The inD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:
The highD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:
The rounD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:
The uniD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:
You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the uniD dataset appropriately and includes a citation of the uniD dataset paper.
You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the uniD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the uniD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the uniD dataset and train the published model using the uniD dataset to see if they can confirm the results.
You are working at a company. You use the uniD dataset to develop a prototype or proof-of-concept. You use the prototype based on the uniD dataset until you have an internal dataset to replace the prototype based on the uniD dataset.
You train existing neural networks on the uniD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.
You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the rounD dataset appropriately and includes a citation of the rounD dataset paper.
You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the rounD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the rounD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the rounD dataset and train the published model using the rounD dataset to see if they can confirm the results.
You are working at a company. You use the rounD dataset to develop a prototype or proof-of-concept. You use the prototype based on the rounD dataset until you have an internal dataset to replace the prototype based on the rounD dataset.
You train existing neural networks on the rounD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.
You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the inD dataset appropriately and includes a citation of the inD dataset paper.
You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the inD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the inD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the inD dataset and train the published model using the inD dataset to see if they can confirm the results.
You are working at a company. You use the inD dataset to develop a prototype or proof-of-concept. You use the prototype based on the inD dataset until you have an internal dataset to replace the prototype based on the inD dataset.
You train existing neural networks on the inD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.
You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the highD dataset appropriately and includes a citation of the highD dataset paper.
You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the highD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the highD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the highD dataset and train the published model using the highD dataset to see if they can confirm the results.
You are working at a company. You use the highD dataset to develop a prototype or proof-of-concept. You use the prototype based on the highD dataset until you have an internal dataset to replace the prototype based on the highD dataset.
You train existing neural networks on the highD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.
The exiD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:
You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the exiD dataset appropriately and includes a citation of the exiD dataset paper.
You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the exiD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the exiD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the exiD dataset and train the published model using the exiD dataset to see if they can confirm the results.
You are working at a company. You use the exiD dataset to develop a prototype or proof-of-concept. You use the prototype based on the exiD dataset until you have an internal dataset to replace the prototype based on the exiD dataset.
You train existing neural networks on the exiD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.