From 2e2c16a908adb1b61a8728ec6ac75c2048a863f3 Mon Sep 17 00:00:00 2001 From: Bartosz Date: Fri, 9 Feb 2024 11:17:19 +0100 Subject: [PATCH] Fix model zoo --- docs/source/main/model_zoo/MODEL_ZOO.md | 49 ++++++++++++------------- 1 file changed, 24 insertions(+), 25 deletions(-) diff --git a/docs/source/main/model_zoo/MODEL_ZOO.md b/docs/source/main/model_zoo/MODEL_ZOO.md index 017b224..6f155e5 100644 --- a/docs/source/main/model_zoo/MODEL_ZOO.md +++ b/docs/source/main/model_zoo/MODEL_ZOO.md @@ -8,43 +8,42 @@ The [Model ZOO](https://chmura.put.poznan.pl/s/2pJk4izRurzQwu3) is a collection ## Segmentation models -| Model | Input size | CM/PX | Description | Example image | -|----------------------------------------------------------------------------------|------------|-------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| -| [Corn Field Damage Segmentation](https://chmura.put.poznan.pl/s/abWFTVYSDIcncWs) | 512 | 3 | [PUT Vision](https://putvision.github.io/) model for Corn Field Damage Segmentation created on own dataset labeled by experts. We used the classical UNet++ model. It generates 3 outputs: healthy crop, damaged crop, and out-of-field area. | [Image](https://chmura.put.poznan.pl/s/i5WVmcfqPNdBTAQ) | -| [Land Cover Segmentation](https://chmura.put.poznan.pl/s/PnAFJw27uneROkV) | 512 | 40 | The model is trained on the [LandCover.ai dataset](https://landcover.ai.linuxpolska.com/). It provides satellite images with 25 cm/px and 50 cm/px resolution. Annotation masks for the following classes are provided for the images: building (1), woodland (2), water(3), road(4). We use `DeepLabV3+` model with `tu-semnasnet_100` backend and `FocalDice` as a loss function. NOTE: the dataset covers only the area of Poland, therefore the performance may be inferior in other parts of the world. | [Image](https://chmura.put.poznan.pl/s/Xa29vnieNQTvSt5) | -| [Buildings Segmentation](https://chmura.put.poznan.pl/s/MwhgQNhyQF3fuBs) | 256 | 40 | Trained on the [RampDataset dataset](https://cmr.earthdata.nasa.gov/search/concepts/C2781412367-MLHUB.html). Annotation masks for buildings and background. Xunet network. Val F1-score 81.0 | [Image](https://chmura.put.poznan.pl/s/XCjuDKDS3FFovDl) | -| [Land Cover Segmentation Sentinel-2](https://chmura.put.poznan.pl/s/UbljXBr1XSc9hCL) | 64 | 1000 | Trained on the [Eurosat dataset](https://www.tensorflow.org/datasets/catalog/eurosat). Uses 13 spectral bands from Sentinel-2, with 10 classes. Model ConvNeXt. | [Image](https://chmura.put.poznan.pl/s/pGR5VX6AV3hYKVl) | -| [Agriculture segmentation RGB+NIR](https://chmura.put.poznan.pl/s/wf5Ml1ZDyiVdNiy) | 256 | 30 | Trained on the [Agriculture Vision 2021 dataset](https://www.agriculture-vision.com/agriculture-vision-2021/dataset-2021). 4 channels input (RGB + NIR). 9 output classes within agricultural field (weed_cluster, waterway, ...). Uses X-UNet. | [Image](https://chmura.put.poznan.pl/s/35A5ISUxLxcK7kL) | -| [Fire risk assesment](https://chmura.put.poznan.pl/s/NxKLdfdr9s9jsVA) | 384 | 100 | Trained on the FireRisk dataset (RGB data). Classifies risk of fires (ver_high, high, low, ...). Uses ConvNeXt XXL. Val F1-score 65.5. | [Image](https://chmura.put.poznan.pl/s/Ijn3VgG76NvYtDY) | -| [Roads Segmentation](https://chmura.put.poznan.pl/s/y6S3CmodPy1fYYz) | 512 | 21 | The model segments the Google Earth satellite images into 'road' and 'not-road' classes. Model works best on wide car roads, crossroads and roundabouts. | [Image](https://chmura.put.poznan.pl/s/rln6mpbjpsXWpKg) | +| Model | Input size | CM/PX | Description | Example image | +|--------------------------------------------------------------------------------------|------------|-------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| +| [Corn Field Damage Segmentation](https://chmura.put.poznan.pl/s/abWFTVYSDIcncWs) | 512 | 3 | [PUT Vision](https://putvision.github.io/) model for Corn Field Damage Segmentation created on own dataset labeled by experts. We used the classical UNet++ model. It generates 3 outputs: healthy crop, damaged crop, and out-of-field area. | [Image](https://chmura.put.poznan.pl/s/i5WVmcfqPNdBTAQ) | +| [Land Cover Segmentation](https://chmura.put.poznan.pl/s/PnAFJw27uneROkV) | 512 | 40 | The model is trained on the [LandCover.ai dataset](https://landcover.ai.linuxpolska.com/). It provides satellite images with 25 cm/px and 50 cm/px resolution. Annotation masks for the following classes are provided for the images: