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MRR_image_classification_dit_29_jan-finetuned
am-infoweb/MRR_image_classification_dit_29_jan-finetuned
MRR_image_classification_dit_29_jan-finetuned is a image classification model from am-infoweb. Use it when you need a label for an image. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- Model type
- beit
- Access
- Public
- Created
- Feb 8, 2024
- Updated
- Feb 8, 2024
- SHA
- 0bfb8101
Base models
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. -->
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
- Task
- Image Classification
- Library
- transformers
- Type
- beit
- Created
- Feb 8, 2024
- Updated
- Feb 8, 2024