update docs and output images

README.md

@@ -1,28 +1,27 @@
 # Language Segment-Anything
 
-Language Segment-Anything is an open-source project that combines the power of instance segmentation and text prompts to generate masks for specific objects in images. Built on the recently released Meta model, segment-anything, and the GroundingDINO detection model, it's an easy-to-use and effective tool for object detection and image segmentation.
+Language Segment-Anything is an open-source project that combines the power of instance segmentation and text prompts to generate masks for specific objects in images. Built on the recently released Meta model, Segment Anything Model 2, and the GroundingDINO detection model, it's an easy-to-use and effective tool for object detection and image segmentation.
 
 ![person.png](/assets/outputs/person.png)
 
 ## Features
 
 - Zero-shot text-to-bbox approach for object detection.
 - GroundingDINO detection model integration.
-- Easy deployment using the Lightning AI app platform.
+- SAM 2.
+- Batch inference support.
+- Easy endpoint deployment using the Lightning AI litserve platform.
 - Customizable text prompts for precise object segmentation.
 
 ## Getting Started
 
 ### Prerequisites
 
-- Python 3.7 or higher
-- torch (tested 2.0)
-- torchvision
+- Python 3.11 or higher
 
 ### Installation
 
 ```
-pip install torch torchvision
 pip install -U git+https://github.com/luca-medeiros/lang-segment-anything.git
 ```
 
@@ -31,34 +30,26 @@ Clone the repository and install the required packages:
 
 ```
 git clone https://github.com/luca-medeiros/lang-segment-anything && cd lang-segment-anything
-pip install torch torchvision
 pip install -e .
 ```
-Or use Conda
-Create a Conda environment from the `environment.yml` file:
-```
-conda env create -f environment.yml
-# Activate the new environment:
-conda activate lsa
-```
 
 #### Docker Installation
 
 Build and run the image.
 
-	```
-	docker build --tag lang-segment-anything:latest .
-	docker run --gpus all -it lang-segment-anything:latest
-	```
-
-If you want a shared folder you can add a volume with `-v <host_source_dir>:<container_dest_dir>` example: `-v ./data:/workspace/data`
+```
+git clone https://github.com/luca-medeiros/lang-segment-anything && cd lang-segment-anything
+docker build --tag lang-segment-anything:latest .
+docker run --gpus all -p 8000:8000 lang-segment-anything:latest
+```
 
 
 ### Usage
 
-To run the Lightning AI APP:
+To run the gradio APP:
 
-`lightning run app app.py`
+`python app.py`
+And open `http://0.0.0.0:8000/gradio`
 
 Use as a library:
 
@@ -68,49 +59,25 @@ from lang_sam import LangSAM
 
 model = LangSAM()
 image_pil = Image.open("./assets/car.jpeg").convert("RGB")
-text_prompt = "wheel"
-masks, boxes, phrases, logits = model.predict(image_pil, text_prompt)
-```
-
-Use with custom checkpoint:
-
-First download a model checkpoint. 
-
-```python
-from PIL import Image
-from lang_sam import LangSAM
-
-model = LangSAM("<model_type>", "<path/to/checkpoint>")
-image_pil = Image.open("./assets/car.jpeg").convert("RGB")
-text_prompt = "wheel"
-masks, boxes, phrases, logits = model.predict(image_pil, text_prompt)
+text_prompt = "wheel."
+results = model.predict([image_pil], [text_prompt])
 ```
 
 ## Examples
 
 ![car.png](/assets/outputs/car.png)
 
-![kiwi.png](/assets/outputs/kiwi.png)
-
-![person.png](/assets/outputs/person.png)
-
-## Roadmap
-
-Future goals for this project include:
-
-1. **FastAPI integration**: To streamline deployment even further, we plan to add FastAPI code to our project, making it easier for users to deploy and interact with the model.
-
-1. **Labeling pipeline**: We want to create a labeling pipeline that allows users to input both the text prompt and the image and receive labeled instance segmentation outputs. This would help users efficiently generate results for further analysis and training.
+![fruits.png](/assets/outputs/fruits.png)
 
-1. **Implement CLIP version**: To (maybe) enhance the model's capabilities and performance, we will explore the integration of OpenAI's CLIP model. This could provide improved language understanding and potentially yield better instance segmentation results.
 
 ## Acknowledgments
 
-This project is based on the following repositories:
+This project is based on/used the following repositories:
 
 - [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO)
-- [Segment-Anything](https://github.com/facebookresearch/segment-anything)
-- [Lightning AI](https://github.com/Lightning-AI/lightning)
+- [Segment-Anything](https://github.com/facebookresearch/segment-anything-2)
+- [LitServe](https://github.com/Lightning-AI/LitServe/)
+- [Supervision](https://github.com/roboflow/supervision)
 
 ## License
 

requirements.txt

@@ -1,5 +1,5 @@
-gradio==4.44.0
-litserve==0.2.2
+gradio==5.0.2
+litserve==0.2.3
 opencv-python-headless==4.10.0.84
 pydantic==2.9.2
 sam-2 @ git+https://github.com/facebookresearch/segment-anything-2@7e1596c0b6462eb1d1ba7e1492430fed95023598
@@ -10,5 +10,5 @@ uvloop==0.20.0
 
 --extra-index-url https://download.pytorch.org/whl/cu124
 torch==2.4.1
---extra-index-url https://download.pytorch.org/whl/cu113
+--extra-index-url https://download.pytorch.org/whl/cu124
 torchvision==0.19.1