ship
open-tinybert-indian-address-nerpranay27sy_-_shipping-class-tag-prediction-Llama-3.2-3B-Instruct-v1.0-ggufpranay27sy_-_shipping-class-tag-prediction-Llama-3.2-3B-Instruct-v2.0-ggufpranay27sy_-_shipping-class-tag-prediction-Llama-3.1-8B-Instruct-v3.0-ggufpranay27sy_-_shipping-class-tag-prediction-Llama-3.1-8B-Instruct-v1.0-ggufpranay27sy_-_shipping-class-tag-prediction-Llama-3.1-8B-Instruct-v2.0-ggufvessel_ship_types_image_detectionGemma3-270m-LOMO-Shipbuilding-Marine-GGUF
Datasets
All datasets matching “ship”ship-tracking-dataLevir_ship_trainingnorthwind_Shipping_orders
Northwind Shipping Orders and Related Documents
This dataset contains a collection of Shipping Orders and related documents from the Northwind database, a sample database used by Microsoft for demonstrating database functionalities.
The Shipping Orders include information about the ship name, Address , Region, postal code ,country, customer ,employee shipped date product names, quantities, unit prices, and total prices. The related documents include shipping documents and stock… See the full description on the dataset page: https://huggingface.co/datasets/AyoubChLin/northwind_Shipping_orders.AdvBench-omni
AdvBench-Omni
Paper | Code
AdvBench-Omni is a dataset constructed to evaluate and reveal safety vulnerabilities in Omni-modal Large Language Models (OLLMs). It is based on a modality-semantics decoupling principle to study how OLLMs handle cross-modal safety risks and conflicts.
Introduction
Omni-modal Large Language Models (OLLMs) expand multimodal capabilities but introduce new cross-modal safety risks. AdvBench-Omni reveals a significant vulnerability in these… See the full description on the dataset page: https://huggingface.co/datasets/shipnebula/AdvBench-omni.godot_rl_ShipsA RL environment called Ships for the Godot Game Engine.
This environment was created with: https://github.com/edbeeching/godot_rl_agents
Downloading the environment
After installing Godot RL Agents, download the environment with:
gdrl.env_from_hub -r edbeeching/godot_rl_Ships
DOTA-ShipBench
Dataset layout (bundled with this repository)
dataset/
├── train/images/ # SAHI-tiled training images (1024×1024)
├── train/labels/ # YOLO-OBB labels
├── train/labelTxt/ # DOTA format (MMRotate)
├── val/images/
├── val/labels/
├── val/labelTxt/
├── dataset.yaml # Ultralytics config (path: .)
├── gt_coco_filtered.json # COCO-OBB GT for unified eval
├── hrsc2016_source_test_gt.json
├── dota_test/ # DOTA 2.0 ships-only test images… See the full description on the dataset page: https://huggingface.co/datasets/usmansaani145/DOTA-ShipBench.