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Fine-tune of MiniCPM-V-4 (4B) for structured extraction from CAD floor plans: walls, doors, windows, stairs, fixtures, and furniture as JSON with bounding boxes normalized to [0, 1000].
Modellquelle
Quellenbeschreibung
Fine-tune of MiniCPM-V-4 (4B) for structured extraction from CAD floor
plans: walls, doors, windows, stairs, fixtures, and furniture as JSON with
bounding boxes normalized to [0, 1000].
{"elements": [{"type": "double_door", "bbox": [623, 730, 710, 763]}, ...]}
Quellen
1 QuelleVerifiziert 9. Sept.
Modellartefakte
1 Artefaktadapter_model.safetensors
safetensors · 1,34 GB · SHA-256 b66e9c80baec…af49 · Hugging Face
HerunterladenQuellenauszüge
2 Auszüge| JSON valid | Precision | Recall | F1 |
|---|
| MiniCPM-V-4 zero-shot | 32% | 0.027 | 0.010 | 0.015 |
| LoRA, LLM-only (300 steps, 800 samples) | 16% | 0.0 | 0.0 | 0.0 |
| LoRA + vision tuning (800 steps, 3.3k samples) | 40% | 0.439 | 0.060 | 0.105 |
The decisive factor was unfreezing the vision tower: LLM-only LoRA learned the output format but stayed image-blind (high-confidence repetition of dataset priors). With vision tuning, precision rose 16x over zero-shot — the model genuinely grounds boxes in the drawing. Recall remains the open weakness (long element lists; outputs sometimes truncate before the JSON closes).
openbmb/MiniCPM-V-4, official MiniCPM-V finetune harness
(llm_type ChatML; note: the harness's qwen2 target-span detection needs a
patch for V-4's tokenizer — spans located by token-id lookup of
'<|im_start|>'/'assistant' only exist in Qwen2's vocab)import torch
from transformers import AutoModel, AutoTokenizer
from peft import PeftModel
base = "openbmb/MiniCPM-V-4"
tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModel.from_pretrained(base, trust_remote_code=True,
torch_dtype=torch.bfloat16, device_map="cuda")
model = PeftModel.from_pretrained(model, "Barath/minicpmv4-floorplan-lora")
model = model.merge_and_unload().eval()
from PIL import Image
img = Image.open("floorplan.png").convert("RGB")
prompt = ('Detect the architectural elements in this floor plan (walls, doors, '
'windows, stairs, fixtures, furniture). Return only JSON: '
'{"elements": [{"type": str, "bbox": [x1, y1, x2, y2]}]} with integer '
'coordinates normalized to [0, 1000].')
out = model.chat(msgs=[{"role": "user", "content": [img, prompt]}],
tokenizer=tokenizer, sampling=False,
max_new_tokens=1500, repetition_penalty=1.1)
print(out)
Trained for the Hugging Face Build Small Hackathon 2026.
--- license: apache-2.0 base_model: openbmb/MiniCPM-V-4 tags: - lora - peft - vision-language - floorplan - object-detection - minicpm-v datasets: - Voxel51/FloorPlanCAD library_name: peft --- # MiniCPM-V-4 Floor-Plan Element Detection (LoRA + vision tuning) Fine-tune of **MiniCPM-V-4 (4B)** for structured extraction from CAD floor plans: walls, doors, windows, stairs, fixtures, and furniture as JSON with bounding boxes normalized to `[0, 1000]`. ```json {"elements": [{"type": "double_door", "bbox": [623, 730, 710, 763]}, ...]} ``` ## Results (held-out FloorPlanCAD, detection F1 @ IoU 0.5, greedy decoding) | | JSON valid | Precision | Recall | F1 | |---|---|---|---|---| | MiniCPM-V-4 zero-shot | 32% | 0.027 | 0.010 | 0.015 | | LoRA, LLM-only (300 steps, 800 samples) | 16% | 0.0 | 0.0 | 0.0 | | **LoRA + vision tuning (800 steps, 3.3k samples)** | **40%** | **0.439** | 0.060 | **0.105** | The decisive factor was **unfreezing the vision tower**: LLM-only LoRA learned the output format but stayed image-blind (high-confidence repetition of dataset priors). With vision tuning, precision rose 16x over zero-shot — the model genuinely grounds boxes in the drawing. Recall remains the open weakness (long element lists; outputs sometimes truncate before the JSON closes). ## Training - Base: `openbmb/MiniCPM-V-4`, official MiniCPM-V finetune harness (`llm_type` ChatML; note: the harness's qwen2 target-span detection needs a patch for V-4's tokenizer — spans located by token-id lookup of `'<|im_start|>'`/`'assistant'` only exist in Qwen2's vocab) - LoRA on LLM attention projections (q/k/v/o) + full vision-tower tuning - 3,281 train / held-out eval from [FloorPlanCAD](https://huggingface.co/datasets/Voxel51/FloorPlanCAD) (FiftyOne detections converted to conversation JSON) - 800 steps, effective batch 8, lr 1e-5 cosine, bf16, single NVIDIA L4 (Modal), ~3.5 h ## Usage ```python import torch from transformers import AutoModel, AutoTokenizer from peft import PeftModel base = "openbmb/MiniCPM-V-4" tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True) model = AutoModel.from_pretrained(base, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda") model = PeftModel.from_pretrained(model, "Barath/minicpmv4-floorplan-lora") model = model.merge_and_unload().eval() from PIL import Image img = Image.open("floorplan.png").co...
Source context: 66 downloads · 2 likes · Pipeline object-detection · Library peft · Repo Barath/minicpmv4-floorplan-lora