---
title: Denali AI
short_description: VLMs for Garment Attribute Extraction
---

# Denali AI — Vision-Language Models for Garment Classification

<div align="center">

**Advancing structured attribute extraction from garment images through multi-stage reinforcement learning**

[![Models](https://img.shields.io/badge/Models-16-blue)](https://huggingface.co/Denali-AI)
[![Benchmark](https://img.shields.io/badge/Benchmark-3%2C500_samples-green)](https://huggingface.co/datasets/Denali-AI/eval-hard-3500)
[![License](https://img.shields.io/badge/License-Apache_2.0-orange)](https://www.apache.org/licenses/LICENSE-2.0)
[![Best Score](https://img.shields.io/badge/Best_Weighted_Score-89.5%25-brightgreen)](https://huggingface.co/Denali-AI/qwen3-vl-2b-sft-grpo-v9)

</div>

---

## 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

![Model Leaderboard](https://huggingface.co/Denali-AI/org-assets/resolve/main/leaderboard.png)

| Rank | Model | Architecture | Params | Training | Weighted | SBERT+NLI | JSON% | Throughput |
|:----:|-------|-------------|:------:|----------|:--------:|:---------:|:-----:|:----------:|
| 1 | **[Qwen3-VL-2B SFT+GRPO v9](https://huggingface.co/Denali-AI/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](https://huggingface.co/Denali-AI/internvl3-2b-grpo-gtpo-full) | InternVL3 | 2B | GRPO+GTPO | **72.7%** | 64.3% | 100% | 11.8/s |
| 3 | [InternVL3-2B GRPO+GTPO FP8](https://huggingface.co/Denali-AI/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](https://huggingface.co/Denali-AI/qwen35-2b-sft-grpo-gtpo-merged) | Qwen3.5-VL | 2B | SFT+GRPO+GTPO | **65.3%** | 60.1% | 100% | 11.3/s |
| 5 | [Qwen3.5-2B SFT v7](https://huggingface.co/Denali-AI/qwen35-2b-sft-merged) | Qwen3.5-VL | 2B | SFT | **63.7%** | 58.9% | 100% | 11.6/s |
| 6 | [Qwen3.5-35B GPTQ-Int4](https://huggingface.co/Denali-AI/qwen35-35b-a3b-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:

```json
{
  "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 Weights](https://huggingface.co/Denali-AI/org-assets/resolve/main/field_weights.png)

| 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

![Radar Comparison](https://huggingface.co/Denali-AI/org-assets/resolve/main/radar_comparison.png)

![Performance Heatmap](https://huggingface.co/Denali-AI/org-assets/resolve/main/heatmap.png)

### Accuracy vs Throughput

![Throughput Analysis](https://huggingface.co/Denali-AI/org-assets/resolve/main/throughput_scatter.png)

**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

![JSON Parse Rates](https://huggingface.co/Denali-AI/org-assets/resolve/main/json_parse.png)

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

![Training Impact](https://huggingface.co/Denali-AI/org-assets/resolve/main/training_impact.png)

**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**](https://huggingface.co/collections/Denali-AI/qwen3-vl-models-69c70950fca01f437228c29b) | 1 | Top-performing Qwen3-VL based models |
| [**Qwen3.5-VL**](https://huggingface.co/collections/Denali-AI/qwen35-vl-models-69c70802ab21ae73a116cc92) | 7 | Qwen3.5-VL models (0.8B to 122B) |
| [**InternVL3**](https://huggingface.co/collections/Denali-AI/internvl3-models-69c70803ab21ae73a116cca2) | 5 | InternVL3 models (1B, 2B) |
| [**Florence-2**](https://huggingface.co/collections/Denali-AI/florence-2-models-69c70802f1456fd2264216e8) | 3 | Florence-2 encoder-decoder models |
| [**Benchmarks**](https://huggingface.co/collections/Denali-AI/benchmarks-and-datasets-69c708037d77aba79963c1a7) | 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](https://huggingface.co/datasets/Denali-AI/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**](https://huggingface.co/datasets/Denali-AI/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

1. **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.

2. **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.

3. **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).

4. **FP8 quantization is effectively free.** InternVL3-2B loses <1% accuracy with FP8, while gaining 21% throughput improvement (11.8 vs 14.3 samples/s).

5. **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](https://huggingface.co/Qwen/Qwen3-VL-7B)** | 7B | Larger Qwen3-VL — our best architecture. Could push past 90% |
| **[InternVL3-4B](https://huggingface.co/OpenGVLab/InternVL3-4B)** | 4B | Mid-range InternVL — may close gap to Qwen3-VL |
| **[SmolVLM2-2.2B](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct)** | 2.2B | HuggingFace's efficient VLM — strong structured output |
| **[PaliGemma2-3B](https://huggingface.co/google/paligemma2-3b-pt-448)** | 3B | Google VLM with excellent OCR — may solve brand/size |
| **[Phi-4-multimodal](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)** | 5.6B | Microsoft's latest — strong structured output |
| **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** | 2.8B | Strong small VLM with good OCR capabilities |
| **[Moondream2](https://huggingface.co/vikhyatk/moondream2)** | 1.6B | Ultra-compact — fastest possible inference |

### Long-Term Research

1. **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)
2. **Curriculum learning:** Progressive difficulty training — easy garments first, hard edge cases last
3. **Synthetic data generation:** Use large VLMs (122B) to generate training labels for unlabeled garment images at scale
4. **Multi-image input:** Leverage front + back + tag images simultaneously for higher accuracy
5. **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](https://huggingface.co/datasets/Denali-AI/eval-hard-3500) |
| **train_10k_balanced_v3** | 10,000 | Training data (balanced sampling) | [Link](https://huggingface.co/datasets/Denali-AI/train-10k-balanced-v3) |

---

## Citation

```bibtex
@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](https://www.apache.org/licenses/LICENSE-2.0).

## Contact

- **Organization:** [Denali Advanced Integration](https://denaliai.com)
- **Issues:** [GitHub](https://github.com/Denali-AI)
- **HuggingFace:** [Denali-AI](https://huggingface.co/Denali-AI)