You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

SpatialBot is a VLM with spatial understanding and reasoning abilties, by precisely understanding depth maps and using them to do high-level tasks.

In this HF repo, we provide the merged SpatialBot-3B, which is based on Phi-2 and SigLIP. It can perform well on general VLM tasks and spatial understanding benchmarks like SpatialBench.

How to use SpatialBot-3B

NOTE: We update the repo and quick start codes in 28 August, 2024. Please update your model and codes if you downloaded them before this date.

  1. Install dependencies first:
pip install torch transformers accelerate pillow numpy
  1. Run the model:
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
import numpy as np

# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')

# set device
device = 'cuda'  # or cpu

model_name = 'RussRobin/SpatialBot-3B'
offset_bos = 0

# create model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16, # float32 for cpu
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True)

# text prompt
prompt = 'What is the depth value of point <0.5,0.2>? Answer directly from depth map.'
text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image 1>\n<image 2>\n{prompt} ASSISTANT:"
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image 1>\n<image 2>\n')]
input_ids = torch.tensor(text_chunks[0] + [-201] + [-202] + text_chunks[1][offset_bos:], dtype=torch.long).unsqueeze(0).to(device)

image1 = Image.open('rgb.jpg')
image2 = Image.open('depth.png')

channels = len(image2.getbands())
if channels == 1:
    img = np.array(image2)
    height, width = img.shape
    three_channel_array = np.zeros((height, width, 3), dtype=np.uint8)
    three_channel_array[:, :, 0] = (img // 1024) * 4
    three_channel_array[:, :, 1] = (img // 32) * 8
    three_channel_array[:, :, 2] = (img % 32) * 8
    image2 = Image.fromarray(three_channel_array, 'RGB')

image_tensor = model.process_images([image1,image2], model.config).to(dtype=model.dtype, device=device)

# generate
output_ids = model.generate(
    input_ids,
    images=image_tensor,
    max_new_tokens=100,
    use_cache=True,
    repetition_penalty=1.0 # increase this to avoid chattering
)[0]

print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())

Paper:

https://arxiv.org/abs/2406.13642

GitHub repo:

https://github.com/BAAI-DCAI/SpatialBot

SpatialBench, the benchmark:

https://huggingface.co/datasets/RussRobin/SpatialBench

CKPTs for SpatialBot-3B with LoRA:

https://huggingface.co/RussRobin/SpatialBot-3B-LoRA

Downloads last month
324
Safetensors
Model size
3.18B params
Tensor type
FP16
·
Inference Examples
Inference API (serverless) does not yet support model repos that contain custom code.