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vit-base-patch16-224-in21k-finetuned-eurosat
graphcore-rahult/vit-base-patch16-224-in21k-finetuned-eurosat
vit-base-patch16-224-in21k-finetuned-eurosat is a machine learning model from graphcore-rahult. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Nov 29, 2022
- Updated
- Dec 7, 2022
- SHA
- dfad354b
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
vit-base-patch16-224-in21k-finetuned-eurosat
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0581
- Accuracy: 0.9904
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- total_eval_batch_size: 4
- 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 precision: Mixed Precision
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0804 | 1.0 | 759 | 0.1383 | 0.9741 |
| 0.0385 | 2.0 | 1518 | 0.0756 | 0.9859 |
| 0.1211 | 3.0 | 2277 | 0.0581 | 0.9904 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Nov 29, 2022
- Updated
- Dec 7, 2022