Downloads · 30 days
9
2% of all-time downloads
swinv2-small-patch4-window8-256-finetuned-eurosat
jaimin/swinv2-small-patch4-window8-256-finetuned-eurosat
swinv2-small-patch4-window8-256-finetuned-eurosat is a image classification model from jaimin. 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. --
All-time downloads
438
Public
Parameters
49M
9.6 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors196 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- swinv2
- Access
- Public
- Created
- Apr 9, 2024
- Updated
- Apr 11, 2024
- SHA
- 65919799
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. -->
swinv2-small-patch4-window8-256-finetuned-eurosat
This model is a fine-tuned version of microsoft/swinv2-small-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2717
- Accuracy: 0.6875
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 0.6579 | 0.6339 |
| No log | 2.0 | 8 | 0.7129 | 0.5 |
| 0.6364 | 3.0 | 12 | 0.6774 | 0.5982 |
| 0.6364 | 4.0 | 16 | 0.6584 | 0.6786 |
| 0.3486 | 5.0 | 20 | 0.6864 | 0.6786 |
| 0.3486 | 6.0 | 24 | 0.8473 | 0.6429 |
| 0.3486 | 7.0 | 28 | 0.9735 | 0.6339 |
| 0.1224 | 8.0 | 32 | 0.8121 | 0.6964 |
| 0.1224 | 9.0 | 36 | 1.2379 | 0.6429 |
| 0.0424 | 10.0 | 40 | 1.1585 | 0.6875 |
| 0.0424 | 11.0 | 44 | 1.5274 | 0.6161 |
| 0.0424 | 12.0 | 48 | 1.1415 | 0.6607 |
| 0.0353 | 13.0 | 52 | 1.4422 | 0.6518 |
| 0.0353 | 14.0 | 56 | 1.6677 | 0.625 |
| 0.0141 | 15.0 | 60 | 1.1960 | 0.6696 |
| 0.0141 | 16.0 | 64 | 1.5515 | 0.625 |
| 0.0141 | 17.0 | 68 | 1.7990 | 0.6161 |
| 0.0135 | 18.0 | 72 | 1.4437 | 0.6607 |
| 0.0135 | 19.0 | 76 | 1.2816 | 0.7054 |
| 0.0073 | 20.0 | 80 | 1.2717 | 0.6875 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Image Classification
- Library
- transformers
- Type
- swinv2
- License
- apache-2.0
- Created
- Apr 9, 2024
- Updated
- Apr 11, 2024