update defaults

app.py

@@ -136,11 +136,13 @@ def get_random_path(length=10):
     return path
 
 default_images = ['./images/image_0.jpg', './images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg', './images/image_5.jpg']
-default_outputs = ['./images/ncut_0.jpg', './images/ncut_1.jpg', './images/ncut_2.jpg', './images/ncut_3.jpg', './images/ncut_5.jpg']
+# default_outputs = ['./images/ncut_0.jpg', './images/ncut_1.jpg', './images/ncut_2.jpg', './images/ncut_3.jpg', './images/ncut_5.jpg']
+default_outputs = ['./images/image-1.webp', './images/image-2.webp', './images/image-3.webp', './images/image-4.webp', './images/image-5.webp']
 default_outputs_independent = ['./images/ncut_0_independent.jpg', './images/ncut_1_independent.jpg', './images/ncut_2_independent.jpg', './images/ncut_3_independent.jpg', './images/ncut_5_independent.jpg']
 
 downscaled_images = ['./images/image_0_small.jpg', './images/image_1_small.jpg', './images/image_2_small.jpg', './images/image_3_small.jpg', './images/image_5_small.jpg']
-downscaled_outputs = ['./images/ncut_0_small.jpg', './images/ncut_1_small.jpg', './images/ncut_2_small.jpg', './images/ncut_3_small.jpg', './images/ncut_5_small.jpg']
+# downscaled_outputs = ['./images/ncut_0_small.jpg', './images/ncut_1_small.jpg', './images/ncut_2_small.jpg', './images/ncut_3_small.jpg', './images/ncut_5_small.jpg']
+downscaled_outputs = default_outputs
 
 example_items = downscaled_images[:3] + downscaled_outputs[:3]
 
@@ -481,7 +483,7 @@ def make_parameters_section():
         "SAM2(sam2_hiera_b+)",
         "SAM2(sam2_hiera_t)",
     ]
-    model_dropdown = gr.Dropdown(model_names, label="Backbone", value="SAM(sam_vit_b)", elem_id="model_name")
+    model_dropdown = gr.Dropdown(model_names, label="Backbone", value="SAM2(sam2_hiera_t)", elem_id="model_name")
     layer_slider = gr.Slider(0, 11, step=1, label="Backbone: Layer index", value=11, elem_id="layer")
     node_type_dropdown = gr.Dropdown(["attn: attention output", "mlp: mlp output", "block: sum of residual"], label="Backbone: Layer type", value="block: sum of residual", elem_id="node_type", info="which feature to take from each layer?")
     num_eig_slider = gr.Slider(1, 1000, step=1, label="NCUT: Number of eigenvectors", value=100, elem_id="num_eig", info='increase for more clusters')