|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: aesthetic_attribute_classifier |
|
results: [] |
|
widget: |
|
- text: Check your vertical on the main support; it looks a little off. I'd also like to see how it looks with a bit of the sky cropped from the photo |
|
|
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# aesthetic_attribute_classifier |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [PCCD dataset](https://github.com/ivclab/DeepPhotoCritic-ICCV17). |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3976 |
|
- Precision: {'precision': 0.877129341279301} |
|
- Recall: {'recall': 0.8751381215469614} |
|
- F1: {'f1': 0.875529982855803} |
|
- Accuracy: {'accuracy': 0.8751381215469614} |
|
|
|
## 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: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:| |
|
| 0.452 | 1.0 | 1528 | 0.4109 | {'precision': 0.8632779077963935} | {'recall': 0.8615101289134438} | {'f1': 0.8618616182904953} | {'accuracy': 0.8615101289134438} | |
|
| 0.3099 | 2.0 | 3056 | 0.3976 | {'precision': 0.877129341279301} | {'recall': 0.8751381215469614} | {'f1': 0.875529982855803} | {'accuracy': 0.8751381215469614} | |
|
| 0.227 | 3.0 | 4584 | 0.4320 | {'precision': 0.876211408446225} | {'recall': 0.874401473296501} | {'f1': 0.8747427955387239} | {'accuracy': 0.874401473296501} | |
|
| 0.1645 | 4.0 | 6112 | 0.4840 | {'precision': 0.8724641667216837} | {'recall': 0.8714548802946593} | {'f1': 0.8714577820909117} | {'accuracy': 0.8714548802946593} | |
|
| 0.1141 | 5.0 | 7640 | 0.5083 | {'precision': 0.8755445355051571} | {'recall': 0.8747697974217311} | {'f1': 0.8748766125899489} | {'accuracy': 0.8747697974217311} | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.16.2 |
|
- Pytorch 1.10.2+cu113 |
|
- Datasets 1.18.3 |
|
- Tokenizers 0.11.0 |
|
|
