Datasets:
File size: 3,561 Bytes
f854df4 5b5a641 c78033d db5d191 7c0d9a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: category
dtype: string
- name: img_id
dtype: string
splits:
- name: train
num_bytes: 687610836.528
num_examples: 26872
- name: test
num_bytes: 178694171.287
num_examples: 6719
download_size: 843239857
dataset_size: 866305007.815
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- image-classification
tags:
- landscapes
- geo
- remote photos
- metric learning
pretty_name: Merged Remote Landscapes v1.0.0
size_categories:
- 10K<n<100K
---
# Dataset Card for Merged Remote Landscapes dataset
[]()
## Dataset summary
This is a merged version of following datasets:
* [torchgeo/ucmerced](https://huggingface.co/datasets/torchgeo/ucmerced)
* [NWPU-RESISC45](https://huggingface.co/datasets/jonathan-roberts1/NWPU-RESISC45)
```python
from datasets import load_dataset
dataset = load_dataset('EmbeddingStudio/merged_remote_landscapes_v1')
```
### Categories
This is a union of categories from original datasets:
agricultural, airplane, airport, baseball diamond, basketball court, beach, bridge, buildings, chaparral, church, circular farmland, cloud, commercial area, desert, forest, freeway, golf course, ground track field, harbor, industrial area, intersection, island, lake, meadow, mountain, overpass, palace, parking lot, railway, railway station, rectangular farmland, residential, river, roundabout, runway, sea ice, ship, snowberg, stadium, storage tanks, tennis court, terrace, thermal power station, wetland
Warning: Synonymous and ambiguous categories were combined (see "Merge method").
## Motivation
EmbeddingStudio is the open-source framework, that allows you transform a joint "Embedding Model + Vector DB" into a full-cycle search engine: collect clickstream -> improve search experience-> adapt embedding model and repeat out of the box.
In the development of EmbeddingStudio the scientific approach is a backbone. On the early stage of the development we can't collect real clickstream data, so to do experiments and choose the best way to improve embedding model we had to use synthetic or emulated data. And the first step is to use the most transparent datasets and the easiest domain.
P.S. this dataset is tagged to be used for the image classification task, but in fact we use it for the metric learning task. And we do another step to emulate clickstream.
We provide this dataset on HuggingFace, so anyone can reproduce our results.
Check our repositories to get more details:
* EmbeddingStudio Framework (coming soon at 22.12.2023)
* Experiments (coming soon)
## Merge method
For this type of dataset it's all simple:
1. Remove duplicates.
2. Resolve synonymous and ambiguous categories with using a simple map (CATEGORIES_MAPPING).
```python
CATEGORIES_MAPPING = {
"dense residential": "residential",
"medium residential": "residential",
"mobile home park": "residential",
"sparse residential": "residential",
"storage tank": "storage tanks",
"storage tanks": "storage tanks",
}
```
All details and code base of merging algorithm will be provided in our experiments repository. If you have any suggestion or you find some mistakes, we will be happy to fix it, so our experimental data will have better quality.
## Contact info
* Alexander Yudaev [email]([email protected] ) [LikedIn](https://www.linkedin.com/in/alexanderyudaev/) |