update

models/detectors/rtcdet/README.md

@@ -1,17 +1,16 @@
 # RTCDet:
 
 ## Effectiveness of the pretrained weight
-- **IN1K Cls**: We pretrained the backbone (RTCNet) on the ImageNet-1K dataset with the classification task setting.
-- **IN1K MIM**: We pretrained the backbone (RTCNet) on the ImageNet-1K dataset with the masked image modeling task setting.
+- **IN1K**: We pretrained the backbone (RTCNet) on the ImageNet-1K dataset with the classification task setting.
 - **Scratch**:  We just train the detector on the COCO without any pretrained weights for the backbone.
 
 For the small model:
 |   Model  | Pretrained | Scale | Epoch | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 |
 |----------|------------|-------|-------|------------------------|-------------------|
-| RTCDet-N | Scratch    |  640  |  500  |          36.8          |        52.9       |
-| RTCDet-N | IN1K MIM   |  640  |  500  |                        |                   |
-| RTCDet-L | Scratch    |  640  |  500  |          50.2          |        68.0       |
-| RTCDet-L | IN1K MIM   |  640  |  500  |                        |                   |
+| RTCDet-N | -          |  640  |  500  |          36.8          |        52.9       |
+| RTCDet-N | IN1K       |  640  |  500  |                        |                   |
+| RTCDet-L | -          |  640  |  500  |          50.2          |        68.0       |
+| RTCDet-L | IN1K       |  640  |  500  |                        |                   |
 
 ## Results on the COCO-val
 |   Model  | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |

models/detectors/rtcdet/rtcdet_backbone.py

@@ -9,7 +9,7 @@ except:
 
 # IN-1k MIM pretrained
 model_urls = {
-    "rtcnet_n": "https://github.com/yjh0410/MAE/releases/download/checkpoints/rtcnet_n.pth",
+    "rtcnet_n": None,
     "rtcnet_s": None,
     "rtcnet_m": None,
     "rtcnet_l": None,