{% extends "base.html" %} {% block title %}{{ project.name }} — {{ app_name }}{% endblock %} {% block content %}
{{ project.description or project.project_type.value | replace('_', ' ') | title }}
{% if project.location %}Location: {{ project.location }}
{{ card.body }}
{% if card.href %} {% else %}{% if chrome.quality_profile == 'image' %}Configure checks, then preview or apply cleaning before generating a version or training.{% else %}Check label readiness, then clean empty / duplicate samples before packaging or training.{% endif %}
Config is saved in this browser and reused when starting training with the readiness gate on.
Click “Run checks” to score this project.
Remediations run before versioning / packaging / training. Preview is non-destructive; Apply may delete rows.
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Suggests images or samples to annotate next. Embeddings-based diversity is not implemented yet.
{{ chrome.classes_hint or 'Define label classes for this task.' }}
Configure splits, preprocessing, and augmentations for detection / segmentation training.
Applied once to every image. Click a card to toggle.
{% if project.project_type.value == 'rag_eval' %} Package query / contexts / answer JSONL for RAGAS, DeepEval, or a custom RAG eval harness. There is no model fine-tune in this flow. {% elif modality == 'image' %} Train YOLO, Face (MTCNN / RetinaFace-style), or Torchvision detectors locally. {% elif modality == 'tabular' %} Train sklearn classifiers / regressors on labeled rows. {% elif modality == 'text' %} Train classical NLP or prepare Hugging Face packages. {% elif modality == 'audio' %} Train audio-label models or prepare Whisper / ASR fine-tune JSONL. {% elif modality == 'video' %} Train video-label models or prepare action-recognition packages. {% elif modality == 'llm' %} Package instruction or preference data for LLM fine-tuning (TRL, Axolotl…). {% else %} Choose a training backend for this workspace. {% endif %}
Members, image assignments, and the review queue.
Training runs are recorded as experiments. Select two or more to compare metrics side-by-side.
Build workflows as a visual DAG or YAML. The runner executes validation, cleaning, splits, export, training, and active learning; other step types are skipped for now.
No run yet.
Register trained YOLO weights, then use them in Label Assist to auto-annotate.