Update app.py

app.py

@@ -5,8 +5,8 @@ import gradio as gr
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-owl_model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to("cuda")
-owl_processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
+owl_model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16").to("cuda")
+owl_processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16")
 
 dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
 dino_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to("cuda")
@@ -76,7 +76,7 @@ demo = gr.Interface(
     inputs=[gr.Image(label="Input Image"), gr.Textbox(label="Candidate Labels"), owl_threshold, dino_threshold],
     outputs=[owl_output, dino_output],
     title="OWLv2 ⚔ Grounding DINO",
-    description="Compare two state-of-the-art zero-shot object detection models [OWLv2](https://huggingface.co/google/owlv2-base-patch16-ensemble) and [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) in this Space. Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in each model.",
+    description="Compare two state-of-the-art zero-shot object detection models [OWLv2](https://huggingface.co/google/owlv2-base-patch16) and [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) in this Space. Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in each model.",
     examples=[["./bee.jpg", "bee, flower", 0.16, 0.12], ["./cats.png", "cat, fishnet", 0.16, 0.12]]
 )
 demo.launch(debug=True)