datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PlantMetricDepth
PlantMetricDepth
PlantMetricDepth is a multimodal plant dataset designed for metric monocular depth estimation (MDE) and related plant analysis tasks.
The dataset provides paired stereo RGB images, disparity maps, generated metric depth maps, and plant segmentation masks collected across 15 acquisition days.
The metric depth maps provide dense per-pixel depth supervision in centimetres, enabling models to learn metric depth from a single RGB image at inference time without… See the full description on the dataset page: https://huggingface.co/datasets/BashayerAA/PlantMetricDepth.MichaelYitzchak
UAV Fault Symptom Reports
How to read this project
Problem. At the moment of a UAV incident, the operator describes what is happening or enters the live readings;
the system finds the most similar past faults and shows the class guidance recorded for that fault type, withheld
when the evidence is uncertain; each component has a pass mark set before it was scored. App: MichaelYitzchak/uav-similar-incident-workbench.
Step
Notebook
Course part
1… See the full description on the dataset page: https://huggingface.co/datasets/Bashifu/MichaelYitzchak.ShIO-bash-26.1
ShIO-bash-26.1
Shell input-output (ShIO) Bash dataset produced by ShIOEnv, a Gymnasium-compatible Bash environment designed to collect execution-annotated command interactions in a Linux system.
Dataset summary
The dataset consists of command-line inputs paired with their execution artifacts, including observable outputs and a structured representation of environment state changes. Samples are produced by executing synthesized Bash inputs inside a… See the full description on the dataset page: https://huggingface.co/datasets/jragsdale1/ShIO-bash-26.1.english-handwriting-diffusionHumayDrone-Rice-Imagesbashkir-lora-qlora-benchmark
Bashkir LoRA/QLoRA Benchmark
📊 Description
This benchmark contains the complete results of fine-tuning various language models (from 82M to 7B parameters) on the Bashkir language. The study compares the effectiveness of LoRA/QLoRA against full fine-tuning, evaluating model quality (perplexity), GPU memory usage, and training time.
Key Findings
Mistral-7B with QLoRA (r=16) achieved the best performance among 7B models (perplexity 3.79)
LoRA drastically reduces… See the full description on the dataset page: https://huggingface.co/datasets/BashkirNLPWorld/bashkir-lora-qlora-benchmark.bashPlant-Segmentationlego_grey_test1bash_images_2
Dataset Card for "bash_images_2"
More Information needed
images