Update README.md

README.md

@@ -7,27 +7,74 @@ base_model:
 tags:
 - safetensors
 ---
+
+<img src="https://takara.ai/images/logo-24/TakaraAi.svg" width="200" alt="Takara.ai Logo" />
+
+From the Frontier Research Team at **Takara.ai** we present **DepthPro-Safetensors**, a memory-efficient and optimized implementation of Apple's high-precision depth estimation model.
+
+---
+
 # DepthPro-Safetensors
 
-[https://huggingface.co/apple/DepthPro](https://huggingface.co/apple/DepthPro) converted to SafeTensors.
+This repository contains Apple's [DepthPro](https://huggingface.co/apple/DepthPro) depth estimation model converted to the SafeTensors format for improved memory efficiency, security, and faster loading times.
+
+## Model Overview
+
+DepthPro is a state-of-the-art monocular depth estimation model developed by Apple that produces sharp and accurate metric depth maps from a single image in less than a second. This converted version preserves all the capabilities of the original model while providing the benefits of the SafeTensors format.
+
+## Technical Specifications
+
+- **Total Parameters**: 951,991,330
+- **Memory Usage**: 1815.78 MB
+- **Precision**: torch.float16
+- **Estimated FLOPs**: 3,501,896,768
 
-## Details
+_Details calculated with [TensorKIKO](https://github.com/takara-ai/TensorKiko)_
 
-- Total Parameters: 951,991,330
-- Memory Usage: 1815.78 MB
-- Precisions: torch.float16
-- Estimated FLOPs: 3,501,896,768
+## Usage
 
-Details calculated with [TensorKIKO](https://github.com/takara-ai/TensorKiko)
+```python
+from transformers import AutoModelForDepthEstimation, AutoImageProcessor
+import torch
+from PIL import Image
+
+# Load model and processor
+model = AutoModelForDepthEstimation.from_pretrained("takara-ai/DepthPro-Safetensors")
+processor = AutoImageProcessor.from_pretrained("takara-ai/DepthPro-Safetensors")
+
+# Prepare image
+image = Image.open("your_image.jpg")
+inputs = processor(images=image, return_tensors="pt")
+
+# Inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_depth = outputs.predicted_depth
+
+# Post-process for visualization
+depth_map = processor.post_process_depth_estimation(outputs, target_size=image.size[::-1])
+```
+
+## Benefits of SafeTensors Format
+
+- **Improved Security**: Resistant to code execution vulnerabilities
+- **Faster Loading Times**: Optimized memory mapping for quicker model initialization
+- **Memory Efficiency**: Better handling of tensor storage for reduced memory footprint
+- **Parallel Loading**: Support for efficient parallel tensor loading
 
 ## Citation
 
 ```bibtex
 @article{Bochkovskii2024:arxiv,
   author     = {Aleksei Bochkovskii and Ama\"{e}l Delaunoy and Hugo Germain and Marcel Santos and
-               Yichao Zhou and Stephan R. Richter and Vladlen Koltun}
+               Yichao Zhou and Stephan R. Richter and Vladlen Koltun},
   title      = {Depth Pro: Sharp Monocular Metric Depth in Less Than a Second},
   journal    = {arXiv},
   year       = {2024},
 }
-```
+```
+
+---
+For research inquiries and press, please reach out to [email protected]
+
+> 人類を変革する