metadata
base_model: microsoft/dit-large
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: MRR_image_classification_dit_29_jan-finetuned-eurosat
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: 0.8250355618776671
MRR_image_classification_dit_29_jan-finetuned-eurosat
This model is a fine-tuned version of microsoft/dit-large on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4995
- Accuracy: 0.8250
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0588 | 1.0 | 175 | 0.8931 | 0.6622 |
| 0.7206 | 2.0 | 351 | 0.6266 | 0.7774 |
| 0.6833 | 2.99 | 525 | 0.4995 | 0.8250 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1