Update README.md

README.md

@@ -10,26 +10,50 @@ widget:
   example_title: Teapot
 - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
   example_title: Palace
+model-index:
+- name: dpt-hybrid-midas
+  results:
+  - task:
+      type: monocular-depth-estimation
+      name: Monocular Depth Estimation
+    dataset:
+      type: MIX 6
+      name: MIX 6
+    metrics:
+    - type: Zero-shot transfer
+      value: 11.06
+      name: Zero-shot transfer
+      config: Zero-shot transfer
+      verified: false
+
 ---
 
-# DPT-Hybrid 
+## Model Details: DPT-Hybrid 
-
-Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/isl-org/DPT). This repository hosts the "hybrid" version of the model as stated in the paper.
-
-Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team.
-
-## Model description
-
-DPT-Hybrid uses the Vision Transformer Hybrid (ViT-Hybrid) as backbone and adds a neck + head on top for monocular depth estimation.
 
+Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. 
+It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/DPT). 
+DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation.
 ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg)
 
-DPT-Hybrid diverges from DPT by using [ViT-hybrid](https://huggingface.co/google/vit-hybrid-base-bit-384) as a backbone and taking some activations from the backbone.
+This repository hosts the "hybrid" version of the model as stated in the paper. DPT-Hybrid diverges from DPT by using [ViT-hybrid](https://huggingface.co/google/vit-hybrid-base-bit-384) as a backbone and taking some activations from the backbone.
 
-## Intended uses & limitations
+The model card has been written in combination by the Hugging Face team and Intel.
 
-You can use the raw model for zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=dpt) to look for
+| Model Detail | Description |
-fine-tuned versions on a task that interests you.
+| ----------- | ----------- | 
+| Model Authors - Company | Intel | 
+| Date | December 22, 2022 | 
+| Version | 1 | 
+| Type | Computer Vision - Monocular Depth Estimation | 
+| Paper or Other Resources | [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) and [GitHub Repo](https://github.com/isl-org/DPT) | 
+| License | Apache 2.0 |
+| Questions or Comments | [Community Tab](https://huggingface.co/Intel/dpt-hybrid-midas/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)|
+
+| Intended Use | Description |
+| ----------- | ----------- | 
+| Primary intended uses | You can use the raw model for zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=dpt) to look for fine-tuned versions on a task that interests you. | 
+| Primary intended users | Anyone doing monocular depth estimation | 
+| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task.  The model should not be used to intentionally create hostile or alienating environments for people.|
 
 ### How to use
 
@@ -41,7 +65,6 @@ import numpy as np
 import requests
 import torch
 
-
 from transformers import DPTForDepthEstimation, DPTFeatureExtractor
 
 model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True)
@@ -74,6 +97,54 @@ depth.show()
 
 For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt).
 
+| Factors | Description | 
+| ----------- | ----------- | 
+| Groups | Multiple datasets compiled together | 
+| Instrumentation | - |
+| Environment | Inference completed on Intel Xeon Platinum 8280 CPU @ 2.70GHz with 8 physical cores and an NVIDIA RTX 2080 GPU. |
+| Card Prompts | Model deployment on alternate hardware and software will change model performance |
+
+| Metrics | Description | 
+| ----------- | ----------- | 
+| Model performance measures | Zero-shot Transfer |
+| Decision thresholds | - | 
+| Approaches to uncertainty and variability | - |
+
+| Training and Evaluation Data | Description | 
+| ----------- | ----------- | 
+| Datasets | The dataset is called MIX 6, and contains around 1.4M images. The model was initialized with ImageNet-pretrained weights.|
+| Motivation | To build a robust monocular depth prediction network |
+| Preprocessing | "We resize the image such that the longer side is 384 pixels and train on random square crops of size 384. ... We perform random horizontal flips for data augmentation." See [Ranftl et al. (2021)](https://arxiv.org/abs/2103.13413) for more details. | 
+
+## Quantitative Analyses
+| Model | Training set | DIW WHDR | ETH3D AbsRel | Sintel AbsRel | KITTI δ>1.25 | NYU δ>1.25 | TUM δ>1.25 |
+| --- | --- | --- | --- | --- | --- | --- | --- | 
+| DPT - Large | MIX 6 | 10.82 (-13.2%) | 0.089 (-31.2%) | 0.270 (-17.5%) | 8.46 (-64.6%) | 8.32 (-12.9%) | 9.97 (-30.3%) |
+| DPT - Hybrid | MIX 6 | 11.06 (-11.2%) | 0.093 (-27.6%) | 0.274 (-16.2%) | 11.56 (-51.6%) | 8.69 (-9.0%) | 10.89 (-23.2%) | 
+| MiDaS  | MIX 6  | 12.95 (+3.9%)  | 0.116 (-10.5%)  | 0.329 (+0.5%)  | 16.08 (-32.7%)  | 8.71 (-8.8%)  | 12.51 (-12.5%)
+| MiDaS [30]  | MIX 5  | 12.46  | 0.129  | 0.327  | 23.90  | 9.55  | 14.29 | 
+ | Li [22]  | MD [22]  | 23.15  | 0.181  | 0.385  | 36.29  | 27.52  | 29.54 | 
+ | Li [21]  | MC [21]  | 26.52  | 0.183  | 0.405  | 47.94  | 18.57  | 17.71 | 
+ | Wang [40]  | WS [40]  | 19.09  | 0.205  | 0.390  | 31.92  | 29.57  | 20.18 | 
+ | Xian [45]  | RW [45]  | 14.59  | 0.186 |  0.422  | 34.08 |  27.00 |  25.02 | 
+ | Casser [5]  | CS [8]  | 32.80  | 0.235  | 0.422  | 21.15  | 39.58  | 37.18 | 
+
+Table 1. Comparison to the state of the art on monocular depth estimation. We evaluate zero-shot cross-dataset transfer according to the
+protocol defined in [30]. Relative performance is computed with respect to the original MiDaS model [30]. Lower is better for all metrics. ([Ranftl et al., 2021](https://arxiv.org/abs/2103.13413))
+
+
+| Ethical Considerations | Description | 
+| ----------- | ----------- | 
+| Data | The training data come from multiple image datasets compiled together. |
+| Human life | The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of monocular depth image datasets. | 
+| Mitigations | No additional risk mitigation strategies were considered during model development. |
+| Risks and harms | The extent of the risks involved by using the model remain unknown. |
+| Use cases | - | 
+
+| Caveats and Recommendations |
+| ----------- | 
+| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. |
+
 ### BibTeX entry and citation info
 
 ```bibtex