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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: convnext-tiny-224-finetuned-brs2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7924528301886793
- name: F1
type: f1
value: 0.7555555555555556
---
<!-- 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. -->
# convnext-tiny-224-finetuned-brs2
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2502
- Accuracy: 0.7925
- F1: 0.7556
- Precision (ppv): 0.8095
- Recall (sensitivity): 0.7083
- Specificity: 0.8621
- Npv: 0.7812
- Auc: 0.7852
## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.6884 | 1.89 | 100 | 0.6907 | 0.5472 | 0.4286 | 0.5 | 0.375 | 0.6897 | 0.5714 | 0.5323 |
| 0.5868 | 3.77 | 200 | 0.6604 | 0.6415 | 0.4242 | 0.7778 | 0.2917 | 0.9310 | 0.6136 | 0.6114 |
| 0.4759 | 5.66 | 300 | 0.6273 | 0.6604 | 0.5 | 0.75 | 0.375 | 0.8966 | 0.6341 | 0.6358 |
| 0.3599 | 7.55 | 400 | 0.6520 | 0.6604 | 0.5 | 0.75 | 0.375 | 0.8966 | 0.6341 | 0.6358 |
| 0.3248 | 9.43 | 500 | 0.9115 | 0.6415 | 0.4571 | 0.7273 | 0.3333 | 0.8966 | 0.6190 | 0.6149 |
| 0.3117 | 11.32 | 600 | 0.8608 | 0.6604 | 0.5263 | 0.7143 | 0.4167 | 0.8621 | 0.6410 | 0.6394 |
| 0.4208 | 13.21 | 700 | 0.8774 | 0.6792 | 0.5641 | 0.7333 | 0.4583 | 0.8621 | 0.6579 | 0.6602 |
| 0.5267 | 15.09 | 800 | 1.0131 | 0.6792 | 0.5405 | 0.7692 | 0.4167 | 0.8966 | 0.65 | 0.6566 |
| 0.234 | 16.98 | 900 | 1.1498 | 0.6981 | 0.5556 | 0.8333 | 0.4167 | 0.9310 | 0.6585 | 0.6739 |
| 0.7581 | 18.87 | 1000 | 1.0952 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.1689 | 20.75 | 1100 | 1.1653 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 |
| 0.0765 | 22.64 | 1200 | 1.1245 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 |
| 0.6287 | 24.53 | 1300 | 1.2222 | 0.6981 | 0.6 | 0.75 | 0.5 | 0.8621 | 0.6757 | 0.6810 |
| 0.0527 | 26.42 | 1400 | 1.2350 | 0.7358 | 0.6818 | 0.75 | 0.625 | 0.8276 | 0.7273 | 0.7263 |
| 0.3622 | 28.3 | 1500 | 1.1022 | 0.7547 | 0.6667 | 0.8667 | 0.5417 | 0.9310 | 0.7105 | 0.7364 |
| 0.3227 | 30.19 | 1600 | 1.1541 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.3849 | 32.08 | 1700 | 1.2818 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.4528 | 33.96 | 1800 | 1.3213 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 |
| 0.1824 | 35.85 | 1900 | 1.3171 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.0367 | 37.74 | 2000 | 1.4484 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.07 | 39.62 | 2100 | 1.3521 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0696 | 41.51 | 2200 | 1.2636 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 |
| 0.1554 | 43.4 | 2300 | 1.2225 | 0.7358 | 0.6667 | 0.7778 | 0.5833 | 0.8621 | 0.7143 | 0.7227 |
| 0.2346 | 45.28 | 2400 | 1.2627 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.097 | 47.17 | 2500 | 1.4892 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 |
| 0.2494 | 49.06 | 2600 | 1.5282 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.0734 | 50.94 | 2700 | 1.3989 | 0.7170 | 0.6341 | 0.7647 | 0.5417 | 0.8621 | 0.6944 | 0.7019 |
| 0.1077 | 52.83 | 2800 | 1.5155 | 0.6792 | 0.5641 | 0.7333 | 0.4583 | 0.8621 | 0.6579 | 0.6602 |
| 0.2456 | 54.72 | 2900 | 1.4400 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.0823 | 56.6 | 3000 | 1.4511 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 |
| 0.0471 | 58.49 | 3100 | 1.5114 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.0144 | 60.38 | 3200 | 1.4412 | 0.7925 | 0.7317 | 0.8824 | 0.625 | 0.9310 | 0.75 | 0.7780 |
| 0.1235 | 62.26 | 3300 | 1.2029 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0121 | 64.15 | 3400 | 1.4925 | 0.7358 | 0.6667 | 0.7778 | 0.5833 | 0.8621 | 0.7143 | 0.7227 |
| 0.2126 | 66.04 | 3500 | 1.3614 | 0.7547 | 0.6667 | 0.8667 | 0.5417 | 0.9310 | 0.7105 | 0.7364 |
| 0.0496 | 67.92 | 3600 | 1.2960 | 0.7736 | 0.7143 | 0.8333 | 0.625 | 0.8966 | 0.7429 | 0.7608 |
| 0.1145 | 69.81 | 3700 | 1.3763 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.1272 | 71.7 | 3800 | 1.6328 | 0.7170 | 0.5946 | 0.8462 | 0.4583 | 0.9310 | 0.675 | 0.6947 |
| 0.0007 | 73.58 | 3900 | 1.5622 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0101 | 75.47 | 4000 | 1.1811 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 |
| 0.0002 | 77.36 | 4100 | 1.8533 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 |
| 0.0423 | 79.25 | 4200 | 1.2510 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0036 | 81.13 | 4300 | 1.3443 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.0432 | 83.02 | 4400 | 1.2864 | 0.7736 | 0.7273 | 0.8 | 0.6667 | 0.8621 | 0.7576 | 0.7644 |
| 0.0021 | 84.91 | 4500 | 0.8999 | 0.7925 | 0.7755 | 0.76 | 0.7917 | 0.7931 | 0.8214 | 0.7924 |
| 0.0002 | 86.79 | 4600 | 1.3634 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 |
| 0.0044 | 88.68 | 4700 | 1.7830 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 |
| 0.0003 | 90.57 | 4800 | 1.2640 | 0.7736 | 0.7273 | 0.8 | 0.6667 | 0.8621 | 0.7576 | 0.7644 |
| 0.0253 | 92.45 | 4900 | 1.2649 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 |
| 0.0278 | 94.34 | 5000 | 1.7485 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.1608 | 96.23 | 5100 | 1.2641 | 0.8113 | 0.7727 | 0.85 | 0.7083 | 0.8966 | 0.7879 | 0.8024 |
| 0.0017 | 98.11 | 5200 | 1.6380 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 |
| 0.001 | 100.0 | 5300 | 1.2502 | 0.7925 | 0.7556 | 0.8095 | 0.7083 | 0.8621 | 0.7812 | 0.7852 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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