Update README.md

README.md

@@ -24,11 +24,12 @@ And a big thanks to all GitHub sponsors who helped with some of my costs before
 ### Oct 10, 2022
 * More weights in `maxxvit` series, incl first ConvNeXt block based `coatnext` and `maxxvit` experiments:
   * `coatnext_nano_rw_224` - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm)
-  * `maxxvit_nano_rw_256` - 83.0 @ 256, 83.7 @ 320  (G) (uses ConvNeXt conv block, no BN)
+  * `maxxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.7 @ 320  (G) (uses ConvNeXt conv block, no BN)
   * `maxvit_rmlp_small_rw_224` - 84.5 @ 224, 85.1 @ 320 (G)
-  * `maxxvit_small_rw_256` - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN)
+  * `maxxvit_rmlp_small_rw_256` - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN)
   * `coatnet_rmlp_2_rw_224` - 84.6 @ 224, 85 @ 320  (T)
-
+  * NOTE: official MaxVit weights (in1k) have been released https://github.com/google-research/maxvit, some extra work needed to port and adapt since my impl was in parallel and has a few small differences
+  
 ### Sept 23, 2022
 * LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier)
   * vit_base_patch32_224_clip_laion2b