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K-Means Anchors Ratios Calculator

Example of usage

$ pip install -r requirements.txt
$ python kmeans_anchors_ratios.py --help

output:

usage: python kmeans_anchors_ratios.py \
           --instances path/to/your_coco_instances.json \
           --anchors-sizes 32 64 128 256 512 \
           --input-size 512 \
           --normalizes-bboxes True \
           --num-runs 3 \
           --num-anchors-ratios 3 \
           --max-iter 300 \
           --min-size 0 \
           --iou-threshold 0.5 \
           --decimals 1 \
           --default-anchors-ratios '[(0.7, 1.4), (1.0, 1.0), (1.4, 0.7)]'

optional arguments:
  -h, --help            show this help message and exit
  --instances N         Path to the json instances file in COCO format.
  --anchors-sizes N [N ...]
                        List of anchors sizes (e.g. [32, 64, 128, 256, 512]).
  --input-size N        Size according to which each image is resized before
                        being processed by the model.
  --normalizes-bboxes N
                        Normalizes bounding boxes, before giving them input to
                        K-Means, so that they have all an area of ​​1.
                        Default: True.
  --num-runs N          How many times to run K-Means. After the end of all
                        runs the best result is returned. Default: 1.
  --num-anchors-ratios N
                        The number of anchors ratios to generate. Default: 3.
  --max-iter N          Maximum number of iterations of the K-Means algorithm
                        for a single run. Default: 300.
  --min-size N          Size to which all bounding box sizes must be stricly
                        greater to be considered by K-Means. Filtering is
                        applied after rescaling the bounding boxes to the same
                        extent that the images are scaled to adapt them to the
                        input_size. min_size=32 implies that that all the
                        bounding boxes with an area less than or equal to 1024
                        (32 * 32) will be filtered. Default: 0.
  --iou-threshold N     Threshold above which anchors are assigned to ground-
                        truth object boxes. Default: 0.5.
  --decimals N          Number of decimals to use when rounding anchors
                        ratios. Default: 1.
  --default-anchors-ratios N
                        List of anchors ratios to be compared with those found
                        by K-Means. It must be passed as a string, e.g.
                        '[(0.7, 1.4), (1.0, 1.0), (1.4, 0.7)]'. Default:
                        [(0.7, 1.4), (1.0, 1.0), (1.4, 0.7)].

$ wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
$ unzip annotations_trainval2017.zip
$ python kmeans_anchors_ratios.py \
    --instances ./annotations/instances_train2017.json \
    --anchors-sizes 32 64 128 256 512 \
    --input-size 512 \
    --normalizes-bboxes True \
    --num-runs 3 \
    --num-anchors-ratios 3 \
    --max-iter 300 \
    --min-size 0 \
    --iou-threshold 0.5 \
    --decimals 1 \
    --default-anchors-ratios '[(0.7, 1.4), (1.0, 1.0), (1.4, 0.7)]'

output:

[06/13 12:57:38] Reading ./annotations/instances_train2017.json
[06/13 12:57:54] Starting the calculation of the optimal anchors ratios
[06/13 12:57:54] Extracting and preprocessing bounding boxes
[06/13 12:57:57] Discarding 2 bounding boxes with size lower or equal to 0
[06/13 12:57:57] K-Means (3 runs): 100%|██████████████████| 3/3 [00:33<00:00, 11.06s/it]
        Runs avg. IoU: 80.48% ± 0.00% (mean ± std. dev. of 3 runs, 0 skipped)
        Avg. IoU between bboxes and their most similar anchors after norm. them to make their area equal (only ratios matter):
80.48%
[06/13 12:58:33] Default anchors ratios: [(0.7, 1.4), (1.0, 1.0), (1.4, 0.7)]
        Avg. IoU between bboxes and their most similar default anchors, no norm. (both ratios and sizes matter): 55.16%
        Num. bboxes without similar default anchors (IoU < 0.5):  253049/860001 (29.42%)
[06/13 12:58:37] K-Means anchors ratios: [(0.6, 1.5), (1.0, 1.0), (1.4, 0.7)]
        Avg. IoU between bboxes and their most similar K-Means anchors, no norm. (both ratios and sizes matter): 55.72%
        Num. bboxes without similar K-Means anchors (IoU < 0.5):  240788/860001 (28.00%)
[06/13 12:58:37] K-Means anchors have an IoU < 50% with bboxes in 1.43% less cases than the default anchors, you should consider to use them

For more infos see the tutorial.

Updates

Acknowledgement

The code of this repo is mainly an adaptation of https://github.com/zhouyuangan/K-Means-Anchors.

Compared to the original, the K-Means implementation of this repo is more than 50x faster and some additional features are provided.