Please adhere to the specified structure to ensure correct operation of the program. If there are any questions or issues, feel free to raise an issue here on GitHub. Usage To compute the CLIP score between images and texts, make sure that the image and text data are contained in two sepa...
clip-score/clip_score.py 目前限制 prompt最大长度为77个。如果超过77,默认会挂掉。 可通过修改https://github.com/Taited/clip-score/blob/dbfa0cabd7bfe12f84899786731c35aa62baab4a/src/clip_score/clip_score.py#L131,修改为data = self.tokenizer(data,truncate=True).squeeze(),把超过77的部分截断。 结...
4 changes: 2 additions & 2 deletions 4 src/clip_score/clip_score.py Original file line numberDiff line numberDiff line change @@ -88,7 +88,7 @@ def __init__(self, real_path, fake_path, ) self.real_folder = self._combine_without_prefix(real_path)...
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clip_score = 0 cnt = 0 for key in tqdm.tqdm(caption_data.keys()): img_path = os.path.join(args.png_path, key) if not os.path.exists(img_path): print("[WARNING] %s not found"%img_path) continue image = Image.open(img_path) caption = caption_data[key] clip_score += evaluat...
代码:https://github.com/zjukg/Structure-CLIP CLIP在结构化的文本-图像匹配上表现不够,如通过clip score并不能区别一张图是人咬狗和狗咬人. 作者认为造成这个问题的原因是CLIP在学习多模态场景中的representations时未能充分利用结构化知识. 文章提出 Structure-CLIP ,一个端到端的框架,通过集成场景图知识来增强多...
Tools (written in C using htslib) for manipulating next-generation sequencing data - Fix hard clip quality score. · samtools/samtools@59ac156
Machine learning metrics for distributed, scalable PyTorch applications. - torchmetrics/src/torchmetrics/multimodal/clip_score.py at master · Lightning-AI/torchmetrics
aesthetic_score: the aesthetic score as rated by aesthetic-predictor. Default is 9. use_mclip: whether to use a multi-lingual version of CLIP. Default is False. aesthetic_weight: the weight of the aesthetic score. Default is 0.5 modality: search over image or text in the index, one of ...
Image Credit:https://github.com/openai/CLIP Usage pip install open_clip_torch importtorchfromPILimportImageimportopen_clip model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32-quickgelu', pretrained='laion400m_e32')