building (1), woodland (2), water(3), road(4). We use `DeepLabV3+` model with `tu-semnasnet_100` backend and `FocalDice` as a loss function. NOTE: the dataset covers only the area of Poland, therefore the performance may be inferior in other parts of the world. | [Image](https://chmura.put.poznan.pl/s/Xa29vnieNQTvSt5) | +| [Buildings Segmentation](https://chmura.put.poznan.pl/s/MwhgQNhyQF3fuBs) | 256 | 40 | Trained on the [RampDataset dataset](https://cmr.earthdata.nasa.gov/search/concepts/C2781412367-MLHUB.html). Annotation masks for buildings and background. Xunet network. Val F1-score 81.0 | [Image](https://chmura.put.poznan.pl/s/XCjuDKDS3FFovDl) | +| [Land Cover Segmentation Sentinel-2](https://chmura.put.poznan.pl/s/UbljXBr1XSc9hCL) | 64 | 1000 | Trained on the [Eurosat dataset](https://www.tensorflow.org/datasets/catalog/eurosat). Uses 13 spectral bands from Sentinel-2, with 10 classes. Model ConvNeXt. | [Image](https://chmura.put.poznan.pl/s/pGR5VX6AV3hYKVl) | +| [Agriculture segmentation RGB+NIR](https://chmura.put.poznan.pl/s/wf5Ml1ZDyiVdNiy) | 256 | 30 | Trained on the [Agriculture Vision 2021 dataset](https://www.agriculture-vision.com/agriculture-vision-2021/dataset-2021). 4 channels input (RGB + NIR). 9 output classes within agricultural field (weed_cluster, waterway, ...). Uses X-UNet. | [Image](https://chmura.put.poznan.pl/s/35A5ISUxLxcK7kL) | +| [Fire risk assesment](https://chmura.put.poznan.pl/s/NxKLdfdr9s9jsVA) | 384 | 100 | Trained on the FireRisk dataset (RGB data). Classifies risk of fires (ver_high, high, low, ...). Uses ConvNeXt XXL. Val F1-score 65.5. | [Image](https://chmura.put.poznan.pl/s/Ijn3VgG76NvYtDY) | +| [Roads Segmentation](https://chmura.put.poznan.pl/s/y6S3CmodPy1fYYz) | 512 | 21 | The model segments the Google Earth satellite images into 'road' and 'not-road' classes. Model works best on wide car roads, crossroads and roundabouts. | [Image](https://chmura.put.poznan.pl/s/rln6mpbjpsXWpKg) | ## Regression models | Model | Input size | CM/PX | Description | Example image | -|---------|---|---|---|---| -| | | | | | +|---------|------------|-------|-------------|---------------| +| | | | | | ## Recognition models -| Model | Input size | CM/PX | Description | Example image | -|---------|---|---|---|---| -| [NAIP Place recognition](https://chmura.put.poznan.pl/s/k7EvbNGc2udHvck) | 224 | 100 | ConvNeXt nano trained using SimSiam onn [NAIP imagery](https://earth.esa.int/eogateway/catalog/pleiades-esa-archive). Rank1-accuracy 75.0. | [Image](https://chmura.put.poznan.pl/s/UzAvz8w5ceCui9y) | -| | | | | | +| Model | Input size | CM/PX | Description | Example image | +|--------------------------------------------------------------------------|------------|-------|--------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| +| [NAIP Place recognition](https://chmura.put.poznan.pl/s/k7EvbNGc2udHvck) | 224 | 100 | ConvNeXt nano trained using SimSiam onn [NAIP imagery](https://earth.esa.int/eogateway/catalog/pleiades-esa-archive). Rank1-accuracy 75.0. | [Image](https://chmura.put.poznan.pl/s/UzAvz8w5ceCui9y) | ## Object detection models -| Model | Input size | CM/PX | Description | Example image | -|--------------------------------------------------------------------------------|------------|-------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| -| [Airbus Planes Detection](https://chmura.put.poznan.pl/s/bBIJ5FDPgyQvJ49) | 256 | 70 | YOLOv7 tiny model for object detection on satellite images. Based on the [Airbus Aircraft Detection dataset](https://www.kaggle.com/datasets/airbusgeo/airbus-aircrafts-sample-dataset). | [Image](https://chmura.put.poznan.pl/s/VfLmcWhvWf0UJfI) | -| [Airbus Oil Storage Detection](https://chmura.put.poznan.pl/s/gMundpKsYUC7sNb) | 512 | 150 | YOLOv5-m model for object detection on satellite images. Based on the [Airbus Oil Storage Detection dataset](https://www.kaggle.com/datasets/airbusgeo/airbus-oil-storage-detection-dataset). | [Image](https://chmura.put.poznan.pl/s/T3pwaKlbFDBB2C3) | -| [Aerial Cars Detection](https://chmura.put.poznan.pl/s/vgOeUN4H4tGsrGm) | 640 | 10 | YOLOv7-m model for cars detection on aerial images. Based on the [ITCVD](https://arxiv.org/pdf/1801.07339.pdf). | [Image](https://chmura.put.poznan.pl/s/cPzw1mkXlprSUIJ) | -| [UAVVaste Instance Segmentation](https://chmura.put.poznan.pl/s/v99rDlSPbyNpOCH) | 640 | 0.5 | YOLOv8-L Instance Segmentation model for litter detection on high-quality UAV