![]() CspNet refactored with dataclass config, simplified CrossStage3 ( cs3) option.Small ResNet defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14).Official research models (w/ weights) added:.Version 0.6.7 PyPi release (/w above bug fixes and new weighs since 0.6.5).deit3 models not being able to resize pos_emb fixed.Add output_stride=8 and 16 support to ConvNeXt (dilation).cs3* weights above all trained on TPU w/ bits_and_tpu branch.All runtime benchmark and validation result csv files are finally up-to-date!.Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights.Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original ( ).More custom ConvNeXt smaller model defs with weights.'Fast Norm' support for LayerNorm and GroupNorm that avoids float32 upcast w/ AMP (uses APEX LN if available for further boost).PyramidVisionTransformer-V2 (adapted from ).MViT-V2 (multi-scale vit, adapted from ).GCVit (weights adapted from, code 100% timm re-write for license purposes).(T) = TPU trained with bits_and_tpu branch training code, (G) = GPU trained.coatnet_0_rw_224 - 82.4 (T) - NOTE timm '0' coatnets have 2 more 3rd stage blocks.Initial CoAtNet and MaxVit timm pretrained weights (working on more):.an unfinished Tensorflow version from MaxVit authors can be found.both found in maxxvit.py model def, contains numerous experiments outside scope of original papers.CoAtNet ( ) and MaxVit ( ) timm original models.Add new RelPosMlp MaxViT weight that leverages this: MaxVit window size scales with img_size by default.Add more weights in maxxvit series incl a pico (7.5M params, 1.9 GMACs), two tiny variants:.Add BEiT-v2 weights for base and large 224x224 models from.Hugging Face timm docs home now exists, look for more here in the future.LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier).NOTE: official MaxVit weights (in1k) have been released at - some extra work is needed to port and adapt since my impl was created independently of theirs and has a few small differences + the whole TF same padding fun.maxxvit_rmlp_small_rw_256 - 84.6 256, 84.9 288 (G) - could be trained better, hparams need tuning (uses ConvNeXt block, no BN).coatnext_nano_rw_224 - 82.0 224 (G) - (uses ConvNeXt conv block, no BatchNorm).More weights in maxxvit series, incl first ConvNeXt block based coatnext and maxxvit experiments:.main branch switched to 0.7.x version, 0.6x forked for stable release of weight only adds.AMP args changed, APEX via -amp-impl apex, bfloat16 supportedf via -amp-dtype bfloat16.Dataset 'parsers' renamed to 'readers', more descriptive of purpose.Can enable per-step LR scheduling via args.in_chans !=3 support for scripts / loader.TFDS/WDS dataloading improvements (sample padding/wrap for distributed use fixed wrt sample count estimate).Train and validation script enhancements.3.7.12E_SIMP & CUDA_圆4_ to the following for hardware support:Īnd a big thanks to all GitHub sponsors who helped with some of my costs before I joined Hugging Face. It allows you to perform very precise boolean operations! It is extremely helpful for architecture, hardsurface modeling, sometimes – for organic elements (scales, teeth). ![]() The scene in the viewport will also be rendered faster because of much less video memory consumption. It opens the possibility to use one volume multiple times in the scene without additional memory consumption. Check new Instancer tool + new check box in Merge tool. Supports 3dsMax, Lightwave, Maya, Modo, Cinema 4D, Blender, ZBrush, Unity 3D, Softimage XSI, Houdini, Messiah, Cheetah 3D, Fusion, and VoidWorld. Transfer your content between 3D-Coat and your 3D modeller in no time. ![]() The main intention of this release is to summarize all what was done for last year and to release AppLinks. The main purpose of this release to summarize all that was done last year and fix minor bugs.ģD-Coat 3.7 released. ![]()
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