Update README.md
Browse files
README.md
CHANGED
@@ -30,6 +30,25 @@ With a pipeline of expert models, we can infer spatial relationships between obj
|
|
30 |
- **Repository:** [VQASynth](https://github.com/remyxai/VQASynth/tree/main)
|
31 |
- **Paper:** [SpatialVLM](https://arxiv.org/abs/2401.12168)
|
32 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
33 |
|
34 |
## Citation
|
35 |
```
|
|
|
30 |
- **Repository:** [VQASynth](https://github.com/remyxai/VQASynth/tree/main)
|
31 |
- **Paper:** [SpatialVLM](https://arxiv.org/abs/2401.12168)
|
32 |
|
33 |
+
## Usage
|
34 |
+
|
35 |
+
Try the `run_inference.py` script to run a quick test:
|
36 |
+
```bash
|
37 |
+
python run_inference.py --model_location remyxai/SpaceLlama3.1
|
38 |
+
--image_source "https://remyx.ai/assets/spatialvlm/warehouse_rgb.jpg"
|
39 |
+
--user_prompt "What is the distance between the man in the red hat and the pallet of boxes?"
|
40 |
+
|
41 |
+
```
|
42 |
+
|
43 |
+
## Deploy
|
44 |
+
Under the `docker` directory, you'll find a dockerized Triton Server for this model. Run the following:
|
45 |
+
|
46 |
+
```bash
|
47 |
+
docker build -f Dockerfile -t spacellava-server:latest
|
48 |
+
docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 24G spacellama3.1-server:latest
|
49 |
+
python3 client.py --image_path "https://remyx.ai/assets/spatialvlm/warehouse_rgb.jpg" \
|
50 |
+
--prompt "What is the distance between the man in the red hat and the pallet of boxes?"
|
51 |
+
```
|
52 |
|
53 |
## Citation
|
54 |
```
|