Update encoders_timm.rst

Author: brianhou0208

docs/encoders_timm.rst

@@ -9,7 +9,7 @@ however, not all models are supported
 
 Below is a table of suitable encoders (for DeepLabV3, DeepLabV3+, and PAN dilation support is needed also)
 
-Total number of encoders: 792 (579+213)
+Total number of encoders: 812 (593+219)
 
 .. note::
 
@@ -99,6 +99,8 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | cs3sedarknet_xdw                 |        ✅        |
 +----------------------------------+------------------+
+| cspdarknet53                     |        ✅        |
++----------------------------------+------------------+
 | cspresnet50                      |        ✅        |
 +----------------------------------+------------------+
 | cspresnet50d                     |        ✅        |
@@ -107,6 +109,14 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | cspresnext50                     |        ✅        |
 +----------------------------------+------------------+
+| darknet17                        |        ✅        |
++----------------------------------+------------------+
+| darknet21                        |        ✅        |
++----------------------------------+------------------+
+| darknet53                        |        ✅        |
++----------------------------------+------------------+
+| darknetaa53                      |        ✅        |
++----------------------------------+------------------+
 | densenet121                      |                  |
 +----------------------------------+------------------+
 | densenet161                      |                  |
@@ -189,14 +199,6 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | eca_vovnet39b                    |                  |
 +----------------------------------+------------------+
-| ecaresnet101d                    |        ✅        |
-+----------------------------------+------------------+
-| ecaresnet101d_pruned             |        ✅        |
-+----------------------------------+------------------+
-| ecaresnet200d                    |        ✅        |
-+----------------------------------+------------------+
-| ecaresnet269d                    |        ✅        |
-+----------------------------------+------------------+
 | ecaresnet26t                     |        ✅        |
 +----------------------------------+------------------+
 | ecaresnet50d                     |        ✅        |
@@ -205,6 +207,14 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | ecaresnet50t                     |        ✅        |
 +----------------------------------+------------------+
+| ecaresnet101d                    |        ✅        |
++----------------------------------+------------------+
+| ecaresnet101d_pruned             |        ✅        |
++----------------------------------+------------------+
+| ecaresnet200d                    |        ✅        |
++----------------------------------+------------------+
+| ecaresnet269d                    |        ✅        |
++----------------------------------+------------------+
 | ecaresnetlight                   |        ✅        |
 +----------------------------------+------------------+
 | ecaresnext26t_32x4d              |        ✅        |
@@ -213,10 +223,10 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | efficientnet_b0                  |        ✅        |
 +----------------------------------+------------------+
-| efficientnet_b0_g16_evos         |        ✅        |
-+----------------------------------+------------------+
 | efficientnet_b0_g8_gn            |        ✅        |
 +----------------------------------+------------------+
+| efficientnet_b0_g16_evos         |        ✅        |
++----------------------------------+------------------+
 | efficientnet_b0_gn               |        ✅        |
 +----------------------------------+------------------+
 | efficientnet_b1                  |        ✅        |
@@ -333,12 +343,12 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | ghostnet_130                     |                  |
 +----------------------------------+------------------+
-| ghostnetv2_050                   |                  |
-+----------------------------------+------------------+
 | ghostnetv2_100                   |                  |
 +----------------------------------+------------------+
 | ghostnetv2_130                   |                  |
 +----------------------------------+------------------+
+| ghostnetv2_160                   |                  |
++----------------------------------+------------------+
 | halo2botnet50ts_256              |        ✅        |
 +----------------------------------+------------------+
 | halonet26t                       |        ✅        |
@@ -711,14 +721,14 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | regnety_160                      |        ✅        |
 +----------------------------------+------------------+
-| regnety_1280                     |        ✅        |
-+----------------------------------+------------------+
-| regnety_2560                     |        ✅        |
-+----------------------------------+------------------+
 | regnety_320                      |        ✅        |
 +----------------------------------+------------------+
 | regnety_640                      |        ✅        |
 +----------------------------------+------------------+
+| regnety_1280                     |        ✅        |
++----------------------------------+------------------+
+| regnety_2560                     |        ✅        |
++----------------------------------+------------------+
 | regnetz_005                      |        ✅        |
 +----------------------------------+------------------+
 | regnetz_040                      |        ✅        |
@@ -733,12 +743,12 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | regnetz_c16_evos                 |        ✅        |
 +----------------------------------+------------------+
-| regnetz_d32                      |        ✅        |
