Microsoft Phi-Ground-Any-4B

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👁 overview

Phi-Ground-Any-4B is one of the Phi-Ground model family, finetuned from microsoft/Phi-3.5-vision-instruct with fixed input resolution 1680x1008.

Main results

👁 overview

Usage

The current transformers version can be verified with: pip list | grep transformers.

Examples of required packages:

flash_attn==2.5.8
numpy==1.24.4
Pillow==10.3.0
Requests==2.31.0
torch==2.3.0
torchvision==0.18.0
transformers==4.43.0
accelerate==0.30.0

Input Formats

The model require strict input format including fixed image resolution, instruction-first order and system prompt.

Input preprocessing

from PIL import Image


def process_image(img):
 # Phi-Ground-Anything uses a larger 5x3-tile canvas (1680 x 1008).
 target_width, target_height = 336 * 5, 336 * 3

 img_ratio = img.width / img.height
 target_ratio = target_width / target_height

 if img_ratio > target_ratio:
 new_width = target_width
 new_height = int(new_width / img_ratio)
 else:
 new_height = target_height
 new_width = int(new_height * img_ratio)
 reshape_ratio = new_width / img.width

 img = img.resize((new_width, new_height), Image.LANCZOS)
 new_img = Image.new("RGB", (target_width, target_height), (255, 255, 255))
 paste_position = (0, 0)
 new_img.paste(img, paste_position)
 return new_img, reshape_ratio


# Phi-Ground-Anything takes the user instruction directly (no "describe the
# element" wrapper) and is trained to emit the click point as
# <x>VALUE</x><y>VALUE</y>
# where VALUE is a relative coordinate in [0, 10000] over the padded canvas
# (i.e., divide by 10000 and multiply by target_width / target_height to get
# pixel coords in the padded image, then divide by reshape_ratio to recover
# coords in the ORIGINAL image).
instruction = "<your instruction>"
prompt = """<|user|> 
{instruction}<|image_1|> 
<|end|> 
<|assistant|>""".format(instruction=instruction)

image_path = "<your image path>"
original_image = Image.open(image_path).convert("RGB")
image, reshape_ratio = process_image(original_image)


# ---------------------------------------------------------------------------
# Example: parse the model output and recover original-image coordinates.
# ---------------------------------------------------------------------------
import re

target_width, target_height = 336 * 5, 336 * 3
SCALE = 10000.0

x_pattern = re.compile(r"<x>\s*(-?\d+(?:\.\d+)?)\s*</x>")
y_pattern = re.compile(r"<y>\s*(-?\d+(?:\.\d+)?)\s*</y>")


def parse_xy(model_output: str):
 xs = [float(v) for v in x_pattern.findall(model_output)]
 ys = [float(v) for v in y_pattern.findall(model_output)]
 return list(zip(xs, ys))


def to_original_pixel(rel_xy, reshape_ratio: float):
 x_rel, y_rel = rel_xy
 px = (x_rel / SCALE) * target_width / reshape_ratio
 py = (y_rel / SCALE) * target_height / reshape_ratio
 return px, py


# model_output = "<x>4823</x><y>3120</y>"
# point_orig = to_original_pixel(parse_xy(model_output)[0], reshape_ratio)

Then you can use huggingface model or vllm to inference.

Base model