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아카이브 평가 35건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
EBC-ZIP-B	MAE 47.81 RMSE 75.04	EBC-ZIP: Improving Blockwise Crowd Counting with Zero-Inflated Poisson Regression 2025-06-24	코드 1
APGCC	MAE 48.8 MSE 76.7	Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance 2024-05-17	코드 1
PSL-Net	MAE 49.9 RMSE 77.6	Crowd Counting and Individual Localization Using Pseudo Square Label 2024-05-13	연결 없음
FGENet	MAE 51.66 MSE 85.00	FGENet: Fine-Grained Extraction Network for Congested Crowd Counting 2024-01-02	연결 없음
VMambaCC	MAE 51.87 MSE 81.3	VMambaCC: A Visual State Space Model for Crowd Counting 2024-05-07	연결 없음
CLIP-EBC (ViT-B/16)	MAE 52.5 RMSE 85.9	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification 2024-03-14	코드 1
P2PNet	MAE 52.74 MSE 85.06	Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework 2021-07-27	코드 1 · 코드 2 · 코드 3
CLIP-EBC (ResNet50)	MAE 54.0 RMSE 83.2	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification 2024-03-14	코드 1
GauNet (ResNet-50)	MAE 54.8 MSE 89.1	Rethinking Spatial Invariance of Convolutional Networks for Object Counting 2022-06-10	코드 1
LoViTCrowd	MAE 54.8 RMSE 80.9	Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd Counting 2022-09-30	코드 1
M-SFANet+M-SegNet	MAE 57.55 MSE 94.48	Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting 2020-03-12	코드 1 · 코드 2
SGANet + CL	MAE 57.6	Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss 2019-11-18	코드 1
SGANet	MAE 58	Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss 2019-11-18	코드 1
S-DCNet	MAE 58.3	From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer 2019-08-15	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
SPANet	MAE 59.4 MSE 92.5	Learning Spatial Awareness to Improve Crowd Counting 2019-09-16	연결 없음
DM-Count	MAE 59.7	Distribution Matching for Crowd Counting 2020-09-28	코드 1
FusionCount	MAE 62.2 MSE 101.2	FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion 2022-02-28	코드 1
DMCount-EBC	MAE 62.3 RMSE 98.9	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification 2024-03-14	코드 1
CAN	MAE 62.3	Context-Aware Crowd Counting 2018-11-26	코드 1 · 코드 2 · 코드 3
OrdinalEntropy	MAE 65.6 MSE 105.0	Improving Deep Regression with Ordinal Entropy 2023-01-21	코드 1
CSRNet-EBC	MAE 66.3 RMSE 105.0	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification 2024-03-14	코드 1
LSC-CNN	MAE 66.4	Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection 2019-06-18	코드 1 · 코드 2
SANet	MAE 67.0	Scale Aggregation Network for Accurate and Efficient Crowd Counting 2018-09-01	코드 1
CSRNet	MAE 68.2	CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes 2018-02-27	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11
ic-CNN	MAE 68.5	Iterative Crowd Counting 2018-07-26	연결 없음
IG-CNN	MAE 72.5	Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN 2018-07-26	연결 없음
D-ConvNet	MAE 73.5	Crowd Counting With Deep Negative Correlation Learning 2018-06-01	코드 1
CP-CNN	MAE 73.6	Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs 2017-08-02	연결 없음
Liu et al.	MAE 73.6	Leveraging Unlabeled Data for Crowd Counting by Learning to Rank 2018-03-08	코드 1
SAFECount	MAE 73.70	Few-shot Object Counting with Similarity-Aware Feature Enhancement 2022-01-22	코드 1