{"url":"/method/mlfpn","slug":"mlfpn","name":"MLFPN","full_name":"MLFPN","full_name_withheld":false,"description_markdown":"**Multi-Level Feature Pyramid Network**, or **MLFPN**, is a feature pyramid block used in object detection models, notably [M2Det](https://paperswithcode.com/method/m2det). We first fuse multi-level features (i.e. multiple layers) extracted by a backbone as a base feature, and then feed it into a block of alternating joint Thinned U-shape Modules ([TUM](https://paperswithcode.com/method/tum)) and Feature Fusion Modules (FFM) to extract more representative, multi-level multi-scale features. Finally, we gather up the feature maps with equivalent scales to construct the final feature pyramid for object detection. Decoder layers that form the final feature pyramid are much deeper than the layers in the backbone, namely, they are more representative. Moreover, each feature map in the final feature pyramid consists of the decoder layers from multiple levels. Hence, the feature pyramid block is called Multi-Level Feature Pyramid Network (MLFPN).","description_state":"present","introduced_year":null,"introduced_by":{"title":"M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network","paper":"/paper/m2det-a-single-shot-object-detector-based-on","first_author":"Qijie Zhao","n_authors":7,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/m2det-a-single-shot-object-detector-based-on"},"source":{"url":"http://arxiv.org/abs/1811.04533v3","title":"M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/qijiezhao/M2Det/blob/ade4f3d12979800c367bf1e46d2e316e73a87514/m2det.py","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Feature Pyramid Blocks","url":"/methods/category/feature-pyramid-blocks","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Feature Extractors","url":"/methods/category/feature-extractors","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/towards-robust-visual-information-extraction","title":"Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution","date":"2021-01-24","arxiv_id":"2102.06732","n_code_links":1,"syntology":null},{"paper":"/paper/m2det-a-single-shot-object-detector-based-on","title":"M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network","date":"2018-11-12","arxiv_id":"1811.04533","n_code_links":11,"syntology":{"ran":1,"of":1,"unverified":0,"pointer_only":0}}],"papers_shown":2,"tasks":[{"task":"/task/3d-feature-matching","name":"3D Feature Matching","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/text-detection","name":"Text Detection","papers":1},{"task":"/task/text-spotting","name":"Text Spotting","papers":1},{"task":"/task/document-understanding","name":"document understanding","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2018","papers":1},{"year":"2021","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/mlfpn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}