{"url":"/method/cornernet-squeeze-hourglass-module","slug":"cornernet-squeeze-hourglass-module","name":"CornerNet-Squeeze Hourglass Module","full_name":"CornerNet-Squeeze Hourglass Module","full_name_withheld":false,"description_markdown":"**CornerNet-Squeeze Hourglass Module** is an image model block used in [CornerNet](https://paperswithcode.com/method/cornernet)-Lite that is based on an [hourglass module](https://paperswithcode.com/method/hourglass-module), but uses modified fire modules instead of residual blocks. Other than replacing the residual blocks, further modifications include: reducing the maximum feature map resolution of the hourglass modules by adding one more downsampling layer before the hourglass modules, removing one downsampling layer in each hourglass module, replacing the 3 × 3 filters with 1 x 1 filters in the prediction modules of CornerNet, and finally replacing the nearest neighbor upsampling in the hourglass network with transpose [convolution](https://paperswithcode.com/method/convolution) with a 4 × 4 kernel.","description_state":"present","introduced_year":null,"introduced_by":{"title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","paper":"/paper/190408900","first_author":"Hei Law","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/190408900"},"source":{"url":"https://arxiv.org/abs/1904.08900v2","title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/princeton-vl/CornerNet-Lite/blob/6a54505d830a9d6afe26e99f0864b5d06d0bbbaf/core/models/CornerNet_Squeeze.py#L10","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Model Blocks","url":"/methods/category/image-model-blocks","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/190408900","title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","date":"2019-04-18","arxiv_id":"1904.08900","n_code_links":6,"syntology":{"ran":1,"of":27,"unverified":26,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/real-time-object-detection","name":"Real-Time Object Detection","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2019","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/cornernet-squeeze-hourglass-module"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}