---
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**
[](https://huggingface.co/Denali-AI)
[](https://huggingface.co/datasets/Denali-AI/eval-hard-3500)
[](https://www.apache.org/licenses/LICENSE-2.0)
[](http://10.201.28.30:8069)
[](https://huggingface.co/Denali-AI/granite4-vision-garment-classifier)
---
## 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-8B SFT+GRPO**, achieves **91.3% 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 | **[Granite4-Vision-3B SFT](https://huggingface.co/Denali-AI/granite4-vision-garment-classifier)** | Granite4-Vision | 4.5B | SFT | **101.4%** | 87.5% | 100% | — |
| 2 | [Qwen3-VL-8B SFT+GRPO](https://huggingface.co/Denali-AI/qwen3-vl-8b-garment-classifier) | Qwen3-VL | 8B | SFT+GRPO | **91.3%** | 78.7% | 100% | — |
| 3 | [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% | — |
| 4 | [Qwen3-VL-8B SFT+GRPO NVFP4](https://huggingface.co/Denali-AI/qwen3-vl-8b-garment-classifier-nvfp4) | Qwen3-VL | 8B | SFT+GRPO+NVFP4 | **89.5%** | 77.0% | 100% | — |
| 5 | [Qwen3-VL-8B Instruct (Base)](https://huggingface.co/Denali-AI/qwen3-vl-8b-instruct-base) | Qwen3-VL | 8B | Zero-shot | **87.5%** | 75.6% | 100% | — |
| 6 | [Qwen3-VL-8B Instruct NVFP4](https://huggingface.co/Denali-AI/qwen3-vl-8b-instruct-nvfp4) | Qwen3-VL | 8B | Zero-shot+NVFP4 | **87.2%** | 75.0% | 100% | — |
| 7 | [Qwen3.5-2B Base](https://huggingface.co/Denali-AI/qwen35-2b-base) | Qwen3.5-VL | 2B | Zero-shot | **84.4%** | 73.0% | 100% | — |
| 8 | [Qwen3-VL-2B SFT+GRPO v9 NVFP4](https://huggingface.co/Denali-AI/qwen3-vl-2b-sft-grpo-v9-nvfp4) | Qwen3-VL | 2B | SFT+GRPO+NVFP4 | **84.2%** | 74.1% | 100% | — |
| 9 | qwen3.5-0.8b-orr-sft | ? | ? | ? | **79.7%** | 70.5% | 100% | — |
| 10 | qwen3.5-2b-orr-sft | ? | ? | ? | **79.6%** | 69.9% | 100% | — |
| 11 | [Qwen3-VL-2B Instruct (Base)](https://huggingface.co/Denali-AI/qwen3-vl-2b-instruct-base) | Qwen3-VL | 2B | Zero-shot | **76.4%** | 66.7% | 100% | — |
| 12 | [InternVL3-2B GRPO+GTPO Full](https://huggingface.co/Denali-AI/internvl3-2b-grpo-gtpo-full) | InternVL3 | 2B | GRPO+GTPO | **72.7%** | 64.3% | 100% | — |
| 13 | [InternVL3-2B GRPO+GTPO FP8](https://huggingface.co/Denali-AI/internvl3-2b-grpo-gtpo-fp8) | InternVL3 | 2B | GRPO+GTPO+FP8 | **72.2%** | 63.8% | 100% | — |
| 14 | InternVL3-2B Base | InternVL3 | 2B | Zero-shot | **71.8%** | 63.7% | 100% | — |
| 15 | Moondream2 Base | Moondream | 1.6B | Zero-shot | **69.8%** | 61.8% | 100% | — |
| 16 | [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% | — |
| 17 | phi-4-multimodal-sft | ? | ? | ? | **65.1%** | 58.6% | 99% | — |
| 18 | [Qwen3.5-2B SFT v7](https://huggingface.co/Denali-AI/qwen35-2b-sft-merged) | Qwen3.5-VL | 2B | SFT | **63.7%** | 58.9% | 100% | — |
| 19 | [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% | — |
| 20 | Qwen3.5-9B NVFP4 v10 | Qwen3.5-VL | 9B | Zero-shot | **47.0%** | 46.0% | 8% | — |
| 21 | Qwen3.5-9B SFT NVFP4 v11 | Qwen3.5-VL | 9B | SFT+NVFP4 | **46.3%** | 45.5% | 8% | — |
| 22 | Qwen3.5-2B NVFP4 v10 | Qwen3.5-VL | 2B | Zero-shot | **42.9%** | 42.9% | 0% | — |
| 23 | Qwen3.5-122B-A10B NVFP4 | Qwen3.5 MoE | 122B (10B) | Zero-shot+NVFP4 | **42.9%** | 42.9% | 0% | — |
| 24 | Qwen3.5-2B SFT NVFP4 v11 | Qwen3.5-VL | 2B | SFT+NVFP4 | **42.9%** | 42.9% | 0% | — |
| 25 | Qwen3.5-2B SFT+GRPO+GTPO NVFP4 | Qwen3.5-VL | 2B | SFT+GRPO+GTPO+NVFP4 | **42.9%** | 42.9% | 0% | — |
| 26 | Phi-4 Multimodal NVFP4 | Phi-4 | 5.6B | Zero-shot+NVFP4 | **42.9%** | 42.9% | 0% | — |
| 27 | Qwen3-8B FP8 | Qwen3 | 8B | Zero-shot+FP8 | **42.9%** | 42.9% | 0% | — |
| 28 | granite4-vision-sft-vllm | ? | ? | ? | **42.9%** | 42.9% | 0% | — |
| 29 | granite4-vision-sft-vllm-deepstack | ? | ? | ? | **42.9%** | 42.9% | 0% | — |
---
## 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 | 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**](https://huggingface.co/collections/Denali-AI/qwen3-vl-models-69c70950fca01f437228c29b) | 3 | Top-performing Qwen3-VL based models (2B, 8B, 8B-NVFP4) |
| [**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 — Powered by [PeakBench](http://10.201.28.30:8069)
All evaluation is run through **PeakBench**, our centralized benchmarking platform. Results are automatically synced to HuggingFace model cards via the [PeakBench-HF sync bridge](https://huggingface.co/Denali-AI/org-assets/blob/main/peakbench_metrics.json). The canonical metric definitions live in [`peakbench_metrics.json`](https://huggingface.co/Denali-AI/org-assets/blob/main/peakbench_metrics.json) and are shared between both platforms.
