|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: resnet50-finetuned-memes |
|
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: 0.5741885625965997 |
|
- task: |
|
type: image-classification |
|
name: Image Classification |
|
dataset: |
|
type: custom |
|
name: custom |
|
split: test |
|
metrics: |
|
- type: f1 |
|
value: 0.47811617701687364 |
|
name: F1 |
|
- type: precision |
|
value: 0.43689216537139497 |
|
name: Precision |
|
- type: recall |
|
value: 0.5695517774343122 |
|
name: Recall |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# resnet50-finetuned-memes |
|
|
|
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.0625 |
|
- Accuracy: 0.5742 |
|
|
|
## 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: 0.00012 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 1.4795 | 0.99 | 40 | 1.4641 | 0.4382 | |
|
| 1.3455 | 1.99 | 80 | 1.3281 | 0.4389 | |
|
| 1.262 | 2.99 | 120 | 1.2583 | 0.4583 | |
|
| 1.1975 | 3.99 | 160 | 1.1978 | 0.4876 | |
|
| 1.1358 | 4.99 | 200 | 1.1614 | 0.5139 | |
|
| 1.1273 | 5.99 | 240 | 1.1316 | 0.5379 | |
|
| 1.0379 | 6.99 | 280 | 1.1024 | 0.5464 | |
|
| 1.041 | 7.99 | 320 | 1.0927 | 0.5580 | |
|
| 0.9952 | 8.99 | 360 | 1.0790 | 0.5541 | |
|
| 1.0146 | 9.99 | 400 | 1.0625 | 0.5742 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.22.1 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
|
|
