用于射电天文目标检测的神经网络源代码及权重
Peng Jia
本代码实现了一种专用于射电观测数据目标检测的神经网络。通过充分利用数据的完整三维结构（即一个频率维度和两个空间维度），本方法能够开展三维分析并输出贝叶斯推断结果。这种基于概率的输出结果对于后续科学研究具有关键意义。
文件
post_get_instances.py
14.22 kB
post_get_catalog.py
7.26 kB
create_data_128.py
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visualizer.cpython-38.pyc
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visualizer.cpython-310.pyc
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utils.cpython-38.pyc
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utils.cpython-310.pyc
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occlusion_sensitivity.cpython-38.pyc
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occlusion_sensitivity.cpython-310.pyc
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img2tensorboard.cpython-38.pyc
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img2tensorboard.cpython-310.pyc
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gradient_based.cpython-38.pyc
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gradient_based.cpython-310.pyc
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class_activation_maps.cpython-38.pyc
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class_activation_maps.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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visualizer.py
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utils.py
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occlusion_sensitivity.py
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img2tensorboard.py
9.05 kB
gradient_based.py
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class_activation_maps.py
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__init__.py
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type_conversion.cpython-38.pyc
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type_conversion.cpython-310.pyc
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tf32.cpython-38.pyc
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tf32.cpython-310.pyc
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state_cacher.cpython-38.pyc
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state_cacher.cpython-310.pyc
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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dictionary.py
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array.py
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__init__.py
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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dictionary.py
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array.py
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__init__.py
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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dictionary.py
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array.py
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__init__.py
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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dictionary.py
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__init__.py
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utils.cpython-38.pyc
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utils.cpython-310.pyc
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functional.cpython-38.pyc
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functional.cpython-310.pyc
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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utils.py
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functional.py
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dictionary.py
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array.py
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__init__.py
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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dictionary.py
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array.py
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__init__.py
573.00 B
dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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dictionary.py
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array.py
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__init__.py
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functional.cpython-38.pyc
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functional.cpython-310.pyc
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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batch.cpython-38.pyc
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batch.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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functional.py
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dictionary.py
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batch.py
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array.py
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__init__.py
573.00 B
utils_pytorch_numpy_unification.cpython-38.pyc
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utils_pytorch_numpy_unification.cpython-310.pyc
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utils_morphological_ops.cpython-310.pyc
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utils_create_transform_ims.cpython-38.pyc
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utils_create_transform_ims.cpython-310.pyc
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utils.cpython-38.pyc
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utils.cpython-310.pyc
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transform.cpython-38.pyc
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transform.cpython-310.pyc
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traits.cpython-38.pyc
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traits.cpython-310.pyc
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nvtx.cpython-38.pyc
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nvtx.cpython-310.pyc
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inverse_batch_transform.cpython-38.pyc
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inverse_batch_transform.cpython-310.pyc
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inverse.cpython-38.pyc
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inverse.cpython-310.pyc
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compose.cpython-38.pyc
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compose.cpython-310.pyc
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adaptors.cpython-38.pyc
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adaptors.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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utils_pytorch_numpy_unification.py
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utils_morphological_ops.py
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utils_create_transform_ims.py
30.42 kB
utils.py
103.89 kB
transform.py
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traits.py
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nvtx.py
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inverse_batch_transform.py
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inverse.py
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compose.py
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adaptors.py
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__init__.py
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utils.cpython-38.pyc
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utils.cpython-310.pyc
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novograd.cpython-38.pyc
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novograd.cpython-310.pyc
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vit.py
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vista3d.py
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varautoencoder.py
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unetr.py
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unet.py
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transformer.py
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transchex.py
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torchvision_fc.py
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swin_unetr.py
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spade_network.py
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spade_diffusion_model_unet.py
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spade_autoencoderkl.py
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senet.py
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segresnet_ds.py
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segresnet.py
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restormer.py
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resnet.py
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regunet.py
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regressor.py
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quicknat.py
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patchgan_discriminator.py
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netadapter.py
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milmodel.py
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mednext.py
12.95 kB
masked_autoencoder_vit.py
9.38 kB
hovernet.py
28.06 kB
highresnet.py
8.67 kB
generator.py
6.43 kB
fullyconnectednet.py
7.04 kB
flexible_unet.py
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efficientnet.py
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dynunet.py
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dints.py
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diffusion_model_unet.py
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densenet.py
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daf3d.py
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controlnet.py
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classifier.py
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cell_sam_wrapper.py
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basic_unetplusplus_origin.py
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basic_unetplusplus_modified_trans.py
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basic_unetplusplus_modified_aspp.py
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basic_unetplusplus_modified.py
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basic_unetplusplus.py
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basic_unet.py
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autoencoderkl.py
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autoencoder.py
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attentionunet.py
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ahnet.py
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__init__.py
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weight_init.cpython-38.pyc
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weight_init.cpython-310.pyc
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vector_quantizer.cpython-310.pyc
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utils.cpython-38.pyc
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spatial_transforms.cpython-38.pyc
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spatial_transforms.cpython-310.pyc
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simplelayers.cpython-38.pyc
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simplelayers.cpython-310.pyc
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gmm.cpython-38.pyc
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gmm.cpython-310.pyc
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filtering.cpython-38.pyc
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filtering.cpython-310.pyc
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factories.cpython-38.pyc
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factories.cpython-310.pyc
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drop_path.cpython-38.pyc
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drop_path.cpython-310.pyc
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convutils.cpython-38.pyc
