metadata
license: apache-2.0
base_model: facebook/dinov2-base-imagenet1k-1-layer
tags:
- image-classification
- vision
- boulderspot
- climbing
- aerial imagery
- remote sensing
- bouldering
metrics:
- accuracy
- f1
- precision
- recall
- matthews_correlation
datasets:
- pszemraj/boulderspot
dinov2-base-1k_1L-boulderspot
This is a model fine-tuned to classify whether an aerial/satellite image contains a climbing area or not.
You can find some images to test inference with in this old repo from the original project
Model description
This model is a fine-tuned version of facebook/dinov2-base-imagenet1k-1-layer on the pszemraj/boulderspot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0519
- Accuracy: 0.9810
- F1: 0.9809
- Precision: 0.9808
- Recall: 0.9810
- Matthews Correlation: 0.8501
Intended uses & limitations
Classification of aerial/satellite imagery, ideally with spacial resolution 10-25 cm (i.e. for 10 cm, each pixel in the image corresonds to approx. 10 cm x 10 cm area on the ground). It may be suitable outside of that, but should be validated as other resolutions were not present in the training data.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7395
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Matthews Correlation |
---|---|---|---|---|---|---|---|---|
0.1596 | 1.0 | 203 | 0.0733 | 0.9766 | 0.9759 | 0.9757 | 0.9766 | 0.8079 |
0.0635 | 2.0 | 406 | 0.1276 | 0.9474 | 0.9522 | 0.9619 | 0.9474 | 0.6845 |
0.1031 | 3.0 | 609 | 0.0602 | 0.9751 | 0.9755 | 0.9760 | 0.9751 | 0.8118 |
0.0587 | 4.0 | 813 | 0.0512 | 0.9737 | 0.9734 | 0.9732 | 0.9737 | 0.7905 |
0.038 | 4.99 | 1015 | 0.0519 | 0.9810 | 0.9809 | 0.9808 | 0.9810 | 0.8501 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.4.0.dev20240328+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2