[2] https://guoxiansong.github.io/homepage/agilegan.html [3] https://github.com/NVlabs/metfaces-dataset [4] https://www.kaggle.com/defileroff/comic-faces-paired-synthetic-v2 [5] https://www.kaggle.com/arnaud58/photo2cartoon [6] https://www.kaggle.com/mostafamozafari/bitmoji-faces [7]...
首先当然得感谢FAIR的铁三角(Ross, Kaiming, Piotr),以及诸位埋头苦干的intern(Xiaolong, Saining等)...
image_processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-large-patch4-window12-192-22k") model = Swinv2Model.from_pretrained("microsoft/swinv2-large-patch4-window12-192-22k") image_processor 定义了一组应用于输入图像的变换,这些图像最初以PIL图像的形式存在: ViTImageProcessor { "_val...
num_train_epochs=3, per_device_train_batch_size=32, save_steps=500, save_total_limit=2, evaluation_strategy="steps", logging_steps=100, logging_dir='./logs', ) # 定义Trainer并开始微调 trainer = Trainer( model=model, args=training_args, train_dataset=your...
df = pd.read_csv("/kaggle/input/queestion-answer-dataset-qa/train.csv") df.columns df = df[['context','question', 'text']] print("Number of records: ", df.shape[0]) 问题陈述 “创建一个能够根据上下文和问题生成响应的模型。” ...
The dataset used for this article is fromKaggle(link is provided in the references). It provided 5000 images for both cases. It also provided some testing and validation images to test our model after being trained. Step 1: Importing the images ...
(7) Unconstrained Face Detection Dataset(UFDD) 数据集地址:https://ufdd.info/。 发布于2018年,这是一个非限制场景下的人脸检测数据集,总共包含6425张图、10897张人脸,包含雨天(Rain)、雪天(Snow)、雾天(Haze)、模糊(Blur)、光照(Illumination)、晶体障碍(Lens impediments)和干扰物(Distractors)等7个场景。
下面的代码是一次性对所有的训练数据进行分析。第一个输入参数为文本1的列表,第二个输入参数为文本2的列表,同时还设置了补齐参数和截断参数(下面的做法一般针对比较小的数据集,比较大数据集合要使用dataset的map函数来操作,防止内存不够)。 tokenized_dataset=tokenizer(raw_datasets["train"]["sentence1"],raw...
df=pd.read_csv("/kaggle/input/queestion-answer-dataset-qa/train.csv")df.columns df=df[['context','question','text']]print("Number of records: ",df.shape[0]) 问题陈述 “创建一个能够根据上下文和问题生成响应的模型。” 例如 Context = “例如,对类似病例的聚类组可以找到 ...
GoogleannouncedFaceNet as its deep learning based face recognition model. It was built on theInceptionmodel. We have been familiar with Inception in kaggle imagenet competitions. Basically, the idea to recognize face lies behindrepresentingtwo images as smaller dimension vectors and decide identity base...