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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) | 88.45 |
| MMAD VisA (2,141 images) | 74.25 |
Which build these weights are. This checkpoint is the first KCR build on this backbone. Its corpus was drawn from the whole 10,236-image rollout pool rather than from the 6,000-image SFT split (only 3,516 of its images lie inside that split, the other 2,484 come from the GRPO split) and it was balanced on the folder name rather than on the trace verdict, which left it at 45.0 % anomalous. The thesis reports the corrected build, rebuilt with all 6,000 images inside the SFT split and exactly 3,000 / 3,000 by verdict, as the replication result: 84.35 / 71.36, 87.32 / 72.65, 86.96 / 73.57 and 86.60 / 74.29 at epochs 1 to 4, with 87.32 / 72.65 the DS-MVTec-selected row. The first build stays on record in the thesis (Table 6.16, Appendix M). Both corpora are in the dataset repo: llava_kcr/sft_llava_C_train.json (first build, this model) and llava_kcr/sft_llava_C_corrected_train.json (corrected build).
Reference rows measured on the exact same harness:
| Model | DS-MVTec | VisA |
|---|---|---|
| LLaVA-OneVision-7B-SI base | 75.66 | 53.80 |
| IAD-R1 released checkpoint | 81.92 | 71.34 |
| AnomalyThink LLaVA SFT (6K Gemini traces) | 85.91 | 68.26 |
| AnomalyThink LLaVA SFT then GRPO | 87.66 | 72.38 |
| This model (KCR, SFT only) | 88.45 | 74.25 |
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. Generation ran through vLLM 0.10.2,
which agreed with the plain transformers generate path 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 88.45
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 74.25 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.llava_kcr/sft_llava_C_train.json,
6,000 traces, balanced 50/50 over normal and anomalous parts, on Real-IAD images.This matters, so it is stated plainly. KCR stands for keep, correct, rewrite. The corpus for this model was built from LLaVA-OneVision's own rollouts, not from the Qwen rollouts used elsewhere in the thesis. The loop was:
The intermediate arms are published next to the final one:
llava_kcr/
holds sft_llava_A_kept.json (keep only), sft_llava_B_kept_corrected.json (keep plus
corrected), sft_llava_C_train.json (the first-build corpus this model was trained on) and
sft_llava_C_corrected_train.json (the corrected build the thesis reports, see the build note above).
Because the rollouts come from this backbone, the corpus is on-policy for LLaVA-OneVision, and that is worth the extra loop. We also ran the control, which is the Qwen derived KCR corpus fine-tuned on this same LLaVA backbone with the same recipe. Its four epochs scored 87.70 / 70.97, 85.05 / 73.52, 86.97 / 69.95, and 85.85 / 70.75. No single checkpoint of that control reaches the 88.45 / 74.25 of the native corpus, and no checkpoint is best on both benchmarks at once. Building the corpus from the backbone's own rollouts is the difference.
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 ships
a plain text_config.rope_theta = 1000000.0 next to the 5.0 style block, so it loads
correctly on both major versions.
python
import torchfrom PIL import Imagefrom transformers import AutoProcessor, LlavaOnevisionForConditionalGenerationrepo = "aacudad/AnomalyThink-LLaVA-OneVision-7B-KCR"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.
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.