images. Based on the [UAVVaste dataset](https://github.com/PUTvision/UAVVaste). | [Image](https://chmura.put.poznan.pl/s/KFQTlS2qtVnaG0q) | +| Model | Input size | CM/PX | Description | Example image | +|-----------------------------------------------------------------------------------------|------------|-------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| +| [Airbus Planes Detection](https://chmura.put.poznan.pl/s/bBIJ5FDPgyQvJ49) | 256 | 70 | YOLOv7 tiny model for object detection on satellite images. Based on the [Airbus Aircraft Detection dataset](https://www.kaggle.com/datasets/airbusgeo/airbus-aircrafts-sample-dataset). | [Image](https://chmura.put.poznan.pl/s/VfLmcWhvWf0UJfI) | +| [Airbus Oil Storage Detection](https://chmura.put.poznan.pl/s/gMundpKsYUC7sNb) | 512 | 150 | YOLOv5-m model for object detection on satellite images. Based on the [Airbus Oil Storage Detection dataset](https://www.kaggle.com/datasets/airbusgeo/airbus-oil-storage-detection-dataset). | [Image](https://chmura.put.poznan.pl/s/T3pwaKlbFDBB2C3) | +| [Aerial Cars Detection](https://chmura.put.poznan.pl/s/vgOeUN4H4tGsrGm) | 640 | 10 | YOLOv7-m model for cars detection on aerial images. Based on the [ITCVD](https://arxiv.org/pdf/1801.07339.pdf). | [Image](https://chmura.put.poznan.pl/s/cPzw1mkXlprSUIJ) | +| [UAVVaste Instance Segmentation](https://chmura.put.poznan.pl/s/v99rDlSPbyNpOCH) | 640 | 0.5 | YOLOv8-L Instance Segmentation model for litter detection on high-quality UAV images. Based on the [UAVVaste dataset](https://github.com/PUTvision/UAVVaste). | [Image](https://chmura.put.poznan.pl/s/KFQTlS2qtVnaG0q) | ## Super Resolution Models -| Model | Input size | CM/PX | Scale Factor |Description | Example image | -|--------------------------------------------------------------------------------|------------|-------|------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| -|[Residual Dense Network (RDN X2)](https://chmura.put.poznan.pl/s/cLBZpjYn3ubuoii) |64 |Trained on 10 cm/px images set it same as input data | X2 | Model originally trained by H Zhang et. al. in "[A Comparative Study on CNN-Based Single-Image Super-Resolution Techniques for Satellite Images](https://github.com/farahmand-m/satellite-image-super-resolution)" converted to onnx format | [Image](https://chmura.put.poznan.pl/s/Ruz24ZpMNg97joV) from Massachusetts Roads Dataset [Dataset in kaggle](https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset) | -|[Residual Dense Network (RDN X4)](https://chmura.put.poznan.pl/s/AaKySmOoOhxW6qZ) |64 |Trained on 10 cm/px images set it same as input data | X4 | Model originally trained by H Zhang et. al. in "[A Comparative Study on CNN-Based Single-Image Super-Resolution Techniques for Satellite Images](https://github.com/farahmand-m/satellite-image-super-resolution)" converted to onnx format | [Image](https://chmura.put.poznan.pl/s/Ruz24ZpMNg97joV) from Massachusetts Roads Dataset [Dataset in kaggle](https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset) | +| Model | Input size | CM/PX | Scale Factor | Description | Example image | +|---------------------------------------------------------------------------------------|------------|--------------------------------------------------------|--------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +|[Residual Dense Network (RDN X2)](https://chmura.put.poznan.pl/s/cLBZpjYn3ubuoii) |64 | Trained on 10 cm/px images set it same as input data | X2 | Model originally trained by H Zhang et. al. in "[A Comparative Study on CNN-Based Single-Image Super-Resolution Techniques for Satellite Images](https://github.com/farahmand-m/satellite-image-super-resolution)" converted to onnx format | [Image](https://chmura.put.poznan.pl/s/Ruz24ZpMNg97joV) from Massachusetts Roads Dataset [Dataset in kaggle](https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset) | +|[Residual Dense Network (RDN X4)](https://chmura.put.poznan.pl/s/AaKySmOoOhxW6qZ) |64 | Trained on 10 cm/px images set it same as input data | X4 | Model originally trained by H Zhang et. al. in "[A Comparative Study on CNN-Based Single-Image Super-Resolution Techniques for Satellite Images](https://github.com/farahmand-m/satellite-image-super-resolution)" converted to onnx format | [Image](https://chmura.put.poznan.pl/s/Ruz24ZpMNg97joV) from Massachusetts Roads Dataset [Dataset in kaggle](https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset) | ## Contributing