desco

app.py

@@ -71,7 +71,7 @@ def predict(image, text):
 image = gr.inputs.Image()
 
 gr.Interface(
-    description="Object Detection in the Wild through GLIP (https://github.com/microsoft/GLIP).",
+    description="Object Recognition with DesCo",
     fn=predict,
     inputs=["image", "text"],
     outputs=[

docs/intro.md

@@ -1,10 +1 @@
-["**GLIP: Grounded Language-Image Pre-training. CVPR 2022, Best Paper Finalist**"](https://arxiv.org/abs/2112.03857)
-
-This is the HuggingFace Gradio Demo for GLIP. The model requires an image, and a text to be the inputs. The text input can either be a natural sentence description (grounding), or a simple concatenation of some random categories (object detection).
-
-The paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich.
-
-Code: https://github.com/microsoft/GLIP
-
-**News**: We are also holding an ODinW challenge at [the CV in the Wild Workshop @ ECCV 2022](https://computer-vision-in-the-wild.github.io/eccv-2022/). We hope our open-source code encourage the community to participate in this challenge!
-
+DesCo