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README.md
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The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. Based on the characteristics of this benchmark dataset, we focus on single-timestamp segmentation here. This demonstrates that our model does not require multiple timestamps during finetuning.
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### Code
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Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/)
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The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. Based on the characteristics of this benchmark dataset, we focus on single-timestamp segmentation here. This demonstrates that our model does not require multiple timestamps during finetuning.
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### Code
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Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/)
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