metadata
base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: CV_model_2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9941275167785235
CV_model_2
This model is a fine-tuned version of microsoft/dit-base-finetuned-rvlcdip on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0113
- Accuracy: 0.9941
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0374 | 0.9954 | 162 | 0.0350 | 0.9866 |
| 0.0233 | 1.9969 | 325 | 0.0258 | 0.9891 |
| 0.0253 | 2.9985 | 488 | 0.0188 | 0.9916 |
| 0.0103 | 4.0 | 651 | 0.0283 | 0.9908 |
| 0.0065 | 4.9770 | 810 | 0.0113 | 0.9941 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1