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large-algae-vit-wirs
samitizerxu/large-algae-vit-wirs
large-algae-vit-wirs is a image classification model from samitizerxu. Use it when you need a label for an image. 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
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Feb 16, 2023
- Updated
- Feb 17, 2023
- SHA
- f95bb5c8
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. -->
large-algae-vit-wirs
This model is a fine-tuned version of samitizerxu/large-algae-vit-wirs on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9128
- Accuracy: 0.6209
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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1662 | 1.0 | 120 | 0.9128 | 0.6209 |
| 1.0885 | 2.0 | 240 | 0.9469 | 0.6138 |
| 1.1315 | 3.0 | 360 | 1.0919 | 0.5757 |
| 1.0542 | 4.0 | 480 | 1.2291 | 0.5599 |
| 1.028 | 5.0 | 600 | 1.1931 | 0.5599 |
| 1.0023 | 6.0 | 720 | 1.1548 | 0.5675 |
| 1.0176 | 7.0 | 840 | 1.0932 | 0.5757 |
| 0.992 | 8.0 | 960 | 1.1387 | 0.5751 |
| 0.9891 | 9.0 | 1080 | 1.2387 | 0.5464 |
| 0.9635 | 10.0 | 1200 | 1.3772 | 0.5428 |
| 0.9764 | 11.0 | 1320 | 1.4329 | 0.5258 |
| 0.9375 | 12.0 | 1440 | 1.2830 | 0.5522 |
| 0.9574 | 13.0 | 1560 | 1.4003 | 0.5229 |
| 0.9907 | 14.0 | 1680 | 1.3447 | 0.5423 |
| 0.9507 | 15.0 | 1800 | 1.2907 | 0.5604 |
| 0.9866 | 16.0 | 1920 | 1.4578 | 0.5393 |
| 0.9297 | 17.0 | 2040 | 1.4779 | 0.5282 |
| 0.9385 | 18.0 | 2160 | 1.3874 | 0.5469 |
| 0.9951 | 19.0 | 2280 | 1.2976 | 0.5587 |
| 0.9794 | 20.0 | 2400 | 1.3110 | 0.5569 |
| 0.9974 | 21.0 | 2520 | 1.3649 | 0.5276 |
| 0.9284 | 22.0 | 2640 | 1.3713 | 0.5364 |
| 0.9144 | 23.0 | 2760 | 1.4117 | 0.5340 |
| 0.9771 | 24.0 | 2880 | 1.3836 | 0.5358 |
| 0.8994 | 25.0 | 3000 | 1.5077 | 0.5282 |
| 0.9061 | 26.0 | 3120 | 1.4622 | 0.5329 |
| 0.9071 | 27.0 | 3240 | 1.4303 | 0.5393 |
| 0.9288 | 28.0 | 3360 | 1.4556 | 0.5329 |
| 0.9285 | 29.0 | 3480 | 1.3900 | 0.5446 |
| 0.8955 | 30.0 | 3600 | 1.4082 | 0.5387 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Feb 16, 2023
- Updated
- Feb 17, 2023