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config.json

@@ -1,7 +1,11 @@
 {
   "architectures": [
-    "LSPDETR"
+    "LSPDetrModel"
   ],
+  "auto_map": {
+    "AutoConfig": "configuration.LSPDetrConfig",
+    "AutoModelForObjectDetection": "modeling.LSPDetrModel"
+  },
   "backbone": "microsoft/swinv2-tiny-patch4-window16-256",
   "depths": [
     6,
@@ -16,7 +20,7 @@
     192,
     96
   ],
-  "model_type": "lspdetr",
+  "model_type": "lsp_detr",
   "num_classes": 2,
   "num_heads": 12,
   "num_radial_distances": 64,

configuration.py

@@ -1,8 +1,8 @@
 from transformers import PretrainedConfig
 
 
-class LSPDETRConfig(PretrainedConfig):
-    model_type = "LSP-DETR"
+class LSPDetrConfig(PretrainedConfig):
+    model_type = "lsp_detr"
 
     def __init__(
         self,

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b709eb65d0a8b89794d9472f9ee3b6e2a6217ce7e0a4e68c3370a9183b27485b
+oid sha256:34df87bc194a31875e6fad557746c2c5e94f027039211df60054807f61107bd0
 size 205650424

modeling.py

@@ -8,7 +8,7 @@ from torch.nn.utils import parametrize
 from transformers import PreTrainedModel, Swinv2Backbone
 from transformers.models.swinv2.modeling_swinv2 import window_partition, window_reverse
 
-from .configuration import LSPDETRConfig
+from .configuration import LSPDetrConfig
 
 
 def init_freqs(head_dim: int, num_heads: int, pos_dim: int, theta: float) -> Tensor:
@@ -643,8 +643,10 @@ class LSPTransformer(nn.Module):
         return ref_points.unsqueeze(-2) + polar
 
 
-class LSPDETR(PreTrainedModel):
-    def __init__(self, config: LSPDETRConfig) -> None:
+class LSPDetrModel(PreTrainedModel):
+    config_class = LSPDetrConfig
+
+    def __init__(self, config: LSPDetrConfig) -> None:
         super().__init__(config)
 
         self.backbone = Swinv2Backbone.from_pretrained(