title: Denali AI
short_description: VLMs for Garment Attribute Extraction
Denali AI — Vision-Language Models for Garment Classification
Advancing structured attribute extraction from garment images through multi-stage reinforcement learning
Abstract
Denali AI develops and benchmarks vision-language models (VLMs) for structured garment attribute extraction — the task of analyzing a garment image and producing a complete JSON object describing 9 key attributes: type, color, pattern, neckline, sleeve length, closure, brand, size, and defect type.
We systematically evaluate the impact of supervised fine-tuning (SFT), Group Relative Policy Optimization (GRPO), and Group-relative Trajectory-based Policy Optimization (GTPO) across multiple model architectures (Qwen3-VL, Qwen3.5-VL, InternVL3, Florence-2) and scales (0.8B to 122B parameters). Our best model, Qwen3-VL-2B SFT+GRPO v9, achieves 89.5% weighted score with 100% JSON parse rate on the eval_hard_3500 benchmark.
Leaderboard
| Rank | Model | Architecture | Params | Training | Weighted | SBERT+NLI | JSON% | Throughput |
|---|---|---|---|---|---|---|---|---|
| 1 | Qwen3-VL-2B SFT+GRPO v9 | Qwen3-VL | 2B | SFT+GRPO | 89.5% | 78.5% | 100% | 15.9/s |
| 2 | InternVL3-2B GRPO+GTPO Full | InternVL3 | 2B | GRPO+GTPO | 72.7% | 64.3% | 100% | 11.8/s |
| 3 | InternVL3-2B GRPO+GTPO FP8 | InternVL3 | 2B | GRPO+GTPO | 72.2% | 63.8% | 100% | 14.3/s |
| 4 | Qwen3.5-2B SFT+GRPO+GTPO v8 | Qwen3.5-VL | 2B | SFT+GRPO+GTPO | 65.3% | 60.1% | 100% | 11.3/s |
| 5 | Qwen3.5-2B SFT v7 | Qwen3.5-VL | 2B | SFT | 63.7% | 58.9% | 100% | 11.6/s |
| 6 | Qwen3.5-35B GPTQ-Int4 | Qwen3.5 MoE | 35B (3B) | Zero-shot | 50.7% | 48.7% | 14% | 1.6/s |
| 7 | Qwen3.5-9B NVFP4 v10 | Qwen3.5-VL | 9B | Zero-shot | 47.0% | 46.0% | 8% | 1.7/s |
| 8 | Qwen3.5-2B NVFP4 v10 | Qwen3.5-VL | 2B | Zero-shot | 42.9% | 42.9% | 0% | 4.0/s |
Task Definition
Given a single garment image, the model must extract 9 structured attributes as a valid JSON object:
{
"type": "t-shirt",
"color": "navy blue",
"pattern": "solid",
"neckline": "crew neck",
"sleeve_length": "short sleeve",
"closure": "pullover",
"brand": "Nike",
"size": "M",
"defect_type": "small hole on left shoulder"
}
Field Importance Weights
Not all fields are equally important. The weighted score uses domain-specific multipliers:
| Field | Weight | Rationale |
|---|---|---|
| Type | 2.5x | Critical for inventory routing and categorization |
| Defect | 2.0x | Directly impacts quality control and pricing |
| Brand | 1.5x | Essential for authentication and valuation |
| Size | 1.5x | Required for accurate listing and search |
| Color, Pattern, Neckline, Sleeve, Closure | 1.0x | Standard descriptive attributes |
Key Results
Per-Field Performance
Accuracy vs Throughput
Key finding: Qwen3-VL-2B v9 achieves the best accuracy-throughput trade-off at 89.5% weighted score and 15.9 samples/s — making it the Pareto-optimal choice for production deployment.
Structured Output Reliability
Fine-tuned models achieve 100% JSON parse rate, while zero-shot baselines (GPTQ, NVFP4) fail to produce valid JSON in 86-100% of cases. This demonstrates that SFT is essential for teaching structured output format, regardless of model scale.
