metadata

base_model:
  - microsoft/Florence-2-base-ft
datasets:
  - salma-remyx/PoseText
library_name: transformers
tags:
  - remyx
  - PoseEstimation
  - TextGeneration
  - MultiModal
  - VLM

Model Card for PoseFlorence-2

This model fine-tunes Florence-2-base-ft in the POSE task for body keypoint estimation using the PoseText Dataset.

Running PoseFlorence-2

import requests

import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM 


device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model = AutoModelForCausalLM.from_pretrained("remyxai/PoseeFlorence-2", trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("remyxai/PoseFlorence-2", trust_remote_code=True)

prompt = "<POSE>"

url = "https://remyx.ai/assets/spatialvlm/warehouse_rgb.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)

generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024,
    num_beams=3,
    do_sample=False
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]

parsed_answer = processor.post_process_generation(generated_text, task="<SpatialVQA>", image_size=(image.width, image.height))

print(parsed_answer)

Developed by: [remyx.ai]
Finetuned from model: [microsoft/Florence-2-base-ft]