除了传统的指标外,我们还展示了基于gpt的评估如何在评估多个方面的响应质量方面与类似人类的表现相匹配。我们提出了一个简单的基线:video - llava,它使用单个线性投影,优于现有的视频llm。最后,我们评估了学术数据集之外的视频llm,这些数据集显示出在驾驶场景中令人鼓舞的识别和推理能力,只有数百个视频指令对进行微调。
Open-source evaluation toolkit of large vision-language models (LVLMs), support 160+ VLMs, 50+ benchmarks - History for vlmeval/vlm/molmo.py - open-compass/VLMEvalKit
(result_file) from vlmeval.evaluate.multiple_choice import build_choices, can_infer tot = defaultdict(lambda: 0) match = defaultdict(lambda: 0) hit = defaultdict(lambda: 0) lt = len(data) for i in range(lt): item = data.iloc[i] cate = item['category'] tot['Overall...
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print(f'VLM参数量:{sum(p.numel()forpinmodel.parameters()ifp.requires_grad)/1e6:.3f}百万') vision_model,preprocess=MiniMindVLM.get_vision_model() returnmodel.eval().to(device),tokenizer,vision_model.eval().to(device),preprocess defsetup_seed(seed): ...
Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks - VLMEvalKit/vlmeval/__init__.py at 058f6733a82954fd4c8c72dfc22c6b3881c93638 · open-compass/VLMEvalKit
The code I used is from vlmeval. class DeepSeekVL2(BaseModel): INSTALL_REQ = True INTERLEAVE = True def __init__(self, model_path='deepseek-ai/deepseek-vl-1.3b-chat', **kwargs): from deepseek_vl2.models import DeepseekVLV2Processor ...
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Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks - VLMEvalKit/vlmeval/vlm/omchat.py at f85582bb9919bfa1180fa1f6a7d9c781e7fa481d · open-compass/VLMEvalKit
Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks - VLMEvalKit/vlmeval/vlm/molmo.py at 058f6733a82954fd4c8c72dfc22c6b3881c93638 · open-compass/VLMEvalKit