HOI Planner and Critic training data
Private research dataset for the HOI Planner and Critic experiments. UTF-8 JSONL files have one JSON object per line. Code and training scripts: esther585/hoi-planner-critic.
Planner
hoi_v1/train.jsonl: 3,852 training instructions.hoi_v1/complex.jsonl: 768 added complex-action prompts, included in training data.hoi_v1/planner.jsonl: 3,852 GPT teacher plans withgeneration_promptandinteraction_graph.hoi_v1/planner_sft.jsonl: 3,852 ready-to-train SFT rows with system/user/assistant messages. Use this for Planner training; it requires no images.hoi_v1/test.jsonlandhoi_v1/labels.jsonl: 192 held-out evaluation prompts and annotations, split evenly between simple and complex tasks. Do not train on these.
Critic
critic/train.jsonl,critic/val.jsonl,critic/test.jsonl: 3,376 / 164 / 139 image-conditioned SFT rows. Train on train and validate on val.critic/train_unique.jsonl: 2,779-row unbalanced alternative to the decision-balanced main train split.critic/system.txt: exact system prompt used for the included Critic LoRA.critic/images/: all 3,082 unique generated PNGs referenced by the JSONL rows. Eachimagespath is relative to thecritic/directory.critic/missing_images.tar: redundant backup for one PNG that a previous ModelScope upload did not expose as a standalone file. This Hugging Face copy also includes the standalone PNG.
The Critic rows contain messages, images, and metadata. The assistant response selects accept, edit, or regenerate and gives errors, a repair route, and semantic/visual scores. The balanced train split contains 1,688 accept, 844 edit, and 844 regenerate examples. These rows were derived from 3,104 image-generation trajectories and filtered by trajectory quality.
The Critic adapter was trained on the Critic training split. To use the JSONLs with ms-swift, resolve relative image paths to absolute paths after download; the linked code repository provides code/paths.py and code/train_critic.sh for this. No public redistribution license is granted.
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