fix: add gdown for model checkpoints

app.py

@@ -4,6 +4,7 @@ import os
 import wget
 import gradio as gr
 import numpy as np
+import gdown
 from argparse import Namespace
 try:
     import detectron2
@@ -33,6 +34,11 @@ lvis_train_cat_info_path = "datasets/metadata/lvis_v1_train_cat_info.json"
 if not os.path.exists(lvis_train_cat_info_path):
     wget.download("https://docs.google.com/uc?export=download&id=17WmkAJYBK4xT-YkiXLcwIWmtfulSUtmO", out=lvis_train_cat_info_path)
 
+# download model
+model_path = "models/proxydet_swinb_w_inl.pth"
+if not os.path.exists(model_path):
+    gdown.download("https://docs.google.com/uc?export=download&id=17kUPoi-pEK7BlTBheGzWxe_DXJlg28qF", model_path)
+
 args = Namespace(
     base_cat_threshold=0.9,
     confidence_threshold=0.0,
@@ -40,13 +46,13 @@ args = Namespace(
     cpu=not torch.cuda.is_available(),
     custom_vocabulary='headphone,webcam,paper,coffe',
     input=['.assets/desk.jpg'],
-    opts=['MODEL.WEIGHTS', 'models/proxydet_swinb_w_inl.pth'],
+    opts=['MODEL.WEIGHTS', model_path],
     output='out.jpg',
     pred_all_class=False,
     video_input=None,
     vocabulary='custom',
     webcam=None,
-    zeroshot_weight_path='datasets/metadata/lvis_v1_clip_a+cname.npy'
+    zeroshot_weight_path=zs_weight_path
 )
 cfg = setup_cfg(args)
 ovd_demo = VisualizationDemo(cfg, args)
@@ -90,7 +96,7 @@ How to use?
 - Simply upload an image and enter comma separated objects (e.g., "dog,cat,headphone") which you want to detect within the image.
 Parameters:
 - You can also use the score threshold slider to set a threshold to filter out low probability predictions.
-- adjust alpha and beta value for base and novel classes, respectively. These determine <b>how much importance will you assign to the scores sourced from our proposed detection head which is trained with our proxy-novel classes</b>
+- adjust alpha and beta value for base and novel classes, respectively. These determine <b>how much importance will you assign to the scores sourced from our proposed detection head which is trained with our proxy-novel classes</b>.
     """
     demo = gr.Interface(
         query_image, 

requirements.txt

@@ -10,3 +10,4 @@ lvis
 wget==3.2
 nltk<=3.7
 numpy>=1.18.5
+gdown==4.7.3