---
license: apache-2.0
base_model: facebook/convnextv2-nano-22k-384
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
- matthews_correlation
model-index:
- name: convnextv2-nano-22k-384-boulderspot-vN
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# convnextv2-nano-22k-384-boulderspot-vN

This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on an unknown 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

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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