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-lowerLR
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: 0.492
resnet-50-finetuned-hateful-meme-restructured-lowerLR
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: 0.6967
- Accuracy: 0.492
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-07
- 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 |
|---|---|---|---|---|
| 0.7071 | 0.99 | 66 | 0.6967 | 0.492 |
| 0.7052 | 2.0 | 133 | 0.6969 | 0.484 |
| 0.7058 | 2.99 | 199 | 0.6961 | 0.484 |
| 0.7024 | 4.0 | 266 | 0.6953 | 0.47 |
| 0.7035 | 4.99 | 332 | 0.6962 | 0.488 |
| 0.7033 | 6.0 | 399 | 0.6962 | 0.488 |
| 0.7019 | 6.99 | 465 | 0.6958 | 0.472 |
| 0.7015 | 8.0 | 532 | 0.6962 | 0.472 |
| 0.7002 | 8.99 | 598 | 0.6958 | 0.472 |
| 0.7019 | 9.92 | 660 | 0.6961 | 0.474 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3