# REG on ColonINST (seen)

Source: [REG on ColonINST (seen)](<https://www.sota2.com/research/sota/reg-on-coloninst-seen>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:27:05.348Z

Evaluation of REG performance on the seen split of the ColonINST dataset, reporting Accuracy.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | REG |
| Dataset (including any stated split) | ColonINST (seen) |
| Metric | Accuracy |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 17 of 17 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Accuracy](<https://www.sota2.com/research/sota/reg-on-coloninst-seen.md?metric=Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| ColonGPT | 99.96 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-v1.5 | 99.32 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-Med-v1.5 | 99.3 | Not recorded | 68fa7b831e209131cea88cd0 |
| MGM-2B | 98.75 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-v1.5 | 98.58 | Not recorded | 68fa7b831e209131cea88cd0 |
| MGM-2B | 98.17 | Not recorded | 68fa7b831e209131cea88cd0 |
| MobileVLM-1.7B | 97.87 | Not recorded | 68fa7b831e209131cea88cd0 |
| MobileVLM-1.7B | 97.78 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-Med-v1.0 | 97.74 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-Med-v1.0 | 97.35 | Not recorded | 68fa7b831e209131cea88cd0 |
| Bunny-v1.0-3B | 96.61 | Not recorded | 68fa7b831e209131cea88cd0 |
| Bunny-v1.0-3B | 96.02 | Not recorded | 68fa7b831e209131cea88cd0 |
| MiniGPT-v2 | 94.69 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-Med-v1.5 | 90.4 | Not recorded | 68fa7b831e209131cea88cd0 |
| MiniGPT-v2 | 87.65 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-v1 | 86.87 | Not recorded | 68fa7b831e209131cea88cd0 |
| LLaVA-v1 | 84.55 | Not recorded | 68fa7b831e209131cea88cd0 |

### ColonGPT

Accuracy: 99.96. Result ID: 698b865d7a967eddb76f4f6e.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Implementation](<https://github.com/ai4colonoscopy/IntelliScope>)

Recorded evaluation configuration: {"Visual encoder":"SigLIP-SO (384px)","Language model":"Phi1.5 (1.3B)","LoRA":false,"EXT":false}. This record does not assert independent reproduction.

### LLaVA-v1.5

Accuracy: 99.32. Result ID: 698b865d7a967eddb76f4f63.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa732e1e209131cea5af59.md>)

[Implementation](<https://llava-vl.github.io/>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (336px)","Language model":"Vicuna-v1.5 (7B)","LoRA":true,"EXT":true}. This record does not assert independent reproduction.

### LLaVA-Med-v1.5

Accuracy: 99.3. Result ID: 698b865d7a967eddb76f4f6c.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6f8e1e209131cea46356.md>)

[Implementation](<https://aka.ms/llava-med>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (224px)","Language model":"Mistral-v0.2 (7B)","LoRA":true,"EXT":false}. This record does not assert independent reproduction.

### MGM-2B

Accuracy: 98.75. Result ID: 698b865d7a967eddb76f4f67.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa72ab1e209131cea57bca.md>)

[Implementation](<https://github.com/dvlab-research/MiniGemini>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (336px) & ConvNeXt-L (768px)","Language model":"Gemma (2B)","LoRA":true,"EXT":false}. This record does not assert independent reproduction.

### LLaVA-v1.5

Accuracy: 98.58. Result ID: 698b865d7a967eddb76f4f62.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa732e1e209131cea5af59.md>)

[Implementation](<https://llava-vl.github.io/>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (336px)","Language model":"Vicuna-v1.5 (7B)","LoRA":true,"EXT":false}. This record does not assert independent reproduction.

### MGM-2B

Accuracy: 98.17. Result ID: 698b865d7a967eddb76f4f66.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa72ab1e209131cea57bca.md>)

[Implementation](<https://github.com/dvlab-research/MiniGemini>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (336px) & ConvNeXt-L (768px)","Language model":"Gemma (2B)","LoRA":false,"EXT":false}. This record does not assert independent reproduction.