| Metric | Weight (JSON GT) | Model / Method | Description |
|--------|:-----------------:|----------------|-------------|
| **Structured Match** | 60% | Field-level JSON comparison | Per-field presence + value accuracy (null-aware) |
| **SBERT Similarity** | 25% | all-mpnet-base-v2 | Semantic cosine similarity via sentence embeddings |
| **Token Set Ratio** | 10% | rapidfuzz | Fuzzy word-set overlap (order-independent) |
| **ROUGE-L** | 5% | LCS F1 | Longest common subsequence F-measure |
| **chrF++** | — | char+word n-grams | Character and word n-gram F-score |
| **METEOR** | — | stems+synonyms | Alignment with stemming and synonym matching |
| **BLEU** | — | n-gram precision | BLEU with brevity penalty |
| **Levenshtein** | — | edit distance | Normalized character-level edit distance |
| **Hallucination** | — | DeBERTa-v3 NLI | Contradiction detection between prompt and response |
| **Consistency** | — | SBERT pairwise | Determinism across repeated inference runs |
PeakBench Metric Definitions (click to expand)
#### PeakBench Quality Score
The **headline composite metric**. For JSON ground truth (our task), weights are: structured match 60%, SBERT similarity 25%, token set ratio 10%, ROUGE-L 5%. Exact case-insensitive match short-circuits to 1.0.
#### Structured Match
Per-field JSON comparison. Decomposes into **field_match_rate** (fraction of expected keys present) and **value_accuracy** (fraction of matched fields with correct values). Null-aware: treats "N/A", "none", "not visible", etc. as equivalent null values.
#### SBERT Similarity
Semantic cosine similarity using [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) sentence embeddings. Captures meaning-level similarity — "navy blue" and "dark blue" score high despite different strings.
#### chrF++ Score
Character and word n-gram F-score. Robust for morphologically rich text and partial matches at the character level.
#### METEOR Score
Alignment-based metric with stemming and synonym matching. Captures paraphrase similarity — "t-shirt" and "tee shirt" score high.
#### ROUGE-L Score
Longest common subsequence F1. Measures structural word-order overlap between prediction and ground truth.
#### BLEU Score
N-gram precision with brevity penalty. Standard MT metric, useful as a surface-level quality signal.
#### Token Set Ratio
Fuzzy word-set overlap via rapidfuzz. Order-independent — "blue navy" matches "navy blue" perfectly.
#### Levenshtein Ratio
Normalized character-level edit distance. `1 - (edits / max_length)`. Catches typos and minor variations.
#### Hallucination Score
NLI contradiction probability between prompt and response using [DeBERTa-v3-base-mnli-fever-anli](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli). Higher = more hallucinated. When contradiction > 0.5, the composite score is penalized.
#### Consistency (Semantic)
Average pairwise SBERT cosine across multiple inference runs on the same prompt. Measures model determinism. 1.0 = perfectly consistent outputs.
#### JSON Parse Rate
Percentage of outputs that are valid, parseable JSON. Fine-tuned models achieve 100%; zero-shot models often fail at 0-14%.
#### Throughput
Samples per second via vLLM on NVIDIA RTX PRO 6000 Blackwell (98 GB VRAM), 8 concurrent workers.
> Full metric definitions: [`peakbench_metrics.json`](https://huggingface.co/Denali-AI/org-assets/blob/main/peakbench_metrics.json)
All metrics are computed by [PeakBench](http://10.201.28.30:8069) and automatically synced to HuggingFace model cards. The shared metric config ensures both platforms always display the same numbers.
### 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. **Best model: Granite4-Vision-3B SFT** achieves **101.4% weighted score** with 100% JSON parse rate on 3,500 hard samples.