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convutils.cpython-310.pyc
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conjugate_gradient.cpython-38.pyc
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conjugate_gradient.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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weight_init.py
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vector_quantizer.py
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utils.py
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spatial_transforms.py
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simplelayers.py
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gmm.py
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filtering.py
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factories.py
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drop_path.py
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convutils.py
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conjugate_gradient.py
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__init__.py
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warp.cpython-38.pyc
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warp.cpython-310.pyc
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upsample.cpython-38.pyc
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upsample.cpython-310.pyc
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unetr_block.cpython-38.pyc
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unetr_block.cpython-310.pyc
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transformerblock.cpython-38.pyc
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transformerblock.cpython-310.pyc
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text_embedding.cpython-38.pyc
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text_embedding.cpython-310.pyc
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squeeze_and_excitation.cpython-38.pyc
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squeeze_and_excitation.cpython-310.pyc
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spatialattention.cpython-310.pyc
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spade_norm.cpython-310.pyc
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selfattention.cpython-38.pyc
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selfattention.cpython-310.pyc
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segresnet_block.cpython-38.pyc
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segresnet_block.cpython-310.pyc
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rel_pos_embedding.cpython-310.pyc
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regunet_block.cpython-38.pyc
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profiling.cpython-38.pyc
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profiling.cpython-310.pyc
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ordering.cpython-310.pyc
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nvtx.cpython-38.pyc
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nvtx.cpython-310.pyc
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module.cpython-38.pyc
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module.cpython-310.pyc
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misc.cpython-38.pyc
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misc.cpython-310.pyc
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jupyter_utils.cpython-38.pyc
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enums.cpython-38.pyc
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dist.cpython-38.pyc
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deprecate_utils.cpython-38.pyc
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decorators.cpython-38.pyc
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component_store.cpython-38.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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type_conversion.py
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tf32.py
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state_cacher.py
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profiling.py
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ordering.py
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nvtx.py
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module.py
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misc.py
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jupyter_utils.py
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enums.py
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dist.py
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deprecate_utils.py
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decorators.py
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component_store.py
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aliases.py
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__init__.py
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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dictionary.py
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array.py
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__init__.py
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functional.cpython-38.pyc
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functional.cpython-310.pyc
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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functional.py
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dictionary.py
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array.py
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__init__.py
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dictionary.cpython-38.pyc
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dictionary.cpython-310.pyc
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array.cpython-38.pyc
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array.cpython-310.pyc
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__init__.cpython-38.pyc
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dictionary.py
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array.py
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regunet_block.cpython-310.pyc
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pos_embed_utils.cpython-38.pyc
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pos_embed_utils.cpython-310.pyc
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patchembedding.cpython-38.pyc
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patchembedding.cpython-310.pyc
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mlp.cpython-38.pyc
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mlp.cpython-310.pyc
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mednext_block.cpython-310.pyc
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localnet_block.cpython-38.pyc
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localnet_block.cpython-310.pyc
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fft_utils_t.cpython-38.pyc
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feature_pyramid_network.cpython-38.pyc
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feature_pyramid_network.cpython-310.pyc
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fcn.cpython-38.pyc
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lr_schedule_handler.cpython-310.pyc
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logfile_handler.cpython-38.pyc
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logfile_handler.cpython-310.pyc
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ignite_metric.cpython-38.pyc
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hausdorff_distance.cpython-38.pyc
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hausdorff_distance.cpython-310.pyc
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garbage_collector.cpython-38.pyc
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earlystop_handler.cpython-38.pyc
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decollate_batch.cpython-38.pyc
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confusion_matrix.cpython-38.pyc
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confusion_matrix.cpython-310.pyc
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clearml_handlers.cpython-38.pyc
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classification_saver.cpython-38.pyc
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checkpoint_saver.cpython-38.pyc
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checkpoint_loader.cpython-38.pyc
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checkpoint_loader.cpython-310.pyc
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average_precision.cpython-310.pyc
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__init__.cpython-38.pyc
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__init__.cpython-310.pyc
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validation_handler.py
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utils.py
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trt_handler.py
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tensorboard_handlers.py
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surface_distance.py
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stats_handler.py
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smartcache_handler.py
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roc_auc.py
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regression_metrics.py
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probability_maps.py
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postprocessing.py
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parameter_scheduler.py
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panoptic_quality.py
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nvtx_handlers.py
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mlflow_handler.py
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metrics_saver.py
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metrics_reloaded_handler.py
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metric_logger.py
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mean_iou.py
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dictionary.py
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dictionary.cpython-38.pyc
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nnunetv2_runner.cpython-38.pyc
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scheduler.py
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rectified_flow.py
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pndm.py
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ddpm.py
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ddim.py
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vqvae.cpython-310.pyc
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voxelmorph.cpython-38.pyc
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vitautoenc.cpython-38.pyc
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vista3d.cpython-310.pyc
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varautoencoder.cpython-38.pyc
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sky_full_v2.fits
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论文信息
论文标题:
Automated Source Detection in Radio Astronomy Images Using Deep Neural Networks
发表期刊:
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
标识符
CSTR:
11379.11.101756
DOI:
10.12149/101756
VO Identifier:
ivo://China-VO/paperdata/101756
发布时间:
2026-06-23
使用统计
总下载量
14330
引用
Peng Jia et al. 2026. The neural network source code and weights for target detection in radio astronomy. 版本 1.0. https://doi.org/10.12149/101756
@misc{10.12149/101756,
doi = {10.12149/101756},
url = {https://doi.org/10.12149/101756},
author = {Peng Jia},
title = {The neural network source code and weights for target detection in radio astronomy},
version = {1.0},
publisher = {Nataional Astronomical Data Center of China},
year= {2026}
}
doi = {10.12149/101756},
url = {https://doi.org/10.12149/101756},
author = {Peng Jia},
title = {The neural network source code and weights for target detection in radio astronomy},
version = {1.0},
publisher = {Nataional Astronomical Data Center of China},
year= {2026}
}
版本