Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points. - Code World

Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points.

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Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points.

Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points.

YoloV8 rising point artifact: CFPNet-ECVBlock's small target detection, plug and play, helping to detect rising points

CFPNet based on YoloV5---ECVBlock's small target detection, plug and play, helping to detect rising points

InceptionNext: When Inception meets ConvNeXt

Yolov5 innovation: NEU-DET steel surface defect detection, the optimized combination is highly novel, CVPR2023 DCNV3 and InceptionNeXt have obvious growth points

NEU-DET steel surface defect detection based on Yolov8, the optimized combination is highly novel: CVPR2023 PConv and BiLevelRoutingAttention, with obvious growth points

YoloV8 improvement strategy: NWD new paradigm of small target detection, helping YoloV5 and V8 to violently increase their points on small targets

YOLOV5/YOLOV8 improvement: CVPR23: join the ConvNeXt V2 backbone, effectively increase points

Video target detection yolo algorithm small model self-training model comparison (yolov5 yolov7 yolov8)

[CVPR2023] Mapping degradation meets label evolution: Learning infrared small target detection with single-point supervision

Gain point artifact: small target performance improvement based on Yolov5/Yolov7

[YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement NO.65] CVPR 2023 | Tsinghua team's plug-and-play network architecture—Slide-Transformer

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.69] Improvement of small targets in remote sensing image target detection CATnet (ContextAggregation module)

YOLOv5, v7 improvement twenty-eight: ICLR 2022 rising point artifact - plug-and-play dynamic convolution ODConv

YOLOv5 Improvement Series (20) - Add BiFormer Attention Mechanism (CVPR2023|Small Goal Increase Point Artifact)

Улучшение YOLOV8: CVPR 2023 | SCConv: свертка пространственной и канальной реконструкции Plug-and-Play

Improving YOLOv5/YOLOv8: Reproduction combined with plug-and-play | Efficient multi-scale attention (EMA), the module becomes a small helper for YOLOv5 improvement

CVPR2023 Target Detection Paper Collection

YOLOV5/YOLOV7/YOLOV8 Verbesserung: InceptionNeXt: Wenn Inception auf ConvNeXt trifft

100 practical application cases of target detection YOLO - small target detection based on improved YOLOv8 (continued)

Raising point artifact: SPD-Conv based on Yolov8, low-resolution images and small objects have obvious rising points

Raising point artifact: SPD-Conv based on Yolov8, low-resolution images and small objects have obvious rising points

The first release of Yolov5/Yolov7 point increase artifact: Huawei Noah 2023's minimalist neural network model VanillaNet---VanillaBlock helps detection and achieves violent point increase

Attention mechanism: based on Yolov8's Triplet attention module, plug and play, the effect is better than cbam and se, and the increase point is obvious

When YoloV5 Meets Raccoon: Use Yolov5 (v4.0) to make a small raccoon catching apk

Yolov8 target detection

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.80] introduce plug-and-play CloFormer: the perfect integration of attention mechanism and convolution

Quickly play Yolov5 target detection - you can play without a good graphics card (2)

Quickly play with Yolov5 target detection - you can play without a good graphics card (1)

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