-+----------------------------------+------------------+
 | regnetz_d8                       |        ✅        |
 +----------------------------------+------------------+
 | regnetz_d8_evos                  |        ✅        |
 +----------------------------------+------------------+
+| regnetz_d32                      |        ✅        |
++----------------------------------+------------------+
 | regnetz_e8                       |        ✅        |
 +----------------------------------+------------------+
 | repghostnet_050                  |                  |
@@ -837,12 +847,12 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | resnet50                         |        ✅        |
 +----------------------------------+------------------+
-| resnet50_gn                      |        ✅        |
-+----------------------------------+------------------+
 | resnet50_clip                    |        ✅        |
 +----------------------------------+------------------+
 | resnet50_clip_gap                |        ✅        |
 +----------------------------------+------------------+
+| resnet50_gn                      |        ✅        |
++----------------------------------+------------------+
 | resnet50_mlp                     |        ✅        |
 +----------------------------------+------------------+
 | resnet50c                        |        ✅        |
@@ -1001,6 +1011,8 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | sebotnet33ts_256                 |        ✅        |
 +----------------------------------+------------------+
+| sedarknet21                      |        ✅        |
++----------------------------------+------------------+
 | sehalonet33ts                    |        ✅        |
 +----------------------------------+------------------+
 | selecsls42                       |                  |
@@ -1045,14 +1057,6 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | seresnetaa50d                    |        ✅        |
 +----------------------------------+------------------+
-| seresnext101_32x4d               |        ✅        |
-+----------------------------------+------------------+
-| seresnext101_32x8d               |        ✅        |
-+----------------------------------+------------------+
-| seresnext101_64x4d               |        ✅        |
-+----------------------------------+------------------+
-| seresnext101d_32x8d              |        ✅        |
-+----------------------------------+------------------+
 | seresnext26d_32x4d               |        ✅        |
 +----------------------------------+------------------+
 | seresnext26t_32x4d               |        ✅        |
@@ -1061,6 +1065,14 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | seresnext50_32x4d                |        ✅        |
 +----------------------------------+------------------+
+| seresnext101_32x4d               |        ✅        |
++----------------------------------+------------------+
+| seresnext101_32x8d               |        ✅        |
++----------------------------------+------------------+
+| seresnext101_64x4d               |        ✅        |
++----------------------------------+------------------+
+| seresnext101d_32x8d              |        ✅        |
++----------------------------------+------------------+
 | seresnextaa101d_32x8d            |        ✅        |
 +----------------------------------+------------------+
 | seresnextaa201d_32x8d            |        ✅        |
@@ -1163,6 +1175,22 @@ These models typically produce feature maps at the following downsampling scales
 +----------------------------------+------------------+
 | tinynet_e                        |        ✅        |
 +----------------------------------+------------------+
+| vgg11                            |                  |
++----------------------------------+------------------+
+| vgg11_bn                         |                  |
++----------------------------------+------------------+
+| vgg13                            |                  |
++----------------------------------+------------------+
+| vgg13_bn                         |                  |
++----------------------------------+------------------+
+| vgg16                            |                  |
++----------------------------------+------------------+
+| vgg16_bn                         |                  |
++----------------------------------+------------------+
+| vgg19                            |                  |
++----------------------------------+------------------+
+| vgg19_bn                         |                  |
++----------------------------------+------------------+
 | vovnet39a                        |                  |
 +----------------------------------+------------------+
 | vovnet57a                        |                  |
@@ -1440,6 +1468,18 @@ Transformer-style models (e.g., Swin Transformer, ConvNeXt) typically produce fe
 +------------------------------------+------------------+
 | mvitv2_tiny                        |                  |
 +------------------------------------+------------------+
+| nest_base                          |                  |
++------------------------------------+------------------+
+| nest_base_jx                       |                  |
++------------------------------------+------------------+
+| nest_small                         |                  |
++------------------------------------+------------------+
+| nest_small_jx                      |                  |
++------------------------------------+------------------+
+| nest_tiny                          |                  |
++------------------------------------+------------------+
+| nest_tiny_jx                       |                  |
++------------------------------------+------------------+
 | nextvit_base                       |                  |
 +------------------------------------+------------------+
 | nextvit_large                      |                  |