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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
| 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 lifts the SFT baseline here; the thesis shows it adds no gain on the stronger Arm-C initialisation.
Supervised fine-tuning on AnomalyThink-6K, then GRPO with a four-component reward (format, accuracy, type, location; group size G=4).
The "AnomalyThink" reasoning traces were distilled from Gemini 2.5-Flash on Real-IAD images. Training data: aacudad/AnomalyThink.
python
import torchfrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationmodel = Qwen2_5_VLForConditionalGeneration.from_pretrained("aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT-GRPO", torch_dtype=torch.bfloat16, device_map="auto")processor = AutoProcessor.from_pretrained("aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT-GRPO")# 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.