If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community! If you're interested in learning more...
Why GitHub? Team Enterprise Explore Marketplace Pricing Sign inSign up tensorflow/datasets Watch105 Star2.5k Fork877 Code Issues234 Pull requests151 Actions Security Insights More master datasets/tensorflow_datasets/object_detection/waymo_open_dataset.py/ ...
https://www.tensorflow.org/datasets GitHub https://github.com/tensorflow/datasets Colab教程 https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/overview.ipynb Enjoy yourself~ —完— 量子位 · QbitAI վ'ᴗ' ի 追踪AI技术和产品新动态 戳右上角「+关注」获取最新资讯...
在训练过程中如果出现no model named pycocotools的问题的话,请参考这个网址(http://www.mamicode.com/info-detail-2660241.html)解决。亲测有效 即: (1)从https://github.com/pdollar/coco.git 下载源码,解压至全英文路径下。 (2)使用cmd进入解压后的cocoapi-master/PythonAPI路径下,输入python setup.py b...
# 参考 https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/translate/wmt19.py# 参考 https://www.statmt.org/wmt19/translation-task.htmlconfig=tfds.translate.wmt.WmtConfig(version="0.0.1",language_pair=("zh","en"),subsets={tfds.Split.TRAIN:["commoncrawl","newscommentary_v1...
tf.data.Dataset 进行简单划分验证集可以参考https://github.com/tensorflow/datasets/issues/665#issuecomment-502409920 如果想对 MNIST 等数据集手动分层随机划分出一个验证集,还是转化成 numpy.ndarray 比较方便,再使用 sklearn 的 train_test_split 方法一行代码就可以搞定。
https://github.com/YaoAIPro/bert-reproduction/blob/main/download_glue_data.py
https://github.com/YaoAIPro/bert-reproduction/blob/main/download_glue_data.py
py https://github.com/tensorflow/datasets/blob/v1.3.0/tensorflow_datasets/core/dataset_builder.py#L236-L308 查看tensorflow官方文档 代码语言:javascript 复制 https://tensorflow.google.cn/datasets/api_docs/python/tfds/core/DatasetInfo 其中有关于数据集dataset的info文件,诶,会不会是他呢? 于是查找...
', homepage='https://github.com/zalandoresearch/fashion-mnist', features=FeaturesDict({ 'image': Image(shape=(28, 28, 1), dtype=tf.uint8), 'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10), }), total_num_examples=70000, splits={ 'test': 10000, 'train': 60000, ...