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aacudad
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llava-hf/llava-onevision-qwen2-7b-si-hf
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Metric is balanced accuracy, (TPR + TNR) / 2, in percent.
| Benchmark | Balanced accuracy |
|---|---|
| MMAD DS-MVTec (1,670 images) | 85.91 |
| MMAD VisA (2,141 images) | 68.26 |
Reference rows measured on the exact same harness:
| Model | DS-MVTec | VisA |
|---|---|---|
| LLaVA-OneVision-7B-SI base | 75.66 | 53.80 |
| This model (SFT) | 85.91 | 68.26 |
| IAD-R1 released checkpoint | 81.92 | 71.34 |
| AnomalyThink LLaVA SFT then GRPO | 87.66 | 72.38 |
| AnomalyThink LLaVA KCR, first build (released weights) | 88.45 | 74.25 |
| AnomalyThink LLaVA KCR, corrected build (thesis row, epoch 2 / epoch 4) | 87.32 / 86.60 | 72.65 / 74.29 |
Evaluation protocol. One shared harness for every row above. The DS-MVTec and VisA
subsets of MMAD, single image per prompt, the same instruction the model was trained on,
greedy decoding at temperature 0, at most 1024 new tokens, images capped at 262,144
pixels. Answers are parsed from the <answer> tag. This particular row was generated
through the plain transformers generate path. The vLLM 0.10.2 path used for the sibling
models agreed with it on 99 percent of a probe set. Nothing here is a re-scored or
best-of-N number.
Strict scoring: a generation with no parsable verdict counts as wrong, as in the thesis. Six of the 2,141 VisA generations of the SFT then GRPO model have no verdict, which gives 72.38; over the 2,135 parsable outputs it is 72.58. No ordering changes.
The public LLaVA-OneVision training mixture (lmms-lab/LLaVA-OneVision-Data, config
vision_flan(filtered)) contains 426 rows whose id matches %MVTecAD%. The base model
has therefore seen MVTec-AD material during its own instruction tuning. Every DS-MVTec
number for any LLaVA-OneVision derived model carries that caveat, including the 85.91
above, and including the IAD-R1 row. We do not know how much of the gap is real
capability and how much is recall.
VisA is not affected. The same query over the mixture returns 0 rows for VisA. So the 68.26 on VisA is the clean number and it is the one to trust for a cross-model comparison.
llava-hf/llava-onevision-qwen2-7b-si-hf.anomalythink_6k/combined_6k_train.json,
6,000 AnomalyThink traces distilled from Gemini 2.5-Flash on Real-IAD images. This
is the same corpus as the Qwen2.5-VL headline SFT model, which is the point. It
isolates the backbone as the only thing that changed.This model is not trained on a KCR corpus, it is trained on the Gemini 2.5-Flash traces. The note still belongs here, because this checkpoint is where the KCR loop starts.
KCR stands for keep, correct, rewrite. The KCR corpus used by the sibling KCR model was built from LLaVA-OneVision's own rollouts, not from the Qwen rollouts used elsewhere in the thesis. GRPO was run on this checkpoint, that GRPO model produced 10,236 rollouts on Real-IAD, the rollouts were bucketed into keep, correct, and rewrite, and a Gemini 2.5-Flash judge then repaired the failing ones. The result is on-policy for this backbone, and that matters. The control, which is the Qwen derived KCR corpus trained on this same backbone, peaked at 87.70 on DS-MVTec and 73.52 on VisA in different epochs. The native corpus reaches 88.45 and 74.25 in one checkpoint.
All three arms of that corpus are published at
llava_kcr/:
sft_llava_A_kept.json, sft_llava_B_kept_corrected.json, and sft_llava_C_train.json.
The weights and configs in this repo were written by transformers 4.51.3. Loading, the processor, and the full evaluation were verified under transformers 4.57.1, both through the plain HF generate path and through vLLM 0.10.2.
One warning for anyone rebuilding this pipeline. Checkpoints saved by transformers
5.0 write the rope settings under text_config.rope_parameters. Transformers 4.x does
not read that key and silently falls back to rope_theta = 10000, which is 100 times
too small. The model then stays fluent but goes blind and answers "no" to nearly
everything, which looks like a collapsed run rather than a loading bug. This repo was
written by 4.51.3 so it was never affected, and it carries a plain
text_config.rope_theta = 1000000.0 like the rest of the family.
python
import torchfrom PIL import Imagefrom transformers import AutoProcessor, LlavaOnevisionForConditionalGenerationrepo = "aacudad/AnomalyThink-LLaVA-OneVision-7B-SFT"model = LlavaOnevisionForConditionalGeneration.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")processor = AutoProcessor.from_pretrained(repo)image = Image.open("part.png").convert("RGB")product = "tile" # the product category, it goes into the promptquestion = (f"Analyze the provided image of the {product}. ""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.")messages = [{"role": "user", "content": [{"type": "image"},{"type": "text", "text": question},]}]prompt = processor.apply_chat_template(messages, add_generation_prompt=True)inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device, torch.bfloat16)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))
Use this exact instruction. The model was trained on it and it degrades on a different phrasing.
Expected output on a defective part:
markdown
<think>I am inspecting a tile with a speckled, grayish-white surface. ... In the center of thetile, I detect a triangular, translucent plastic fragment. ...</think><location>center</location><type>Contamination</type><answer>Yes</answer>
On a normal part the model emits <think> and then <answer>No</answer>, with no
<location> or <type> tag.
Research on explainable industrial anomaly detection. This is a thesis artefact, not a production inspection system. If you only want the best detector, take the KCR model instead.
Known limitations:
<type> label is coarse and the model over-uses "missing parts" for any loss of
material, including chips and gouges.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. The training data is at aacudad/AnomalyThink.
Apache-2.0, inherited from the LLaVA-OneVision-7B-SI base. Trained on Real-IAD images, which are not redistributed here, so cite Real-IAD separately. Reasoning traces were distilled from Gemini 2.5-Flash.