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---
license: apache-2.0
base_model: facebook/convnextv2-nano-22k-384
tags:
- image-classification
- vision
- boulderspot
- climbing
- aerial imagery
- remote sensing
- bouldering
metrics:
- accuracy
- f1
- precision
- recall
- matthews_correlation
datasets:
- pszemraj/boulderspot
---
# convnextv2-nano-22k-384-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/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on the pszemraj/boulderspot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0340
- Accuracy: 0.9883
- F1: 0.9883
- Precision: 0.9883
- Recall: 0.9883
- Matthews Correlation: 0.8962
## example usage
```py
import requests
from PIL import Image
from transformers import pipeline
pipe = pipeline(
"image-classification",
model="pszemraj/convnextv2-nano-22k-384-boulderspot",
)
url = "https://huggingface.co/pszemraj/convnextv2-nano-22k-384-boulderspot/resolve/main/test_img_magic_wood.png?download=true"
image = Image.open(requests.get(url, stream=True).raw)
result = pipe(image)[0]
print(result)
# image.show()
```
## 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: 7890
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------:|
| 0.1102 | 1.0 | 203 | 0.0431 | 0.9839 | 0.9840 | 0.9841 | 0.9839 | 0.8590 |
| 0.0559 | 2.0 | 406 | 0.0476 | 0.9839 | 0.9845 | 0.9858 | 0.9839 | 0.8709 |
| 0.0402 | 3.0 | 609 | 0.0464 | 0.9810 | 0.9817 | 0.9831 | 0.9810 | 0.8468 |
| 0.0334 | 4.0 | 813 | 0.0348 | 0.9868 | 0.9869 | 0.9870 | 0.9868 | 0.8846 |
| 0.0445 | 4.99 | 1015 | 0.0340 | 0.9883 | 0.9883 | 0.9883 | 0.9883 | 0.8962 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.4.0.dev20240328+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|