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Luigi
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Model Details
Model Provider
Luigi
Model Tree
Base
Qwen/Qwen3.5-2B
Fine-tuned
this model
Input Modalities
Text
Image
Video
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Input: a few consecutive frames (oldest→newest). Output: JSON, e.g.
{"posture":"horizontal-on-floor","status":"down","confidence":0.9,"person_down":true,"n_people":1}
where status ∈ {down, distress, normal} (3-class down3 scheme).
edge-fall-vlm-2.2b (SmolVLM2-2.2B) has strong recall on our in-the-wild benchmark, but a
real user-submitted clip (an elderly person falling from a bent/reaching position near a
chair, in a cluttered, low-light room) was missed by every variant of it we tried —
including three separate rounds of synthetic-data improvement (more fall-type coverage,
procedural occlusion, and real photo-textured occlusion + indoor HDRI lighting). None of
those closed the gap. Testing a different base architecture on the exact same clip did:
Qwen3.5-2B, fine-tuned with the same recipe/data as the SmolVLM2 model (no extra
synthetic enrichment), correctly detected the fall — the only model in the investigation
to do so, verified across multiple frame windows spanning the actual fall event.
| Model | Accuracy | Recall (danger) | This specific hard real clip |
|---|---|---|---|
| edge-fall-vlm-2.2b (SmolVLM2) | 0.800 | 0.897 | Missed |
| edge-fall-vlm-qwen3.5-2b (this model) | 0.847 | 0.784 | Detected |
This is a genuine trade-off, not a strict improvement: SmolVLM2-2.2B has meaningfully
better recall on the general in-the-wild population (catches more everyday falls), while
this Qwen3.5-2B model has better overall accuracy/specificity and uniquely handled one
particularly hard case (elderly subject, furniture occlusion, patterned-fabric camouflage,
low light). Chosen here because quality/capability on hard real cases was prioritized
over raw recall-rate optimization for this deployment — if maximizing general recall is
the priority, edge-fall-vlm-2.2b may be the better choice; see that model's card.
Same as edge-fall-vlm-2.2b, for a clean architecture comparison:
down3 label scheme, balanced oversampling.python
from transformers import AutoModelForImageTextToText, AutoProcessorimport torchproc = AutoProcessor.from_pretrained("Luigi/edge-fall-vlm-qwen3.5-2b", do_image_splitting=False,size={"shortest_edge": 64*64, "longest_edge": 384*384})model = AutoModelForImageTextToText.from_pretrained("Luigi/edge-fall-vlm-qwen3.5-2b",dtype=torch.bfloat16).to("cuda").eval()prompt = ("You are a safety monitor. These are consecutive video frames (oldest first), ""possibly with more than one person. Report whether ANYONE has fallen, fainted, ""is lying immobile, or is in distress; else normal. Answer with JSON only.")frames = [...] # list of PIL images, ~6 frames spanning ~3.5smsgs = [{"role":"user","content":[{"type":"image"} for _ in frames]+[{"type":"text","text":prompt}]}]text = proc.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)batch = proc(text=[text], images=[frames], return_tensors="pt").to("cuda")print(proc.batch_decode(model.generate(**batch, max_new_tokens=64, do_sample=False)[:, batch["input_ids"].shape[1]:], skip_special_tokens=True)[0])
Important: always pass enable_thinking=False to apply_chat_template — Qwen's chat
template defaults to injecting a <think> block, which without this flag causes the model
to ramble into open-ended reasoning instead of emitting the trained JSON format directly.
Apache-2.0 (inherits Qwen3.5). Evaluation datasets (URFD, OmniFall/OOPS) are not redistributed; see their sources for terms.