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ANALYSIS.md

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Analyzing Pre-trained Features for Part Segmentation

Visualizing Part Segments by Pixel Grouping

We first explore different pre-trained features and their capability of part segmentation. We provide a demo to try out:

python part_segment_demo.py --input figs/input/cat_and_cola.jpg --output figs/output/cola.jpg --vocabulary custom --confidence-threshold 0.1 --custom_vocabulary cola --min-image-size 640 --k 4 --weight-name coco_instance_seg --dcrf

python part_segment_demo.py --input figs/input/cat_and_cola.jpg --output figs/output/cat.jpg --vocabulary custom --confidence-threshold 0.1 --custom_vocabulary cat --min-image-size 640 --k 4 --weight-name coco_instance_seg --dcrf

Above command reads cat.jpg image as input, and use Detic to first segment instance of the prompted class (--custom_vocabulary, "cat" in this case). Then it uses the pre-trained features specified with --weight-name to cluster the features to group pixels.

  • --k is used for the number of clusters.
  • --dcrf is used for applying dense-CRF as post-processing.
  • See here to find the available weight-name options. Please download the weights from Mask2Former (here) and place them under ./weights/... (see here).

If setup correctly, the result should look like below:

Evaluating Part Segments on PartImageNet

Here we evaluate the pixel-grouping as part segments on PartImageNet dataset.

python pixel_grouping_test_net.py --config-file configs/PixelGrouping.yaml --num-gpus 8 --num-machines 1 --eval-only \
PIXEL_GROUPING.NUM_SUPERPIXEL_CLUSTERS 4 \
PIXEL_GROUPING.DISTANCE_METRIC "dot" \
PIXEL_GROUPING.BACKBONE_FEATURE_KEY_LIST '["res3","res4"]' \
PIXEL_GROUPING.FEATURE_NORMALIZE False
  • Change settings to explore different configuration.
  • If W&B is setup, set WANDB.DISABLE_WANDB to False and use WANDB.VIS_PERIOD_TEST to visualize the part segments.