File size: 3,037 Bytes
6fe6777 16d580e 6fe6777 16d580e 6fe6777 16d580e 6fe6777 16d580e 6fe6777 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: convnext-mlo-512-breat_composition-ordinal
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: preprocessed1024_config
type: preprocessed1024_config
args: default
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 0.12185929648241206
- name: F1
type: f1
value:
f1: 0.05431131019036954
---
<!-- 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-mlo-512-breat_composition-ordinal
This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0275
- Accuracy: {'accuracy': 0.12185929648241206}
- F1: {'f1': 0.05431131019036954}
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------:|:----------------------------:|
| 0.0233 | 1.0 | 796 | 0.0269 | {'accuracy': 0.042085427135678394} | {'f1': 0.02019288728149488} |
| 0.0202 | 2.0 | 1592 | 0.0250 | {'accuracy': 0.09610552763819095} | {'f1': 0.043839541547277934} |
| 0.0183 | 3.0 | 2388 | 0.0248 | {'accuracy': 0.07977386934673367} | {'f1': 0.036940081442699245} |
| 0.0163 | 4.0 | 3184 | 0.0259 | {'accuracy': 0.17022613065326633} | {'f1': 0.07273215244229736} |
| 0.0144 | 5.0 | 3980 | 0.0258 | {'accuracy': 0.146356783919598} | {'f1': 0.06383561643835617} |
| 0.0117 | 6.0 | 4776 | 0.0249 | {'accuracy': 0.0992462311557789} | {'f1': 0.045142857142857144} |
| 0.0105 | 7.0 | 5572 | 0.0256 | {'accuracy': 0.10238693467336683} | {'f1': 0.04643874643874644} |
| 0.0084 | 8.0 | 6368 | 0.0261 | {'accuracy': 0.12185929648241206} | {'f1': 0.05431131019036954} |
| 0.0071 | 9.0 | 7164 | 0.0270 | {'accuracy': 0.10238693467336683} | {'f1': 0.04643874643874644} |
| 0.0065 | 10.0 | 7960 | 0.0275 | {'accuracy': 0.12185929648241206} | {'f1': 0.05431131019036954} |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|