2. **Granite4-Vision dominates.** Best Granite4-Vision (101.4%) leads best Qwen3-VL (91.3%) by 10.1pp. Architecture rankings: Granite4-Vision (101%), Qwen3-VL (91%), Qwen3.5-VL (84%), ? (80%), InternVL3 (73%).
3. **SFT is essential for structured output.** Fine-tuned models: 76% avg JSON parse rate, best 101.4%. Zero-shot models: 52% avg JSON parse, best 87.5%. Training adds +13.9pp at the top.
4. **NVFP4 quantization costs 5.3pp on average** (max 5.3pp) across 1 model pairs, while reducing size ~60% and increasing throughput ~50%.
5. **Hardest fields** (on best model): neckline (79%), pattern (80%), type (80%). **Easiest:** brand (96%), defect (97%), size (100%).
6. **Scale vs efficiency.** Best large (Qwen3-VL-8B SFT+GRPO: 91.3%) beats best small (Qwen3-VL-2B SFT+GRPO v9: 89.5%) by 1.8pp — small model is highly competitive for edge deployment.
7. **Benchmark coverage:** 29 models across 9 architectures, 12 fine-tuned + 17 zero-shot/quantized.
---
## Research Directions & Future Work
### Near-Term Improvements
| Direction | Expected Impact | Rationale |
|-----------|:--------------:|-----------|
| **SFT+GRPO on Moondream** | +5-15pp | Zero-shot at 69.8%, fine-tuning consistently adds significant gains |
| **SFT+GRPO on Qwen3.5 MoE** | +5-15pp | Zero-shot at 50.7%, fine-tuning consistently adds significant gains |
| **SFT+GRPO on Phi-4** | +5-15pp | Zero-shot at 42.9%, fine-tuning consistently adds significant gains |
| **SFT+GRPO on Qwen3** | +5-15pp | Zero-shot at 42.9%, fine-tuning consistently adds significant gains |
| **NVFP4 quantize Granite4-Vision-3B SFT** | -1-2pp, +50% speed | At 101.4%, no quantized variant exists yet |
| **NVFP4 quantize Qwen3.5-2B SFT+GRPO+GTPO v8** | -1-2pp, +50% speed | At 65.3%, no quantized variant exists yet |
| **NVFP4 quantize Qwen3.5-2B SFT v7** | -1-2pp, +50% speed | At 63.7%, no quantized variant exists yet |
| **GTPO on Qwen3-VL-8B SFT+GRPO** | +1-3pp | Currently SFT+GRPO only, GTPO adds trajectory coherence |
| **GTPO on Qwen3-VL-2B SFT+GRPO v9** | +1-3pp | Currently SFT+GRPO only, GTPO adds trajectory coherence |
### Architecture Exploration
Models not yet benchmarked — recommended based on current findings:
| Model | Parameters | Why Promising |
|-------|:----------:|---------------|
| **Qwen3-VL-3B-Instruct** | 3B | Same family as our #1 (Granite4-Vision), mid-range scale |
| **InternVL3-8B** | 8B | Larger InternVL — may close gap to Qwen3-VL at same scale |
| **InternVL3-4B** | 4B | Mid-range InternVL — potential efficiency sweet spot |
| **SmolVLM2-2.2B-Instruct** | 2.2B | HuggingFace's efficient VLM — strong structured output |
| **PaliGemma2-3B** | 3B | Google VLM with excellent OCR — may improve brand/size fields |
| **Phi-4-multimodal-instruct** | 5.6B | Microsoft VLM — needs SFT (zero-shot JSON fails) |
| **MiniCPM-V-2.6** | 2.8B | Strong small VLM with good OCR capabilities |
| **Molmo-7B-D** | 7B | Allen AI VLM — strong visual grounding, may help with defect detection |
| **Idefics3-8B** | 8B | HuggingFace VLM — instruction-following optimized |
| **DeepSeek-VL2-Small** | 3B | DeepSeek's latest compact VLM — strong reasoning |
### Long-Term Research
1. **Ensemble routing:** Route each field to its best-performing model architecture
2. **Multi-image input:** Front + back + tag images simultaneously for higher brand/size accuracy
3. **Curriculum learning:** Progressive difficulty — easy garments first, hard edge cases last
4. **Synthetic data:** Use 122B models to generate training labels at scale
5. **Active learning:** Prioritize annotation of samples where models disagree most
6. **Guided JSON decoding:** Constrained generation to force valid JSON without training
### Key Open Questions
- Why does Granite4-Vision outperform Qwen3-VL by 10.1pp at similar scale? Vision encoder, cross-attention, or training data?
- Can RL gains (GRPO/GTPO) be amplified beyond current levels with better reward engineering?
- Is there a parameter sweet spot between 2B and 8B where accuracy saturates?
- Would domain-specific pre-training (garment images) outperform general VLM fine-tuning?
- **closure** averages only 52% across top-5 models — is the ground truth noisy, or is this genuinely hard?
---
## 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) |
---
*Last updated: 2026-04-04 03:59 UTC*
---
## 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)