example ❖ Batch normalization, ReLu ❖ Our residual network ➢ thinner layers (25 conv + 4 pool + fc) ➢ downsampling after each block ➢ no bottleneck
- 9. ❖ Uses residual block as bases ❖ Block width is divided in k parts which learn separately ❖ Blocks are wider than in resNet ❖ Distinct cardinalities ❖ 29, 50 and 101 layers ❖ Our resNext: ➢ thinner layers ➢ downsampling after each block ➢ less layers (19 conv + 4 pool + fc) ➢ cardinality 32 always - best results according to paper Models: ResNext
- 10. ❖ Uses residual block as bases ❖ Block is k times wider (k=1, 2, 4, 8, 10, 12) ❖ Dropout beside batch normalization ❖ 16, 22, 28, 40 layers ❖ Our wideResNet: ➢ k = 2 - doesn’t increase much number of parameters, but shows biggest improvement in original results ➢ dropout keep = 0.7 - best in original results ➢ less layers (13 conv + 4 pool + fc) Models: WideResNet
- 11. Challenges in dataset ❖ 5.6 TB after splitting data ❖ Train data (707 Batches) & Test data (177 Batches) ❖ Smaller batch: more reads and writes ❖ Bigger batch: memory error ❖ Each batch has 10,000 products ~ 20,000 images ❖ Cross-validation data: 707 products ~ 1500 images
- 12. Challenges in implementation ❖ Used owlsnesttwo high performance computing system ❖ GPU: NVIDIA Tesla P100 PCIe 12GB ❖ Only two jobs allowed in parallel ❖ Implemented with tensorflow and tflearn in python ❖ Network ➢ Complexity in debugging ➢ Bigger network: memory error => tuning network ➢ Thinner and less layers ➢ Small number of epoch
- 13. Baseline (CNN) Result ❖ CNN: (10 Epochs, 10 Batches) ❖ CNN: (50 Epochs, 50 Batches) (in progress) Label # class Accuracy Error Time Category 1 49 52% 10.7 4 hours Category 2 483 47% 11.68 5 hours Category 3 5270 32% 4.89 11 hours Category 1 49 12% (1 epoch) 3.42 20 hours
- 14. Experimental Result (category 3) Model # Batch # Epoch Accuracy Error Time DenseNet 50/707 1 (running) 4% 9.83 10 days ResNet 50/707 10 34% 3.94489 5.69 hours ResNext 50/707 10 28.9% 4.51031 16.8 hours WideResNext 50/707 10 41.93% 3.53318 6.11 hours ResNet 707/707 3 (running) 36.69% 3.84427 3.35 days ResNext 707/707 1 (running) 28.47% 4.50394 9.90 days WideResNext 707/707 2 (running) 40.51% 3.49804 3.60 days
- 15. We are here now
- 16. Experimental results: Error in classifying Mobile cover bag Mobile Case Mobile Laptop cover bag Mobile film protector Mobile Case
- 17. Conclusions and Future Work ❖ Baseline: CNN; Proposed: resNet, resNext, denseNet, wideResNet ❖ All proposed networks have similar number of parameters ❖ wideResNet performs the best ❖ resNext gave worst results and is 3 times slower than resNet ❖ DenseNet requires GPU with huge memory ❖ Requires a lot of time, huge memory and fast computational resources ❖ Number of epochs has to be 70+ ❖ Future: Submit result to Kaggle competition