metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned-hateful-meme-restructured-balanced
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.522
resnet-50-finetuned-hateful-meme-restructured-balanced
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6946
- Accuracy: 0.522
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: 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 |
|---|---|---|---|---|
| 0.6941 | 0.98 | 47 | 0.6947 | 0.494 |
| 0.6906 | 1.99 | 95 | 0.6945 | 0.492 |
| 0.6885 | 2.99 | 143 | 0.6951 | 0.492 |
| 0.6873 | 4.0 | 191 | 0.6946 | 0.5 |
| 0.6851 | 4.98 | 238 | 0.6941 | 0.516 |
| 0.6813 | 5.99 | 286 | 0.6946 | 0.522 |
| 0.6817 | 6.99 | 334 | 0.6955 | 0.508 |
| 0.6849 | 8.0 | 382 | 0.6948 | 0.52 |
| 0.6834 | 8.98 | 429 | 0.6953 | 0.508 |
| 0.6758 | 9.84 | 470 | 0.6953 | 0.516 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3