dpt hybrid midas LLM Model

Introduction

The DPT-Hybrid-MiDaS model is a Dense Prediction Transformer designed for monocular depth estimation. Developed by Intel and introduced in the paper "Vision Transformers for Dense Prediction," it leverages a Vision Transformer (ViT) backbone with additional components for depth estimation tasks.

Architecture

The DPT-Hybrid-MiDaS employs a ViT-hybrid architecture, which integrates a Vision Transformer with additional neck and head layers. This model diverges from the standard DPT by using the ViT-hybrid backbone, incorporating specific activations from it. This hybrid structure facilitates accurate depth estimation.

Training

The model is trained on the MIX 6 dataset, comprising approximately 1.4 million images. The training process involves using ImageNet-pretrained weights, resizing images to ensure the longer side is 384 pixels, and applying random horizontal flips for data augmentation. The model's training emphasizes zero-shot transfer capabilities across various datasets.

Guide: Running Locally

To run the DPT-Hybrid-MiDaS model locally, follow these steps:

Install Dependencies: Ensure transformers, torch, and PIL are installed in your Python environment.

Load Model and Processor:

from transformers import DPTImageProcessor, DPTForDepthEstimation

image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True)

Prepare Input Image:

from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt")

Perform Inference:

with torch.no_grad():
    outputs = model(**inputs)
    predicted_depth = outputs.predicted_depth

Visualize Output:

import numpy as np
import torch

prediction = torch.nn.functional.interpolate(
    predicted_depth.unsqueeze(1),
    size=image.size[::-1],
    mode="bicubic",
    align_corners=False,
)

output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
depth.show()

For enhanced performance, consider using a cloud GPU service such as AWS EC2, Google Cloud, or Azure.