dpt large

Introduction

The DPT-Large model, also known as MiDaS 3.0, is a Dense Prediction Transformer (DPT) designed for monocular depth estimation. It was developed by Intel and introduced in the paper "Vision Transformers for Dense Prediction" by Ranftl et al. The model utilizes a Vision Transformer (ViT) backbone and is trained on 1.4 million images, making it robust for depth estimation tasks.

Architecture

DPT-Large employs the Vision Transformer (ViT) as its core structure, with additional components, a neck and a head, specifically for monocular depth estimation. This architecture allows the model to predict depth from single images using a transformer-based approach.

Training

The model was trained on the MIX-6 dataset, which contains approximately 1.4 million images. Initialization was done using ImageNet-pretrained weights. The training involves resizing images to a 384-pixel longer side and utilizing random square crops for data augmentation, along with horizontal flips to enhance model robustness.

Guide: Running Locally

To run the DPT-Large model locally:

1. Install the Transformers library:

   pip install transformers
   

2. Load and use the model:

   from transformers import pipeline
   
   pipe = pipeline(task="depth-estimation", model="Intel/dpt-large")
   image = "path_to_your_image.jpg"
   result = pipe(image)
   print(result["depth"])
   

3. Alternative Implementation: For a manual setup involving image processing and model loading, use:

   from transformers import DPTImageProcessor, DPTForDepthEstimation
   import torch
   from PIL import Image
   import requests
   
   url = "http://images.cocodataset.org/val2017/000000039769.jpg"
   image = Image.open(requests.get(url, stream=True).raw)
   
   processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
   model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
   
   inputs = processor(images=image, return_tensors="pt")
   
   with torch.no_grad():
       outputs = model(**inputs)
       predicted_depth = outputs.predicted_depth
   
   prediction = torch.nn.functional.interpolate(
       predicted_depth.unsqueeze(1),
       size=image.size[::-1],
       mode="bicubic",
       align_corners=False,
   )
   
   output = prediction.squeeze().cpu().numpy()
   

4. Cloud GPU Recommendation: For faster processing, consider using cloud GPU services like AWS EC2, Google Cloud, or Azure. These platforms offer powerful GPU instances suitable for deep learning tasks.

License

The DPT-Large model is released under the Apache 2.0 license, allowing for both personal and commercial use with minimal restrictions.