| --- |
| title: Denali AI |
| short_description: VLMs for Garment Attribute Extraction |
| --- |
| |
| # Denali AI — Vision-Language Models for Garment Classification |
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| <div align="center"> |
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| **Advancing structured attribute extraction from garment images through multi-stage reinforcement learning** |
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| [](https://huggingface.co/Denali-AI) |
| [](https://huggingface.co/datasets/Denali-AI/eval-hard-3500) |
| [](https://www.apache.org/licenses/LICENSE-2.0) |
| [](https://huggingface.co/Denali-AI/qwen3-vl-2b-sft-grpo-v9) |
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| </div> |
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| --- |
|
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| ## Abstract |
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| 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. |
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| 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. |
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| --- |
|
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| ## Leaderboard |
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| | 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 | |
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| --- |
|
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| ## Task Definition |
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| Given a single garment image, the model must extract **9 structured attributes** as a valid JSON object: |
|
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| ```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" |
| } |
| ``` |
|
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| ### Field Importance Weights |
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| Not all fields are equally important. The weighted score uses domain-specific multipliers: |
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|  |
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| | 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 | |
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| --- |
|
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| ## Key Results |
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| ### Per-Field Performance |
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| ### Accuracy vs Throughput |
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| **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. |
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| ### Structured Output Reliability |
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|  |
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| 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. |
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| ### Impact of Training Stages |
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|  |
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| **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. |
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| **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. |
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| --- |
|
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| ## Model Collections |
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| ### By Architecture |
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| | 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 | |
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| --- |
|
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| ## Training Pipeline |
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| All fine-tuned models follow the **Denali-AI Multi-Stage RL Pipeline**: |
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| ``` |
| ┌─────────────────────────────────────────────────┐ |
| │ Denali-AI Training Pipeline │ |
| └─────────────────────────────────────────────────┘ |
| │ |
| ┌─────────────────────┼─────────────────────┐ |
| ▼ ▼ ▼ |
| ┌──────────┐ ┌──────────────┐ ┌──────────────┐ |
| │ Stage 1 │ │ Stage 2 │ │ Stage 3 │ |
| │ SFT │───────▶│ GRPO │─────▶│ GTPO │ |
| │ (LoRA) │ │ (Rewards) │ │ (Trajectory) │ |
| └──────────┘ └──────────────┘ └──────────────┘ |
| │ │ │ |
| JSON format Field accuracy Coherence & |
| acquisition optimization regularization |
| ``` |
|
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| ### Stage 1: Supervised Fine-Tuning (SFT) |
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| - **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 |
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| ### Stage 2: Group Relative Policy Optimization (GRPO) |
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| - **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 |
|
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| ### Stage 3: Group-relative Trajectory-based Policy Optimization (GTPO) |
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| - **Method:** Conflict-aware gradient optimization with entropy regularization |
| - **Key outcome:** Trajectory-level coherence and reduced field-level conflicts |
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| --- |
|
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| ## Evaluation Methodology |
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| ### Benchmark |
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| 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) |
|
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| ### Metrics |
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| We employ a **comprehensive multi-metric evaluation framework** rather than relying on exact match: |
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| | 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) | |
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| 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. |
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| ### Evaluation Protocol |
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| - **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) |
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| --- |
|
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| ## Key Findings |
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| 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. |
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| 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. |
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| 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). |
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| 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). |
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| 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%. |
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| --- |
|
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| ## Research Directions & Future Work |
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| ### Near-Term Improvements |
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| | 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 | |
|
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| ### Architecture Exploration |
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| Models we haven't tested but are strong candidates: |
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| | 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 | |
|
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| ### Long-Term Research |
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| 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 |
|
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| ### Key Open Questions |
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| - 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? |
|
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| --- |
|
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| ## Datasets |
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| | 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) |
| |
