WAIC2019 hackthon (Webank Garbage Classification)

1st Place Solution to WAIC2019 hackthon Garbage Classification Challenge

Time: August 29-31, 2019, Shanghai, China

Team: Skye

Team Member: Skye (yeah, I'm a solo player :ghost:)

CONTENTS

• Introduction
• Rules
• Method
  • Problem Analysis
  • Classifier Design
  • Feature Extractor Design
  • Inference Strategy
• Experiments
• Usage
  • Requirements
  • File Description
  • Training
  • Evaluation
  • Inference
• Acknowledgement
• References
• Contact

1 Intro

This repo is the solution of team Skye, the hackthon lasts for 36 hours, all contestants need to develop a model for garbage image classification within the specified time. See slides.

Since July 1, 2019, Shanghai has taken the lead in implementing the four-category policy of garbage (all garbage are classified into 4 categoires: harmful/recyclable/other/kitchen). Since my roommates and me are firm practitioners of this policy, I'm very interested in this challenge and participated in alone (they are better at hardware than programming).

2 Rules

• Datasets

Training set: ~20,000 images

Testing set: 9,000 images (invisible to participants)

• Label

Every sample has two level labels: level-1 label for 4 coarse categories and level-2 for specific objects. For example:

CD、DVD,2,223

2: other

223: CD、DVD

• Evaluation Metric

The final accuracy only computed on 4 coarse categories (level-2 label is not required for inference), each sample has only one category.

3 Method

3.1 Problem Analysis

Before doing anything, I analyzed this problem and made 2 conclusions:

• Level-2 label is more important than level-1 label:

Since a mapping_list.txt is provided, we can always get the right level-1 label by mapping level-2 to level-1.

• Label Mapping is important for inference:

Although we have mapping_list.txt, here is a question when directly mapping level-2 label to level-1 label: Level-1 label or mapped level-2 label, which one should be trusted during inferencing? It is obvious that setting proper confidence distribution for level-1 label and mapped level-2 label is rather important for inference.

Based on the analysis, my strategy is consists of three parts: classifier design, feature extractor design and inference strategy.

3.2 Classifier Design

I treat this problem as a multi-class image classification problem, each image has two classes so the multi-hot label vector is filled with 2 ones and 401 zeros. (403 labels in total: 4 level-1 label + 399 level-2 label)

So the classifier is simply a Softmax classifier with a fully connected layer, and loss is multi-class cross entropy loss.

Another intesting design is utilizing two classifiers: one for level-1 label classification and the other for level-2. However, after training my model with this kind of design, loss gets harder to converge. (Maybe sharing the same feature extractor parameter weights for both classifiers is not brilliant.)

3.3 Feature Extractor Design

• Baseline: SENet154 (Squeeze-and-Excitation Network)

This is a strong baseline for ImageNet classification, I utilized this model and the weights pretrained on ImageNet, then fine-tuned all weights (with a modified last linear layer).

• Local Feature: Non-local Block 2D (Gaussian Version)

Non-local block has a very nice property that the input size and output size are always equal, so it is rather easy to insert the block to any off-the-shelf model. I inserted three non-local blocks (see pretrainedmodels/models/senet.py for details.)

• Global Feature: NetVLAD Encoding

NetVLAD is an effective feature aggregation method and is widely applied to image/video understanding challenges (see YouTube-8M Video Understanding Challenge). I applied NetVLAD encoding to feature from the last average pooling layer of SENet154.

3.4 Inference Strategy*

Inference strategy including label mapping is very important as aforementioned. I came up with three kinds of inference strategies during competition:

• Naïve Inference

with torch.no_grad():
        batch_size = outputs.size(0)
        valid_indices = [0,1,2,3]
        valid_pred = torch.zeros(outputs.size())
        for i in range(batch_size):
            valid_pred[i][valid_indices] = outputs[i][valid_indices].cpu().float()
        _, pred_indices = valid_pred.topk(1,1,True,True)

I utilized Naïve Inference for final submission, the Naïve Inference only takes indices [0,1,2,3] into account, the corresponding predictions are valid_pred, then argmax(valid_pred) is chosen as final prediction.

• Hard Mapping

with torch.no_grad():
        batch_size = outputs.size(0)
        _, pred = outputs.topk(topk, 1, True, True)
        for j in range(batch_size):
            hash_table = np.zeros([4])
            single_pred = pred[j]
            for i in range(len(single_pred)):
                if single_pred[i].item() not in [0,1,2,3]:
                    hash_table[self.label_map[str(single_pred[i].item())]] += 1
                else:
                    hash_table[single_pred[i].item()] += 1

            index = np.argmax(hash_table)

Take the top k predictions as candidate perdictions, then if level-1 label is in top k predictions (denoted as P_valid), take argmax(P_valid); if all top k predictions are level-2 label, then mapping level-2 label to level-1 label and vote for final prediction. (Maybe taking top 1 mapped level-2 label is a better choice.)

