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aacudad
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Model Details
Model Provider
aacudad
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Model | DS-MVTec | VisA |
|---|---|---|
| Qwen3-VL-8B-Instruct base, zero-shot | 78.68 | 64.45 |
| AnomalyThink-Qwen3-VL-8B-SFT (6K Gemini traces, epoch 1) | 80.31 | 67.32 |
| AnomalyThink-Qwen3-VL-8B-SFT-GRPO (epoch 1, training prompt) | 87.14 | 72.39 |
| AnomalyThink-Qwen3-VL-8B-KCR (own corrected rollouts, epoch 2) | 84.39 | 76.63 |
| This model | 87.14 | 72.39 |
Evaluation protocol. One harness for every row: the DS-MVTec (1,670 images) and VisA (2,141 images) subsets of MMAD, one image per prompt, the training prompt with the one-line system message "Please answer by yes or no", greedy decoding, at most 1,024 new tokens, images capped at 262,144 pixels, vLLM generation. Balanced accuracy is the mean of sensitivity and specificity. Strict scoring: a generation with no parsable <answer> counts as wrong. Per-sample evaluation files are next to the weights (eval_*.json).
The pattern of the thesis reproduces on this backbone: GRPO carries DS-MVTec, and the corrected corpus built from the GRPO policy's rollouts carries VisA, where 76.63 is the highest VisA score of any model trained in the project.
An SFT plus GRPO policy is sampled eight times on every training image. A trace is kept when the verdict is right and a Gemini-3-Flash judge finds the reasoning grounded, corrected by the teacher when every rollout was wrong, and revised by the teacher when the verdict was right but the reasoning was weakly grounded. A fresh model is then fine-tuned from the base weights on the curated corpus, so the final model is supervised and the reinforcement-learned policy is the source of its training data.
The checkpoint was saved with transformers 5.0.0 and loads directly with transformers>=5.0:
python
from transformers import AutoProcessor, AutoModelForImageTextToTextimport torchrepo = "aacudad/AnomalyThink-Qwen3-VL-8B-SFT-GRPO"processor = AutoProcessor.from_pretrained(repo)model = AutoModelForImageTextToText.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")msgs = [{"role": "system", "content": "Please answer by yes or no"},{"role": "user", "content": [{"type": "image", "image": "part.jpg"},{"type": "text", "text": "Analyze the provided image of the pcb. Determine if there are any anomalies present. If an anomaly is detected, specify its type and location, and provide a detailed reasoning for your conclusion."}]}]inputs = processor.apply_chat_template(msgs, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
The output is <think>...</think> followed, for anomalies, by <location>, <type> and <answer>Yes</answer>, or <answer>No</answer> for a normal part. For transformers 4.57 or vLLM 0.10.x use the files in transformers4_vllm/ (the stock Qwen3-VL-8B-Instruct tokenizer and processor files and a config with rope_theta written into text_config) next to model.safetensors; this is how every number above was produced.
Research artefact for explainable industrial anomaly detection on top-down product images. Single seed, no human evaluation of the explanations, selection on DS-MVTec with VisA reported at the same checkpoint. The public benchmark images may have been seen during backbone pretraining, which cannot be ruled out for any model in this comparison.
bibtex
@mastersthesis{acudad2026reasoning,author = {Acudad, A.},title = {Reasoning-Enhanced Vision-Language Models for Explainable Industrial Anomaly Detection},school = {Delft University of Technology},year = {2026},type = {Master's thesis},url = {https://resolver.tudelft.nl/uuid:65c62420-79c0-447f-b095-7fb11d4474fc}}
Thesis: https://resolver.tudelft.nl/uuid:65c62420-79c0-447f-b095-7fb11d4474fc. Code and evaluation files: https://github.com/aacudad/IAD-VLMs.
Apache-2.0, derived from Qwen/Qwen3-VL-8B-Instruct.