Update README.md

README.md

@@ -103,202 +103,6 @@ Marrying <a href="https://github.com/IDEA-Research/GroundingDINO">Grounding DINO
 ![model_explain1](.asset/model_explan1.PNG)
 ![model_explain2](.asset/model_explan2.PNG)
 
-## :label: TODO 
-
-- [x] Release inference code and demo.
-- [x] Release checkpoints.
-- [x] Grounding DINO with Stable Diffusion and GLIGEN demos.
-- [ ] Release training codes.
-
-## :hammer_and_wrench: Install 
-
-**Note:**
-
-0. If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set. It will be compiled under CPU-only mode if no CUDA available.
-
-Please make sure following the installation steps strictly, otherwise the program may produce: 
-```bash
-NameError: name '_C' is not defined
-```
-
-If this happened, please reinstalled the groundingDINO by reclone the git and do all the installation steps again.
- 
-#### how to check cuda:
-```bash
-echo $CUDA_HOME
-```
-If it print nothing, then it means you haven't set up the path/
-
-Run this so the environment variable will be set under current shell. 
-```bash
-export CUDA_HOME=/path/to/cuda-11.3
-```
-
-Notice the version of cuda should be aligned with your CUDA runtime, for there might exists multiple cuda at the same time. 
-
-If you want to set the CUDA_HOME permanently, store it using:
-
-```bash
-echo 'export CUDA_HOME=/path/to/cuda' >> ~/.bashrc
-```
-after that, source the bashrc file and check CUDA_HOME:
-```bash
-source ~/.bashrc
-echo $CUDA_HOME
-```
-
-In this example, /path/to/cuda-11.3 should be replaced with the path where your CUDA toolkit is installed. You can find this by typing **which nvcc** in your terminal:
-
-For instance, 
-if the output is /usr/local/cuda/bin/nvcc, then:
-```bash
-export CUDA_HOME=/usr/local/cuda
-```
-**Installation:**
-
-1.Clone the GroundingDINO repository from GitHub.
-
-```bash
-git clone https://github.com/IDEA-Research/GroundingDINO.git
-```
-
-2. Change the current directory to the GroundingDINO folder.
-
-```bash
-cd GroundingDINO/
-```
-
-3. Install the required dependencies in the current directory.
-
-```bash
-pip install -e .
-```
-
-4. Download pre-trained model weights.
-
-```bash
-mkdir weights
-cd weights
-wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
-cd ..
-```
-
-## :arrow_forward: Demo
-Check your GPU ID (only if you're using a GPU)
-
-```bash
-nvidia-smi
-```
-Replace `{GPU ID}`, `image_you_want_to_detect.jpg`, and `"dir you want to save the output"` with appropriate values in the following command
-```bash
-CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \
--c groundingdino/config/GroundingDINO_SwinT_OGC.py \
--p weights/groundingdino_swint_ogc.pth \
--i image_you_want_to_detect.jpg \
--o "dir you want to save the output" \
--t "chair"
- [--cpu-only] # open it for cpu mode
-```
-
-If you would like to specify the phrases to detect, here is a demo:
-```bash
-CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \
--c groundingdino/config/GroundingDINO_SwinT_OGC.py \
--p ./groundingdino_swint_ogc.pth \
--i .asset/cat_dog.jpeg \
--o logs/1111 \
--t "There is a cat and a dog in the image ." \
---token_spans "[[[9, 10], [11, 14]], [[19, 20], [21, 24]]]"
- [--cpu-only] # open it for cpu mode
-```
-The token_spans specify the start and end positions of a phrases. For example, the first phrase is `[[9, 10], [11, 14]]`. `"There is a cat and a dog in the image ."[9:10] = 'a'`, `"There is a cat and a dog in the image ."[11:14] = 'cat'`. Hence it refers to the phrase `a cat` . Similarly, the `[[19, 20], [21, 24]]` refers to the phrase `a dog`.
-
-See the `demo/inference_on_a_image.py` for more details.
-
-**Running with Python:**
-
-```python
-from groundingdino.util.inference import load_model, load_image, predict, annotate
-import cv2
-
-model = load_model("groundingdino/config/GroundingDINO_SwinT_OGC.py", "weights/groundingdino_swint_ogc.pth")
-IMAGE_PATH = "weights/dog-3.jpeg"
-TEXT_PROMPT = "chair . person . dog ."
-BOX_TRESHOLD = 0.35
-TEXT_TRESHOLD = 0.25
-
-image_source, image = load_image(IMAGE_PATH)
-
-boxes, logits, phrases = predict(
-    model=model,
-    image=image,
-    caption=TEXT_PROMPT,
-    box_threshold=BOX_TRESHOLD,
-    text_threshold=TEXT_TRESHOLD
-)
-
-annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)
-cv2.imwrite("annotated_image.jpg", annotated_frame)
-```
-**Web UI**
-
-We also provide a demo code to integrate Grounding DINO with Gradio Web UI. See the file `demo/gradio_app.py` for more details.
-
-**Notebooks**
-
-- We release [demos](demo/image_editing_with_groundingdino_gligen.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [GLIGEN](https://github.com/gligen/GLIGEN)  for more controllable image editings.
-- We release [demos](demo/image_editing_with_groundingdino_stablediffusion.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) for image editings.
-
-## COCO Zero-shot Evaluations
-
-We provide an example to evaluate Grounding DINO zero-shot performance on COCO. The results should be **48.5**.
-
-```bash
-CUDA_VISIBLE_DEVICES=0 \
-python demo/test_ap_on_coco.py \
- -c groundingdino/config/GroundingDINO_SwinT_OGC.py \
- -p weights/groundingdino_swint_ogc.pth \
- --anno_path /path/to/annoataions/ie/instances_val2017.json \
- --image_dir /path/to/imagedir/ie/val2017
-```
-
-
-## :luggage: Checkpoints
-
-<!-- insert a table -->
-<table>
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>name</th>
-      <th>backbone</th>
-      <th>Data</th>
-      <th>box AP on COCO</th>
-      <th>Checkpoint</th>
-      <th>Config</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>1</th>
-      <td>GroundingDINO-T</td>
-      <td>Swin-T</td>
-      <td>O365,GoldG,Cap4M</td>
-      <td>48.4 (zero-shot) / 57.2 (fine-tune)</td>
-      <td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth">GitHub link</a> | <a href="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth">HF link</a></td>
-      <td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py">link</a></td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>GroundingDINO-B</td>
-      <td>Swin-B</td>
-      <td>COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO</td>
-      <td>56.7 </td>
-      <td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha2/groundingdino_swinb_cogcoor.pth">GitHub link</a>  | <a href="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth">HF link</a> 
-      <td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinB.cfg.py">link</a></td>
-    </tr>
-  </tbody>
-</table>
 
 ## :medal_military: Results