Update README.md, missed recent regnetz results

README.md

@@ -27,7 +27,7 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 * A number of updated weights anew new model defs
   * `eca_halonext26ts` - 79.5 @ 256
   * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288
-  * `resnet50` - 80.68 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth))
+  * `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth))
   * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288
   * `sebotnet33ts_256` (new) - 81.2 @ 224
   * `lamhalobotnet50ts_256` - 81.5 @ 256
@@ -36,6 +36,8 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
   * `resnet101` - 82.0 @ 224, 82.8 @ 288
   * `resnetv2_101` (new) - 82.1 @ 224, 83.0 @ 288
   * `resnet152` - 82.8 @ 224, 83.5 @ 288
+  * `regnetz_d8` (new) - 83.5 @ 256, 84.0 @ 320
+  * `regnetz_e8` (new) - 84.5 @ 256, 85.0 @ 320
 * `vit_base_patch8_224` (85.8 top-1) & `in21k` variant weights added thanks [Martins Bruveris](https://github.com/martinsbruveris)
 * Groundwork in for FX feature extraction thanks to [Alexander Soare](https://github.com/alexander-soare)
   * models updated for tracing compatibility (almost full support with some distlled transformer exceptions)