Wild-Anim Dataset
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The Wild-Anim dataset available from this page consists of 5 classes that contains uniformly distributed images examples of wild animals. In total, the dataset contains 5000 images. The images are slightly anamorphic in their representation.  These images were passed as input to both deep learning and classical computer vision methods for carrying out animal recognition task.   In our experiments,  we considered two subsets each containing  1000 examples (non-repetitive images) from the total image examples' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Computer vision","Deep learning","Feature extraction","Machine learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The Wild-Anim dataset available from this page consists of 5 classes that contains uniformly distributed images examples of wild animals. In total, the dataset contains 5000 images. The images are slightly anamorphic in their representation.  These images were passed as input to both deep learning and classical computer vision methods for carrying out animal recognition task.   In our experiments,  we considered two subsets each containing  1000 examples (non-repetitive images) from the total image examples' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The Wild-Anim dataset available from this page consists of 5 classes that contains uniformly distributed images examples of wild animals. In total, the dataset contains 5000 images. The images are slightly anamorphic in their representation.  These images were passed as input to both deep learning and classical computer vision methods for carrying out animal recognition task.   In our experiments,  we considered two subsets each containing  1000 examples (non-repetitive images) from the total image examples
Computer vision; Deep learning; Feature extraction; Machine learning