README.md

metadata

license: other
tags:
  - generated_from_trainer
datasets:
  - image_folder
metrics:
  - accuracy
model-index:
  - name: mit-b0-finetuned-eurosat
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: image_folder
          type: image_folder
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9494949494949495

mit-b0-finetuned-eurosat

This model is a fine-tuned version of nvidia/mit-b0 on the image_folder dataset. It achieves the following results on the evaluation set:

• Loss: 0.1782
• Accuracy: 0.9495

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	7	0.3828	0.8081
0.4864	2.0	14	0.2224	0.9192
0.2035	3.0	21	0.1782	0.9495

Framework versions

• Transformers 4.17.0
• Pytorch 1.11.0+cpu
• Datasets 2.2.0
• Tokenizers 0.12.1