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aacudad
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Model Details
Model Provider
aacudad
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Two stages. First supervised fine-tuning on 6,000 teacher-written reasoning traces, which gives aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT. Then GRPO on top of that. This is checkpoint-530 of the GRPO run.
This model also has a second role in the project. It is the policy that Keep-Correct-Revise (KCR) rolls out from. KCR is the recipe where this SFT plus GRPO policy generates its own reasoning traces on Real-IAD images, and those traces are then kept when the verdict is correct, corrected by a teacher when the verdict is wrong, and revised when the verdict is correct but the reasoning is only weakly grounded. The base model is then fine-tuned from scratch on that curated corpus, which produces the thesis headline model aacudad/AnomalyThink-Qwen2.5-VL-7B-KCR.
| Benchmark | Balanced accuracy |
|---|---|
| DS-MVTec (1,670) | 82.73% |
| VisA (2,141) | 70.39% |
Evaluated under a single common harness on the MMAD DS-MVTec and VisA subsets. GRPO clearly lifts the plain SFT baseline here. On the stronger KCR initialisation the thesis finds no comparable gain, so KCR remains SFT only.
Supervised fine-tuning on AnomalyThink-6K, then GRPO with a four-component reward (format, accuracy, type, location) and group size G=4.
The "AnomalyThink" reasoning traces were distilled from Gemini 2.5-Flash on Real-IAD images. Training data: aacudad/AnomalyThink.
| Model | DS-MVTec | VisA | Note |
|---|---|---|---|
…-7B-KCR | 82.80 | 72.07 | thesis headline |
…-7B-SFT | 80.16 | 64.78 | plain SFT baseline |
python
import torchfrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationmodel_id = "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT-GRPO"model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")processor = AutoProcessor.from_pretrained(model_id)# Build the structured single-image IAD prompt + image, then generate.
Research on explainable IAD. Known limitations: the model can confidently hallucinate a defect on a normal part (false positive), and GRPO-lineage variants can over-predict the "Missing Parts" type. As the public DS-MVTec/VisA images may appear in VLM pretraining, absolute numbers should be read with that caveat.
bibtex
@mastersthesis{acudad2026anomalythink,title = {Reasoning-Enhanced Vision-Language Models for Explainable Industrial Anomaly Detection},author = {Acudad, Adnane},school = {Delft University of Technology},year = {2026}}
Apache-2.0 (inherits the Qwen2.5-VL-7B base). Trained on Real-IAD (cite Real-IAD separately, images are not redistributed) with traces distilled from Gemini 2.5-Flash.
…-7B-SFT-GRPO| 82.73 |
| 70.39 |
| this model |
…-7B-KCR-GRPO | 82.95 | 72.62 | research preview, not a thesis result |