Impact of Training Stages
Left panel: Adding GRPO+GTPO to Qwen3.5-2B improves brand recognition from 15.6% to 24.8% and defect detection from 89.5% to 95.1%, with a +1.6% overall gain.
Right panel: FP8 quantization of InternVL3-2B shows <1% accuracy degradation across all fields while reducing memory footprint, confirming FP8 as a practical deployment optimization.
Model Collections
By Architecture
| Collection | Models | Description |
|---|---|---|
| Qwen3-VL | 1 | Top-performing Qwen3-VL based models |
| Qwen3.5-VL | 7 | Qwen3.5-VL models (0.8B to 122B) |
| InternVL3 | 5 | InternVL3 models (1B, 2B) |
| Florence-2 | 3 | Florence-2 encoder-decoder models |
| Benchmarks | 2 | Evaluation and training datasets |
Training Pipeline
All fine-tuned models follow the Denali-AI Multi-Stage RL Pipeline:
┌─────────────────────────────────────────────────┐
│ Denali-AI Training Pipeline │
└─────────────────────────────────────────────────┘
│
┌─────────────────────┼─────────────────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────────┐ ┌──────────────┐
│ Stage 1 │ │ Stage 2 │ │ Stage 3 │
│ SFT │───────▶│ GRPO │─────▶│ GTPO │
│ (LoRA) │ │ (Rewards) │ │ (Trajectory) │
└──────────┘ └──────────────┘ └──────────────┘
│ │ │
JSON format Field accuracy Coherence &
acquisition optimization regularization
Stage 1: Supervised Fine-Tuning (SFT)
- Method: LoRA (r=16, alpha=32) on frozen base model
- Data: train-10k-balanced-v3 — 10,000 curated samples
- Objective: Teach valid JSON output format and basic field extraction
- Key outcome: 100% JSON parse rate
Stage 2: Group Relative Policy Optimization (GRPO)
- Method: Reward-based RL without a critic model
- Reward engine: 3-layer scoring system
- Layer 1: JSON validity gate (binary)
- Layer 2: Structural correctness (20% weight)
- Layer 3: Per-field content accuracy (80% weight)
- Key outcome: Improved field-level accuracy, especially for challenging fields
Stage 3: Group-relative Trajectory-based Policy Optimization (GTPO)
- Method: Conflict-aware gradient optimization with entropy regularization
- Key outcome: Trajectory-level coherence and reduced field-level conflicts
Evaluation Methodology
Benchmark
All models are evaluated on eval_hard_3500 — a curated benchmark of 3,500 challenging garment images selected for diversity in:
- Garment type (tops, bottoms, dresses, outerwear, accessories)
- Visual complexity (patterns, prints, multi-color)
- Edge cases (ambiguous attributes, partially visible labels)
Metrics
We employ a comprehensive multi-metric evaluation framework rather than relying on exact match:
| Metric | Model | Description |
|---|---|---|
| SBERT Cosine | all-MiniLM-L6-v2 | Semantic similarity via sentence embeddings |
| NLI Score | nli-MiniLM2-L6-H768 | Natural language inference entailment |
| Levenshtein Ratio | — | Fuzzy string matching distance |
| Token F1 | — | Token-level precision and recall |
| SBERT+NLI Combined | — | Primary metric: average of SBERT cosine and NLI |
| Weighted Score | — | Field-weighted aggregate (see weights above) |
This multi-metric approach captures semantic similarity rather than requiring exact string matches, which is critical for fields like color ("navy blue" vs "dark blue") and defect descriptions.
Evaluation Protocol
- Inference: 8 concurrent workers via OpenAI-compatible API (vLLM)
- Samples: All 3,500 samples, no subsampling
- Compute: NVIDIA RTX PRO 6000 Blackwell (98 GB VRAM)
- Reproducibility: Fixed prompts, deterministic sampling (temperature=0)
Key Findings
Architecture matters more than scale. The 2B Qwen3-VL (89.5%) outperforms the 35B Qwen3.5 MoE (50.7%) by a wide margin, largely due to the zero-shot model's inability to produce valid JSON.
