From ce734de55cfaa76d9f564e9f92497ffc1d8c59ed Mon Sep 17 00:00:00 2001 From: Benjamin Consolvo <bconsolvo@users.noreply.huggingface.co> Date: Thu, 23 Feb 2023 17:59:48 +0000 Subject: [PATCH] Update README.md (#2) - Update README.md (c188d745fbb431654ec4007bed424fbc78351a0b) --- README.md | 96 ++++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 85 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 03f409f..6d07001 100644 --- a/README.md +++ b/README.md @@ -10,24 +10,98 @@ widget: example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace + +model-index: +- name: dpt-large + results: + - task: + type: monocular-depth-estimation + name: Monocular Depth Estimation + dataset: + type: MIX 6 + name: MIX 6 + metrics: + - type: Zero-shot transfer + value: 10.82 + name: Zero-shot transfer + config: Zero-shot transfer + verified: false --- -# DPT (large-sized model) - -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). - -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 +## Model Details: DPT (large-sized model) +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. -  -## Intended uses & limitations +The model card has been written in combination by the Hugging Face team and Intel. + +| Model Detail | Description | +| ----------- | ----------- | +| Model Authors - Company | Intel | +| Date | March 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 | [MIT](https://github.com/isl-org/DPT/blob/main/LICENSE) | +| Questions or Comments | [Community Tab](https://huggingface.co/Intel/dpt-large/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. | + +| 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 | +| ----------- | +| There are no additional caveats or recommendations for this model. | -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. ### How to use