Update README.md

README.md

@@ -50,25 +50,11 @@ The selected images were annotated using the [Labelbox](https://labelbox.com) an
 ### Statistics
 The OmniCount-191 benchmark presents images with small, densely packed objects from multiple classes, reflecting real-world object counting scenarios. This dataset encompasses 30,230 images, with dimensions averaging 700 × 580 pixels. Each image contains an average of 10 objects, totaling 302,300 objects, with individual images ranging from 1 to 160 objects. To ensure diversity, the dataset is split into training and testing sets, with no overlap in object categories – 118 categories for training and 73 for testing, corresponding to a 60%-40% split. This results in 26,978 images for training and 3,252 for testing.
 
-#### Personal and Sensitive Information
+### Splits
+We have prepared dedicated splits within the OmniCount-191 dataset to facilitate the assessment of object counting models under zero-shot and few-shot learning conditions. Please refer to the [technical report](https://arxiv.org/pdf/2403.05435.pdf) (Sec. 9.1, 9.2) for more detais.
 
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->