yolo_box

Model · Object detection

locate-anything.cpp-gguf

Mudler

GGUF builds of nvidia/LocateAnything-3B for locate-anything.cpp - a C++/ggml inference engine for open-vocabulary detection / visual grounding, no Python at inference time. Brought to you by the LocalAI team. The detections are the same as the official PyTorch implementation (the engine is parity-gated against it), and it runs faster - on CPU and GPU. The full-precision f32 GGUF (~15 GB) is reproducible from the HF weights with scripts/convertlocateanythingtogguf.py in the repo. Same detections as the official model, faster. Full methodology, the warm/median setup, parity checks, and more images are in the repo's Slow-mode inference on the 448 fixture; vs official divides the official…

Model · Object detection

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 Im open to commissions, hit me up in Discord - anzhc P.S. All model names in tables have download links attached:3 Series of models aiming at detecting and segmenting face…

Model · Object detection

yolov11-license-plate-detection

Nuttapong Chimwai

This is a fine-tuned version of YOLOv11 (n, s, m, l, x) specialized for License Plate Detection, using a public dataset from Roboflow Universe: The upstream Roboflow dataset (license-plate-recognition-rxg4e) contains train/test contamination — the same source images appear in both the training and test splits with only minor manual augmentation applied (see Discussion #2 for concrete examples). As a result: - The reported metrics below are likely overestimated, because the test set is not a true held-out evaluation. - Real-world generalization performance is expected to be lower than the numbers in the table. - Treat all evaluation figures with caution and validate the model on your own…

Model · Object detection

surya_layout2

Datalab

A lightweight document layout detection model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU. This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model. Documentation, installation, and everything else lives in the Point the fast layout predictor at this checkpoint: Or make it the default so the CLI and library use it without an explicit path: Released under the AI Pubs OpenRAIL-M license (see LICENSE) — the same license as the surya-ocr-2 model weights.

Model · Object detection

detr-resnet-50

Joshua

https://huggingface.co/facebook/detr-resnet-50 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Test it out here, or create your own object-detection demo with 1 click! Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).