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@@ -116,39 +116,44 @@ configs:
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  path: data/sg_what_relation-*
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  ---
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- # Dataset Card for TaskMeAnything-v1-imageqa-random
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- <h2 align="center"> TaskMeAnything-v1-imageqa-random dataset</h2>
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  <h2 align="center"> <a href="https://www.task-me-anything.org/">🌐 Website</a> | <a href="https://arxiv.org/abs/2406.11775">πŸ“‘ Paper</a> | <a href="https://huggingface.co/collections/jieyuz2/taskmeanything-664ebf028ab2524c0380526a">πŸ€— Huggingface</a> | <a href="https://huggingface.co/spaces/zixianma/TaskMeAnything-UI">πŸ’» Interface</a></h2>
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  <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2>
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- ## TaskMeAnything-v1-Random
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- [TaskMeAnything-v1-imageqa-random](https://huggingface.co/datasets/weikaih/TaskMeAnything-v1-imageqa-random) is a dataset which using
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- randomly sampled questions from TaskMeAnything-v1, including 5,700 ImageQA questions. The dataset contains 19 splits, while each splits contains 300 questions from a specific task generator in TaskMeAnything-v1. For each row of dataset, it includes: image, question, options, answer and its corresponding task plan.
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- ## Load TaskMeAnything-v1-Random ImageQA Dataset
 
 
 
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  ```
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  import datasets
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- dataset_name = 'weikaih/TaskMeAnything-v1-imageqa-random'
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  dataset = datasets.load_dataset(dataset_name, split = TASK_GENERATOR_SPLIT)
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  ```
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- where `TASK_GENERATOR_SPLIT` is one of the task generators, eg, `random_2d_how_many`.
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  ## Evaluation Results
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  ### Overall
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/9x9dloN9fKRBj-VUJijXB.png)
 
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  ### Breakdown performance on each task types
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/8gq7G9Ky228eooi9Mt4ep.png)
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/ux-4o12LCDdyqGSLFl2CX.png)
 
 
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/oVZlgtlqDVR_oQj32ZeEj.png)
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/UEbtq1FIPfvvoYdk6UIf0.png)
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  ## Out-of-Scope Use
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  This dataset should not be used for training models.
 
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  path: data/sg_what_relation-*
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  ---
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+ # Dataset Card for TaskMeAnything-v1-imageqa-2024
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+ <h2 align="center"> TaskMeAnything-v1-imageqa-2024 benchmark dataset</h2>
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  <h2 align="center"> <a href="https://www.task-me-anything.org/">🌐 Website</a> | <a href="https://arxiv.org/abs/2406.11775">πŸ“‘ Paper</a> | <a href="https://huggingface.co/collections/jieyuz2/taskmeanything-664ebf028ab2524c0380526a">πŸ€— Huggingface</a> | <a href="https://huggingface.co/spaces/zixianma/TaskMeAnything-UI">πŸ’» Interface</a></h2>
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  <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2>
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+ ## TaskMeAnything-v1-2024
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+ [TaskMeAnything-v1-imageqa-2024](https://huggingface.co/datasets/weikaih/TaskMeAnything-v1-imageqa-2024) is a benchmark for reflecting the current progress of MLMs by `automatically` finding tasks that SOTA MLMs struggle with using the TaskMeAnything Top-K queries.
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+ This benchmark includes 3,279 2d questions, 7,095 3d questions, and 1,896 real image questions that the TaskMeAnything algorithm automatically approximated as challenging for over 12 popular MLMs.
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+
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+ The dataset contains 19 splits, while each splits contains 400+ questions from a specific task generator in TaskMeAnything-v1. For each row of dataset, it includes: image, question, options, answer and its corresponding task plan.
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+
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+ ## Load TaskMeAnything-v1-2024 ImageQA Dataset
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  ```
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  import datasets
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+ dataset_name = 'weikaih/TaskMeAnything-v1-imageqa-2024'
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  dataset = datasets.load_dataset(dataset_name, split = TASK_GENERATOR_SPLIT)
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  ```
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+ where `TASK_GENERATOR_SPLIT` is one of the task generators, eg, `2024_2d_how_many`.
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  ## Evaluation Results
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  ### Overall
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/_KadJKJSHhZXXfIfePaUg.png)
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  ### Breakdown performance on each task types
 
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/-DrQ90FuGatJE4CuHsWS9.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/6D33K2tSc1OYF4_f6YJ63.png)
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/eKzh5ghGNVrCluVmnkZW0.png)
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/sm8dAmjxsXmJu8oeqLaeQ.png)
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  ## Out-of-Scope Use
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  This dataset should not be used for training models.