update parameters

app.py

@@ -464,9 +464,10 @@ def image_clip_feature(
     return outputs
 
 
-
 def extract_features(images, model_name="sam", node_type="block", layer=-1):
     if model_name == "SAM(sam_vit_b)":
+        if not use_cuda:
+            gr.warning("GPU not detected. Running SAM on CPU, ~30s/image.")
         return image_sam_feature(images, node_type=node_type, layer=layer)
     elif model_name == 'MobileSAM':
         return image_mobilesam_feature(images, node_type=node_type, layer=layer)
@@ -583,15 +584,15 @@ demo = gr.Interface(
         gr.Dropdown(["attn", "mlp", "block"], label="Node type", value="block", elem_id="node_type", info="attn: attention output, mlp: mlp output, block: sum of residual stream"),
         gr.Slider(0, 11, step=1, label="Layer", value=11, elem_id="layer", info="which layer of the image backbone features"),
         gr.Slider(1, 1000, step=1, label="Number of eigenvectors", value=100, elem_id="num_eig", info='increase for more object parts, decrease for whole object'),
-        gr.Slider(0.01, 1, step=0.01, label="Affinity focal gamma", value=0.3, elem_id="affinity_focal_gamma", info="decrease for more aggressive cleaning on the affinity matrix"),
+        gr.Slider(0.01, 1, step=0.01, label="Affinity focal gamma", value=0.5, elem_id="affinity_focal_gamma", info="decrease for more aggressive cleaning on the affinity matrix"),
     ],
     gr.Gallery(value=default_outputs, label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto"),
     additional_inputs=[
-        gr.Slider(100, 30000, step=100, label="num_sample (NCUT)", value=10000, elem_id="num_sample_ncut", info="for Nyström approximation"),
+        gr.Slider(100, 10000, step=100, label="num_sample (NCUT)", value=5000, elem_id="num_sample_ncut", info="for Nyström approximation"),
         gr.Slider(1, 100, step=1, label="KNN (NCUT)", value=10, elem_id="knn_ncut", info="for Nyström approximation"),
-        gr.Slider(100, 10000, step=100, label="num_sample (t-SNE)", value=1000, elem_id="num_sample_tsne", info="for Nyström approximation. Adding will slow down t-SNE quite a lot"),
+        gr.Slider(100, 1000, step=100, label="num_sample (t-SNE)", value=500, elem_id="num_sample_tsne", info="for Nyström approximation. Adding will slow down t-SNE quite a lot"),
         gr.Slider(1, 100, step=1, label="KNN (t-SNE)", value=10, elem_id="knn_tsne", info="for Nyström approximation"),
-        gr.Slider(10, 1000, step=10, label="Perplexity (t-SNE)", value=500, elem_id="perplexity", info="for t-SNE"),
+        gr.Slider(10, 500, step=10, label="Perplexity (t-SNE)", value=250, elem_id="perplexity", info="for t-SNE"),
         
     ]
 )