### MobileVLM-1.7B

Accuracy: 97.87. Result ID: 698b865d7a967eddb76f4f69.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa71a71e209131cea52555.md>)

[Implementation](<https://github.com/Meituan-AutoML/MobileVLM>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (336px)","Language model":"MobileLLaMA (1.4B)","LoRA":true,"EXT":true}. This record does not assert independent reproduction.

### MobileVLM-1.7B

Accuracy: 97.78. Result ID: 698b865d7a967eddb76f4f68.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa71a71e209131cea52555.md>)

[Implementation](<https://github.com/Meituan-AutoML/MobileVLM>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (336px)","Language model":"MobileLLaMA (1.4B)","LoRA":true,"EXT":false}. This record does not assert independent reproduction.

### LLaVA-Med-v1.0

Accuracy: 97.74. Result ID: 698b865d7a967eddb76f4f6a.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6f8e1e209131cea46356.md>)

[Implementation](<https://aka.ms/llava-med>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (224px)","Language model":"LLaMA1 (7B)","LoRA":false,"EXT":false}. This record does not assert independent reproduction.

### LLaVA-Med-v1.0

Accuracy: 97.35. Result ID: 698b865d7a967eddb76f4f6b.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6f8e1e209131cea46356.md>)

[Implementation](<https://aka.ms/llava-med>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (224px)","Language model":"LLaMA1 (7B)","LoRA":true,"EXT":false}. This record does not assert independent reproduction.

### Bunny-v1.0-3B

Accuracy: 96.61. Result ID: 698b865d7a967eddb76f4f64.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa74351e209131cea60750.md>)

[Implementation](<https://github.com/BAAI-DCAI/Bunny>)

Recorded evaluation configuration: {"Visual encoder":"SigLIP-SO (384px)","Language model":"Phi2 (2.7B)","LoRA":true,"EXT":false}. This record does not assert independent reproduction.

### Bunny-v1.0-3B

Accuracy: 96.02. Result ID: 698b865d7a967eddb76f4f65.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa74351e209131cea60750.md>)

[Implementation](<https://github.com/BAAI-DCAI/Bunny>)

Recorded evaluation configuration: {"Visual encoder":"SigLIP-SO (384px)","Language model":"Phi2 (2.7B)","LoRA":true,"EXT":true}. This record does not assert independent reproduction.

### MiniGPT-v2

Accuracy: 94.69. Result ID: 698b865d7a967eddb76f4f5e.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa71251e209131cea4f7e5.md>)

[Implementation](<https://minigpt-v2.github.io/>)

Recorded evaluation configuration: {"Visual encoder":"EVA-G/14 (448px)","Language model":"LLaMA2 (7B)","LoRA":false,"EXT":false}. This record does not assert independent reproduction.

### LLaVA-Med-v1.5

Accuracy: 90.4. Result ID: 698b865d7a967eddb76f4f6d.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6f8e1e209131cea46356.md>)

[Implementation](<https://aka.ms/llava-med>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (224px)","Language model":"Mistral-v0.2 (7B)","LoRA":false,"EXT":false}. This record does not assert independent reproduction.

### MiniGPT-v2

Accuracy: 87.65. Result ID: 698b865d7a967eddb76f4f5f.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa71251e209131cea4f7e5.md>)

[Implementation](<https://minigpt-v2.github.io/>)

Recorded evaluation configuration: {"Visual encoder":"EVA-G/14 (448px)","Language model":"LLaMA2 (7B)","LoRA":true,"EXT":true}. This record does not assert independent reproduction.

### LLaVA-v1

Accuracy: 86.87. Result ID: 698b865d7a967eddb76f4f61.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa717d1e209131cea51970.md>)

[Implementation](<https://llava-vl.github.io/>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (224px)","Language model":"Vicuna-v1.3 (7B)","LoRA":true,"EXT":true}. This record does not assert independent reproduction.

### LLaVA-v1

Accuracy: 84.55. Result ID: 698b865d7a967eddb76f4f60.

[Reporting paper](<https://www.sota2.com/research/paper/68fa7b831e209131cea88cd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa717d1e209131cea51970.md>)

[Implementation](<https://llava-vl.github.io/>)

Recorded evaluation configuration: {"Visual encoder":"CLIP-L/14 (224px)","Language model":"Vicuna-v1.3 (7B)","LoRA":true,"EXT":false}. This record does not assert independent reproduction.

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