Add small vision transformer weights. 77.42 top-1.

timm/models/vision_transformer.py

@@ -29,7 +29,7 @@ import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
+from .helpers import load_pretrained
 from .layers import DropPath, to_2tuple, trunc_normal_
 from .resnet import resnet26d, resnet50d
 from .registry import register_model
@@ -48,7 +48,9 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = {
     # patch models
-    'vit_small_patch16_224': _cfg(),
+    'vit_small_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
+    ),
     'vit_base_patch16_224': _cfg(),
     'vit_base_patch16_384': _cfg(input_size=(3, 384, 384)),
     'vit_base_patch32_384': _cfg(input_size=(3, 384, 384)),
@@ -271,6 +273,9 @@ class VisionTransformer(nn.Module):
 def vit_small_patch16_224(pretrained=False, **kwargs):
     model = VisionTransformer(patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3., **kwargs)
     model.default_cfg = default_cfgs['vit_small_patch16_224']
+    if pretrained:
+        load_pretrained(
+            model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
     return model