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Cataloged from microsoft/Phi-Ground-Any
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[Image blocked: 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.
[Image blocked: overview]
The current transformers version can be verified with: pip list | grep transformers.
Examples of required packages:
The model require strict input format including fixed image resolution, instruction-first order and system prompt.
Input preprocessing
Then you can use huggingface model or vllm to inference.
flash_attn==2.5.8numpy==1.24.4Pillow==10.3.0Requests==2.31.0torch==2.3.0torchvision==0.18.0transformers==4.43.0accelerate==0.30.0from 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 * 3SCALE = 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)