---
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
---

<!-- waddup. -->

# 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](https://github.com/pszemraj/BoulderAreaDetector/tree/cbb22bdb3373b4b72d798dedfcb28543c0dc769d/test_images)


## Model description

This model is a fine-tuned version of [facebook/dinov2-base-imagenet1k-1-layer](https://huggingface.co/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