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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-nano-22k-384 |
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tags: |
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- image-classification |
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- vision |
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- boulderspot |
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- climbing |
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- aerial imagery |
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- remote sensing |
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- bouldering |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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- matthews_correlation |
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datasets: |
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- pszemraj/boulderspot |
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--- |
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# convnextv2-nano-22k-384-boulderspot |
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This is a model fine-tuned to classify whether an aerial/satellite image contains a climbing area or not. |
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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) |
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## Model description |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0340 |
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- Accuracy: 0.9883 |
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- F1: 0.9883 |
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- Precision: 0.9883 |
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- Recall: 0.9883 |
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- Matthews Correlation: 0.8962 |
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## example usage |
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```py |
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import requests |
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from PIL import Image |
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from transformers import pipeline |
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pipe = pipeline( |
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"image-classification", |
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model="pszemraj/convnextv2-nano-22k-384-boulderspot", |
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) |
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url = "https://huggingface.co/pszemraj/convnextv2-nano-22k-384-boulderspot/resolve/main/test_img_magic_wood.png?download=true" |
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image = Image.open(requests.get(url, stream=True).raw) |
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result = pipe(image)[0] |
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print(result) |
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# image.show() |
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``` |
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## Intended uses & limitations |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 7890 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Matthews Correlation | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------:| |
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| 0.1102 | 1.0 | 203 | 0.0431 | 0.9839 | 0.9840 | 0.9841 | 0.9839 | 0.8590 | |
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| 0.0559 | 2.0 | 406 | 0.0476 | 0.9839 | 0.9845 | 0.9858 | 0.9839 | 0.8709 | |
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| 0.0402 | 3.0 | 609 | 0.0464 | 0.9810 | 0.9817 | 0.9831 | 0.9810 | 0.8468 | |
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| 0.0334 | 4.0 | 813 | 0.0348 | 0.9868 | 0.9869 | 0.9870 | 0.9868 | 0.8846 | |
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| 0.0445 | 4.99 | 1015 | 0.0340 | 0.9883 | 0.9883 | 0.9883 | 0.9883 | 0.8962 | |
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### Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.4.0.dev20240328+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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