Datasets:
license: apache-2.0
language:
- en
task_categories:
- visual-question-answering
Contact Frame SFT Dataset
This dataset is used to improve a VLM's accuracy for the EgoLoc pipeline.
This dataset IS NOT shuffled. When undergoing SFT, recommend shuffling the dataset.
3x3 grid dataset for VLM fine-tuning on contact frame identification.
- Training videos: 176
- Eval videos: 20 (10 DeskTIL + 10 EgoPAT3D)
- Grid variants: 9 per video (GT frame at each cell position)
- Training rows: 1389
- Eval rows: 146
- Skipped training rows (edge cases): 195
- Skipped eval rows (edge cases): 34
- Grid size: 3x3
- Action: Grasping the object
train/metadata.jsonl is training-only. validation/metadata.jsonl is the held-out eval subset. This layout is compatible with Hugging Face ImageFolder split detection. Each row contains a grid image, a prompt, and a target response with the cell number (1-9) of the contact frame.
Each row contains a grid image, a prompt, and a target response with the cell number (1-9) of the contact frame.
Why skipped (edge cases)?
When the script generates 9 grid variants per video, it slides a window of 9 consecutive frames so the GT frame lands at each of the 9 positions. But some offsets are impossible if the GT frame is too close to the start or end of the video.