metadata
license: apache-2.0
base_model: NekoFi/portrait_cosu_exp3
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: portrait_cosu_exp4
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: 0.9037037037037037
- name: Precision
type: precision
value: 0.9042846124813338
- name: Recall
type: recall
value: 0.9037037037037037
- name: F1
type: f1
value: 0.9035443167305236
portrait_cosu_exp4
This model is a fine-tuned version of NekoFi/portrait_cosu_exp3 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2432
- Accuracy: 0.9037
- Precision: 0.9043
- Recall: 0.9037
- F1: 0.9035
- Confusion Matrix: [[66, 5], [8, 56]]
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Confusion Matrix |
---|---|---|---|---|---|---|---|---|
0.3876 | 1.0 | 19 | 0.3650 | 0.8370 | 0.8555 | 0.8370 | 0.8336 | [[68, 3], [19, 45]] |
0.2696 | 2.0 | 38 | 0.2479 | 0.8963 | 0.8965 | 0.8963 | 0.8962 | [[65, 6], [8, 56]] |
0.2143 | 3.0 | 57 | 0.2665 | 0.8889 | 0.8906 | 0.8889 | 0.8885 | [[66, 5], [10, 54]] |
0.1629 | 4.0 | 76 | 0.2432 | 0.9037 | 0.9043 | 0.9037 | 0.9035 | [[66, 5], [8, 56]] |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1