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https://github.com/amirassov/kaggle-imaterialist

The First Place Solution of Kaggle iMaterialist (Fashion) 2019 at FGVC6
https://github.com/amirassov/kaggle-imaterialist

computer-vision deep-learning hybrid-task-cascade imaterialist instance-segmentation kaggle kaggle-imaterialist mmdetection object-detection pytorch

Last synced: 10 days ago
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The First Place Solution of Kaggle iMaterialist (Fashion) 2019 at FGVC6

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# The First Place Solution of [iMaterialist (Fashion) 2019](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6/)



![ensemble](figures/prediction.png)



## Solution

My solution is based on the COCO challenge 2018 winners article: https://arxiv.org/abs/1901.07518. 



### Model: 

[Hybrid Task Cascade with ResNeXt-101-64x4d-FPN backbone](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py). This model has a metric Mask mAP = 43.9 on COCO dataset. This is SOTA for instance segmentation.



### Validation:

For validation, I used 450 training samples splitted using https://github.com/trent-b/iterative-stratification.



### Preprocessing:

I applied light augmentatios from the [albumentations](https://github.com/albu/albumentations) library to the original image. Then I use multi-scale training: in each iteration, the scale of short edge is randomly sampled

from [600, 1200], and the scale of long edge is fixed as 1900.



![preprocessing](figures/preproc.png)



### Training details:

* pre-train from COCO

* optimizer: `SGD(lr=0.03, momentum=0.9, weight_decay=0.0001)`

* batch_size: 16 = 2 images per gpu x 8 gpus Tesla V100

* learning rate scheduler:

```

if iterations < 500:

   lr = warmup(warmup_ratio=1 / 3)

if epochs == 10:

   lr = lr ∗ 0.1

if epochs == 18:

   lr = lr ∗ 0.1

if epochs > 20:

   stop

```

* training time: ~3 days.



### Parameter tuning:

After the 12th epoch with the default parameters, the metric on LB was **0.21913**. Next, I tuned postprocessing thresholds using validation data:

```

rcnn=dict(

    score_thr=0.5,

    nms=dict(type='nms', iou_thr=0.3),

    max_per_img=100,

    mask_thr_binary=0.45

)

```



This improved the metric on LB: **0.21913 -> 0.30011.**



### Test time augmentation:

I use 3 scales as well as horizontal flip at test time and ensemble the results. Testing scales are (1000, 1600), (1200, 1900), (1400, 2200). 



I drew a TTA scheme for Mask R-CNN, which is implemented in mmdetection library. For Hybrid Task Cascade R-CNN, I rewrote this code. 



This improved the metric on LB: **0.30011 -> 0.31074.**



![TTA](figures/tta.png)



### Ensemble:

I ensemble the 3 best checkpoints of my model. The ensemble scheme is similar to TTA. 



This improved the metric on LB: **0.31074 -> 0.31626.**



![ensemble](figures/ensemble.png)



### Attributes:

I didn't use attributes at all: they were difficult to predict and the removal of classes with attributes greatly improved the metric. 



During the whole competition, I deleted classes with attributes: `{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}` U `{27, 28, 33}`. But two days before the end I read [the discussion] (https://www.kaggle.com/c/kaggle-imaterialist-fashion-2019-FGVC6/discussion/94811#latest548137) and added back classes `{27, 28, 33 }`. 



This improved the metric on LB: **0.31626 -> 0.33511.**



### Postprocessing for masks

My post-processing algorithm for avoid intersections of masks of the same class:

```python

def hard_overlaps_suppression(binary_mask, scores):

    not_overlap_mask = []

    for i in np.argsort(scores)[::-1]:

        current_mask = binary_mask[..., i].copy()

        for mask in not_overlap_mask:

            current_mask = np.bitwise_and(current_mask, np.invert(mask))

        not_overlap_mask.append(current_mask)

    return np.stack(not_overlap_mask, -1)

```



### Small postprocessing:

I deleted objects with an area of less than 20 pixels. 



This improved the metric on LB: **0.33511 -> 0.33621.**



## How to run?



### Docker

```bash

make build

make run-[server-name]

make exec

```



### Build mmdetection:

```bash

cd mmdetection

bash compile.sh

python setup.py develop

```



### Prepare pretrained weights:

```bash

bash prepare_weights.sh

```



### Data structure

```

/data/

├── train/

│   └── ...

├── test/

│   └── ...

└── train.csv.zip

/dumps/

└── htc_dconv_c3-c5_mstrain_x101_64x4d_fpn_20e_1200x1900/



```

Fix the [error](https://www.kaggle.com/c/kaggle-imaterialist-fashion-2019-FGVC6/discussion/91217#latest-529042) in `train.csv.zip.`



### Prepare annotations for mmdetection:

```bash

cd scripts

bash create_mmdetection_train.sh

bash create_mmdetection_test.sh

bash split.sh

```



### Training the model:

```bash

CUDA_VISIBLE_DEVICES=[list of gpus] bash dist_train.sh [config] [gpus] [--validate] 

```



#### My best checkpoint:

https://yadi.sk/d/-raqliq_ad6r_Q



### Test the model:

```bash

CUDA_VISIBLE_DEVICES=[list of gpus] bash dist_test_ensemble.sh [config] [gpus]

```



## References

* https://github.com/open-mmlab/mmdetection