• Soft Mapping

Experimental results show that hard mapping results in clear performance drop, this is because that voting is not suitable for the classifier. (for example, a prediction vector [0.8,0.5,0.5,0.5,0.5], the correct answer is 0, however when applying voting, the answer is not 0.)

A better design for mapping level-2 label to level-1 label is letting the mapping process to be learnt by model. For example, we can use two classifiers: level-1 classifier and level-2 classifier, the predictions from them are level-1 predictions (4-D vector) and level-2 predictions (399-D vector) respectively. Then we can use linear transformation to map a 399-D vector to a 4-D vector and use torch.mm(mapped_vector, level-1 predictions) to get final prediction, thus the final prediction is decided by both level-1 classifier and level-2 classifier.

4 Experiments

Training dataset: 50%

Evaluation dataset: 50%

For inference: 95% training data

Validation Results

Inference Results

An interesting observation: testing results are much lower than validation results, that is because the training and validation data are crawled from web while the testing data is captured in our daily life which is the real garbage!

5 Usage

5.0 Requirements

pip install torchvision==0.4.0 \    # torch 1.2.0 will be installed automatically
            numpy==1.15.4 \
            pillow==5.4.1 \

5.1 File Description

2019WAIC-hackthon-Garbage-Classification
|-- data-processing.py                       # crawl, rename and validate data
|-- DataReader.py                            # ImageLoader, ImageTransformation
|-- eval.py                                  # evaluation code
|-- inference                                # files for final submission
|   |-- DataReader.py                             # ImageLoader, ImageTransformation
|   |-- inference.py                              # inference python code
|   |-- inference.sh                              # inference shell code
|   |-- log                                       # directory to store model checkpoints
|   |-- mapping_list.txt                          # modified level-1 and level-2 label mapping list
|   |-- modified.lst                              # modified training data labels after modifying line:0-3 in mapping_list.txt
|   |-- pretrainedmodels                          # pretrained image classification models
|   |-- result.txt                                # submission file
|   |-- test.txt                                  # data list for inference
|   |-- utils                                     # inference utils
|   `-- validate.py                               # validate result.txt(not neccessary)
|-- lists                                    # directory stors train and validation data
|   |-- back                                 # for offline test (50% train, 50% test)
|   |-- mklist.py                            # python script to create test.lst and train.lst (95% train, 5% test)
|   |-- test.lst                             # validation data(see test data in ./inference)
|   `-- train.lst                            # train data
|-- log
|   `-- senet154-FT-08301618_plain           # senet154 checkpoints
|-- mapping_list.txt                         # modified level-1 and level-2 label mapping list
|-- modified.lst                             # modified training data labels after modifying line:0-3 in mapping_list.txt
|-- pretrainedmodels                         # pretrained image classification models
|   |-- ckpts
|   |-- datasets
|   |-- __init__.py
|   |-- models                               # off-the-shelf models (including senet154)
|   |-- utils.py
|   `-- version.py
|-- README.md
|-- success.lst                              # valide downloaded images, transformed into .jpeg format
|-- sync_batchnorm                           # utils for torch.nn.DataParallel(), with sync_batchnorm we gain better results
|   |-- batchnorm.py
|   |-- batchnorm_reimpl.py
|   |-- comm.py
|   |-- __init__.py
|   |-- __pycache__
|   |-- replicate.py
|   `-- unittest.py
|-- train.py                                 # train code
|-- train-twoclassifier.py                   # train two classifiers code (not work)
`-- utils                                    # utils for train and evaluation
    |-- eval_utils.py
    |-- __init__.py
    `-- train_utils.py

5.2 Training

python train.py model_name gpu_id batch_size

example:

python train.py senet154 0 128

Note: For the first time, a script in ./pretrainedmodels will automatically download a model dict file pretrained on ImageNet.

5.3 Evaluation

python eval.py model_name gpu_id batch_size model_path

example:

python eval.py senet154 0 128 ./log/senet154-FT-08301618_plain/Epoch_4

Then a file named [model_name].lst will be generated, which including all data ids, topk ground truth and topk predictions.

5.4 Inference

cd inference
python inference.py test.txt

Then a file named result.txt will be generated for submission.

Acknowledgement

Thanks to WAIC committe（世界人工智能大会）, Tencent Webank（腾讯微众银行）and Synced (机器之心).

References

[1] Wang, Xiaolong, et al. "Non-local neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.

[2] Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

[3] Arandjelovic, Relja, et al. "NetVLAD: CNN architecture for weakly supervised place recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

Contact

If you are interested in this project, for sharing solutions or discussing questions, please send me an e-mail: [email protected]. PR & issues are welcome.