Update README.md

README.md

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 The released checkpoint contains a self-supervised ResNet-18 encoder and SimCLR projection head rather than a trained classification head. For image classification, users can attach and train an appropriate classifier on the learned backbone representations or fine-tune the encoder for the target task.
 
-## Limitations
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-The checkpoint does not contain a trained ImageNet-100 classification head. Classification or other supervised applications therefore require an additional prediction head, linear probe, or model fine-tuning.
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-The exact installed version of Lightly used during training was not stored in the checkpoint. The PyTorch Lightning checkpoint metadata records version `2.6.1`.
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-Exact reproduction of the analyses reported in the associated study also requires the corresponding preprocessing, feature-extraction, linear-probe, and NSD encoding-model procedures.
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 ## Related Models
 
 This model was used as a non-egocentric reference model in the study associated with the **[Eccentricity-Constrained SimCLR Models (VEDB)](https://hf.co/collections/DM-Diaz/eccentricity-constrained-simclr-models-vedb)** collection.