diff --git a/tests/test_convnext.py b/tests/test_convnext.py
index 97000d9..c516420 100644
--- a/tests/test_convnext.py
+++ b/tests/test_convnext.py
@@ -9,12 +9,12 @@ def test_forward():
     m(torch.randn(1, 3, 224, 224))
 
 
-# def test_from_pretrained():
-#     m = ViT.from_config("Ti", 16, 224, True).eval()
-#     x = torch.randn(1, 3, 224, 224)
-#     out = m(x)
+def test_from_pretrained():
+    m = ConvNeXt.from_config("T", True).eval()
+    x = torch.randn(1, 3, 224, 224)
+    out = m(x)
 
-#     m_timm = timm.create_model("vit_tiny_patch16_224.augreg_in21k", pretrained=True, num_classes=0).eval()
-#     out_timm = m_timm(x)
+    m_timm = timm.create_model("convnext_tiny.fb_in22k", pretrained=True, num_classes=0).eval()
+    out_timm = m_timm(x)
 
-#     torch.testing.assert_close(out, out_timm, rtol=2e-5, atol=2e-5)
+    torch.testing.assert_close(out, out_timm)
diff --git a/vision_toolbox/backbones/convnext.py b/vision_toolbox/backbones/convnext.py
index 2ed5b11..a32ee9a 100644
--- a/vision_toolbox/backbones/convnext.py
+++ b/vision_toolbox/backbones/convnext.py
@@ -100,6 +100,43 @@ def from_config(variant: str, pretrained: bool = False) -> ConvNeXt:
         m = ConvNeXt(d_model, depths)
 
         if pretrained:
-            pass
+            # TODO: also add torchvision checkpoints?
+            ckpt = dict(
+                T="convnext_tiny_22k_224.pth",
+                S="convnext_small_22k_224.pth",
+                B="convnext_base_22k_224.pth",
+                L="convnext_large_22k_224.pth",
+                XL="convnext_xlarge_22k_224.pth",
+            )[variant]
+            base_url = "https://dl.fbaipublicfiles.com/convnext/"
+            state_dict = torch.hub.load_state_dict_from_url(base_url + ckpt)["model"]
+            m.load_official_ckpt(state_dict)
 
         return m
+
+    @torch.no_grad()
+    def load_official_ckpt(self, state_dict: dict[str, Tensor]) -> None:
+        def copy_(m: nn.Conv2d | nn.Linear | nn.LayerNorm, prefix: str):
+            m.weight.copy_(state_dict.pop(prefix + ".weight"))
+            m.bias.copy_(state_dict.pop(prefix + ".bias"))
+
+        copy_(self.stem[0], "downsample_layers.0.0")  # Conv2d
+        copy_(self.stem[2], "downsample_layers.0.1")  # LayerNorm
+
+        for stage_idx, stage in enumerate(self.stages):
+            if stage_idx > 0:
+                copy_(stage[0][0], f"downsample_layers.{stage_idx}.0")  # LayerNorm
+                copy_(stage[0][2], f"downsample_layers.{stage_idx}.1")  # Conv2d
+
+            for block_idx in range(1, len(stage)):
+                block: ConvNeXtBlock = stage[block_idx]
+                prefix = f"stages.{stage_idx}.{block_idx - 1}."
+
+                copy_(block.layers[1], prefix + "dwconv")
+                copy_(block.layers[3], prefix + "norm")
+                copy_(block.layers[4], prefix + "pwconv1")
+                copy_(block.layers[6], prefix + "pwconv2")
+                block.layer_scale.copy_(state_dict.pop(prefix + "gamma"))
+
+        copy_(self.head_norm, "norm")
+        assert len(state_dict) == 2