OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection
Paper โข 2505.19889 โข Published โข 1
How to use MoritzM00/qwen3-vl-8b-lora-r16-sft-omnifall-all with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "MoritzM00/qwen3-vl-8b-lora-r16-sft-omnifall-all")Rank-16 SFT LoRA adapter for Qwen/Qwen3-VL-8B-Instruct, fine-tuned jointly on the OmniFall staged, synthetic, and in-the-wild training partitions. It predicts one of 16 human-activity labels from a video clip.
Place the video first in the user message, then append the following text. No system prompt is required.
Role:
You are an expert Human Activity Recognition (HAR) specialist.
Task:
Analyze the video clip and classify the primary action being performed.
Assign exactly one label from the allowed list below.
Note that the clip may contain more than one action. If this is the case,
focus on classifying the action in the first part of the clip, not the entire clip.
Example: Clips shows a person jumping and then falling. The correct label is jump.
Allowed Labels:
- walk
- fall
- fallen
- sit_down
- sitting
- lie_down
- lying
- stand_up
- standing
- other
- kneel_down
- kneeling
- squat_down
- squatting
- crawl
- jump
Output Format:
Respond with 'The best answer is: <class_label>' where <class_label> is one of the allowed labels.
Base model
Qwen/Qwen3-VL-8B-Instruct