LW-DETR, a Light-Weight DEtection TRansformer model, is designed to be a real-time object detection alternative that outperforms conventional convolutional (YOLO-style) and earlier transformer-based (DETR) methods in terms of speed and accuracy trade-off. It was introduced in the paper LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection by Chen et al. and first released in this repository. Disclaimer: This model was originally contributed by stevenbucaille in 🤗 transformers. LW-DETR is an end-to-end object detection model that uses a Vision Transformer (ViT) backbone as its encoder, a simple convolutional projector, and a shallow DETR decoder. The core philosophy is to leverage the power of transformers while implementing several efficiency-focused techniques to achieve real-time performance. Key Architectural Details: - ViT Encoder: Uses a plain ViT architecture. To reduce the quadratic complexity of global self-attention, it adopts interleaved window and global attentions. - Window-Major Organization: It employs a highly efficient window-major feature map organization scheme for attention computation, which drastically reduces the costly memory permutation operations required when transitioning between global and window attention modes, leading to lower inference latency. - Feature Aggregation: It aggregates features from multiple levels (intermediate and final layers) of the ViT encoder to create richer input for the decoder. - Projector: A C2f block (from YOLOv8) connects the encoder and decoder. For larger versions (large/xlarge), it outputs two-scale features ($1/8$ and $1/32$) to the decoder. - Shallow DETR Decoder: It uses a computationally efficient 3-layer transformer decoder (instead of the standard 6 layers), incorporating deformable cross-attention for faster convergence and lower latency. - Object Queries: It uses a mixed-query selection scheme to form the object queries from both learnable content queries and generated spatial queries (based on top-K features from the Projector).

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doclayout-yolo-docstructbench

anyformat

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Anzhcs_YOLOs

Anzhc

YOLOs in this repo are trained with datasets that i have annotated myself, or with the help of my friends(They will be appropriately mentioned in those cases). YOLOs on open datasets will have their own pages. ~~Ultralytics 8.3.217 updates mask handling, which breaks function in main Adetailer repo. Install Ultralytics==8.3.216 or lower. Alternatively - use forks that fix this.~~ - Fixed in main repo. I've added some features to make Adetailer more usable and less manual - https://github.com/Anzhc/aadetailer-reforge > ## **Table of Contents** > - **Face segmentation** > - *Universal* > - *Real Face, gendered* > - **Eyes segmentation** > - **Head+Hair segmentation** > - **Breasts** > - *Breasts Segmentation* > - *Breast size detection/classification* > - **Drone detection** > - **Anime Art Scoring** > - **Support**

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detr-resnet-50-federated-3-clients-3-datasets

ARG-NCTU

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detr-resnet-50-finetuned-federated-600-epochs-Taichung_Port_dataset

ARG-NCTU

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yolo11-document-layout

Armaggheddon

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yolo26-document-layout

Armaggheddon

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yolov8n_handwritten_text_detection

armvectores

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YOLOv8-nano-aadhar-card

arnabdhar

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deformable-detr-DocLayNet

Aryn

Deformable DEtection TRansformer (DETR), trained on DocLayNet (including 80k annotated pages in 11 classes). The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.

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