SFT is non-negotiable for structured output. All fine-tuned models achieve 100% JSON parse rate; all zero-shot models fail at 0-14%. No amount of model scale compensates for the lack of format training.
RL provides meaningful but modest gains. GRPO+GTPO adds +1.6% weighted score over SFT-only for Qwen3.5-2B, with the largest gains on brand (+9.2pp) and defect (+5.6pp).
FP8 quantization is effectively free. InternVL3-2B loses <1% accuracy with FP8, while gaining 21% throughput improvement (11.8 vs 14.3 samples/s).
Brand and size are the hardest fields. Even the best model (v9) achieves only 89.3% on brand and 95.8% on size, while defect detection reaches 97.2%.
Research Directions & Future Work
Near-Term Improvements
| Direction | Expected Impact | Effort |
|---|---|---|
| GTPO on Qwen3-VL-2B v9 | +2-4pp weighted (currently SFT+GRPO only) | Low |
| QLoRA on Qwen3.5-35B GPTQ | JSON parse 14% → 100%, weighted 50% → ~80%+ | Low |
| OCR pre-processing pipeline | Fix brand/size for Qwen3.5 models (+30-60pp on those fields) | Medium |
| Higher LoRA rank (r=32/64) | +1-3pp from increased adapter capacity | Low |
| Guided JSON decoding | Force 100% JSON parse on zero-shot models without training | Low |
Architecture Exploration
Models we haven't tested but are strong candidates:
| Model | Parameters | Why Promising |
|---|---|---|
| Qwen3-VL-7B | 7B | Larger Qwen3-VL — our best architecture. Could push past 90% |
| InternVL3-4B | 4B | Mid-range InternVL — may close gap to Qwen3-VL |
| SmolVLM2-2.2B | 2.2B | HuggingFace's efficient VLM — strong structured output |
| PaliGemma2-3B | 3B | Google VLM with excellent OCR — may solve brand/size |
| Phi-4-multimodal | 5.6B | Microsoft's latest — strong structured output |
| MiniCPM-V-2.6 | 2.8B | Strong small VLM with good OCR capabilities |
| Moondream2 | 1.6B | Ultra-compact — fastest possible inference |
Long-Term Research
- Ensemble routing: Use a lightweight classifier to route each field to the best-performing model (e.g., Qwen3-VL for visual attributes, InternVL3 for brand/size)
- Curriculum learning: Progressive difficulty training — easy garments first, hard edge cases last
- Synthetic data generation: Use large VLMs (122B) to generate training labels for unlabeled garment images at scale
- Multi-image input: Leverage front + back + tag images simultaneously for higher accuracy
- Active learning: Identify samples where models disagree most and prioritize annotation of those
Key Open Questions
- Why does Qwen3-VL dramatically outperform Qwen3.5-VL at the same scale? Is it the vision encoder, the cross-attention mechanism, or training data?
- Can RL gains be amplified beyond +1.6pp? Current GRPO/GTPO hyperparameters may be suboptimal
- Is there a parameter count sweet spot between 2B and 7B where accuracy saturates?
- Would instruction-tuned base models (vs base models) yield better SFT starting points?
Datasets
| Dataset | Samples | Purpose | Link |
|---|---|---|---|
| eval_hard_3500 | 3,500 | Evaluation benchmark (hard subset) | Link |
| train_10k_balanced_v3 | 10,000 | Training data (balanced sampling) | Link |
Citation
@misc{denali-ai-2026,
title={Structured Garment Attribute Extraction via Multi-Stage Reinforcement Learning},
author={Denali AI},
year={2026},
publisher={HuggingFace},
url={https://huggingface.co/Denali-AI}
}
License
All models and datasets are released under the Apache 2.0 License.
Contact
- Organization: Denali Advanced Integration
- Issues: GitHub
- HuggingFace: Denali-AI