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  1. Dockerfile +10 -9
  2. docs/intro.md +5 -282
  3. requirements.txt +1 -1
Dockerfile CHANGED
@@ -1,5 +1,4 @@
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- FROM pengchuanzhang/pytorch:ubuntu20.04_torch1.9-cuda11.3-nccl2.9.9
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-
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  WORKDIR /code
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@@ -7,23 +6,25 @@ COPY ./requirements.txt /code/requirements.txt
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  RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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-
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  # Set up a new user named "user" with user ID 1000
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  RUN useradd -m -u 1000 user
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-
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  # Switch to the "user" user
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  USER user
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-
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  # Set home to the user's home directory
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  ENV HOME=/home/user \
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- PATH=/home/user/.local/bin:$PATH
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-
 
 
 
 
 
 
 
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  # Set the working directory to the user's home directory
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  WORKDIR $HOME/app
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  # Copy the current directory contents into the container at $HOME/app setting the owner to the user
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  COPY --chown=user . $HOME/app
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- #CMD ["python", "main.py"]
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  CMD ["python", "app.py"]
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- #RUN main.py
 
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+ FROM python:3.9
 
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  WORKDIR /code
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  RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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  # Set up a new user named "user" with user ID 1000
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  RUN useradd -m -u 1000 user
 
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  # Switch to the "user" user
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  USER user
 
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  # Set home to the user's home directory
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  ENV HOME=/home/user \
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+ PATH=/home/user/.local/bin:$PATH \
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+ PYTHONPATH=$HOME/app \
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+ PYTHONUNBUFFERED=1 \
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+ GRADIO_ALLOW_FLAGGING=never \
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+ GRADIO_NUM_PORTS=1 \
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+ GRADIO_SERVER_NAME=0.0.0.0 \
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+ GRADIO_THEME=huggingface \
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+ SYSTEM=spaces
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+
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  # Set the working directory to the user's home directory
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  WORKDIR $HOME/app
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  # Copy the current directory contents into the container at $HOME/app setting the owner to the user
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  COPY --chown=user . $HOME/app
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  CMD ["python", "app.py"]
 
docs/intro.md CHANGED
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- <!-- HTML_TAG_START --><p><a rel="noopener nofollow" href="https://arxiv.org/abs/2112.03857">"<strong>GLIP: Grounded Language-Image Pre-training. CVPR 2022, Best Paper Finalist</strong>"</a></p>
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- <p>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).</p>
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- <p>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.</p>
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- <p>Code: <a rel="noopener nofollow" href="https://github.com/microsoft/GLIP">https://github.com/microsoft/GLIP</a></p>
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- <p><strong>News</strong>: We are also holding an ODinW challenge at <a rel="noopener nofollow" href="https://computer-vision-in-the-wild.github.io/eccv-2022/">the CV in the Wild Workshop @ ECCV 2022</a>. We hope our open-source code encourage the community to participate in this challenge!</p>
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+ ["**GLIP: Grounded Language-Image Pre-training. CVPR 2022, Best Paper Finalist**"](https://arxiv.org/abs/2112.03857)
 
 
 
 
 
 
 
 
 
 
 
 
2
 
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+ 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).
4
 
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+ 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.
 
 
 
 
 
 
 
 
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+ Code: https://github.com/microsoft/GLIP
 
 
 
 
 
 
 
 
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+ **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!
10
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
requirements.txt CHANGED
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- gradio
 
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  requests==2.27.*
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