Update README.md

README.md

@@ -34,7 +34,6 @@ The models are trained at 256x256 (working on 384 variants) image resolution.
 
 At 256x256, the ConvNext-Large-D used roughly 1/2 the training FLOPs to achieve accuracy greater than previous L/14 model trained on LAION-2B. L/14 model is ~1.65x more GMAC, 1.45x more activations, and 1.22x more parameters. The ConvNeXt was trained with 26B samples-seen and L/14 with 34B.
 
-All models in this series were trained for 13B samples and have ImageNet Zero-Shot top-1 of >= 70.8%. Comparing to ViT-B/16 at 34B SS with zero-shot of 70.2% (68.1% for 13B SS) this suggests the ConvNeXt architecture may be more sample efficient in this range of model scale. More experiments needed to confirm.
 
 | Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) |
 | ----- | ------- | ---------- | ------------ | --------- |