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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

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