This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 3 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2
Open-weight model · Image classification
swin-tiny-patch4-window7-224-finetuned-eurosat
by User Name UserName-6370/swin-tiny-patch4-window7-224-finetuned-eurosat
swin-tiny-patch4-window7-224-finetuned-eurosat is an open-weight model for image classification from User Name, released under Apache License 2.0. It has 28M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset.
Parameters28M
Context—
Weights110.1 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve swin-tiny-patch4-window7-224-finetuned-eurosat (28M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
Model Card
By User Name, published under apache-2.0, revision e65e2e775f23.
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 128 - lrschedulertype: linear - numepochs: 3 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2
Read User Name's full model card
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0565 - Accuracy: 0.9789
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1973 | 1.0 | 190 | 0.0896 | 0.9696 |
| 0.1299 | 2.0 | 380 | 0.0533 | 0.98 |
| 0.1129 | 3.0 | 570 | 0.0583 | 0.9781 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2
Configuration
- Architecture
- SwinForImageClassification
- Hidden size
- 768
- Model type
- swin
Identity and Version
- Repository
- UserName-6370/swin-tiny-patch4-window7-224-finetuned-eurosat
- Publisher
- User Name
- Task
- Image classification
- Modality
- Image
- Library
- transformers
- Parameters
- 28M parameters
- Languages
- Not stated by the source
- Revision
- e65e2e775f23fce9f13c70f35ca8fb2e942c700f
- First published
- 2026-10-06
- Last updated
- 2026-10-11
Files and Weights
8 files, 110.1 MB in total. The weights are 2 files totalling 110.1 MB in bin, safetensors.
Weights2 files · 110.1 MB
Configuration4 files · 1.9 KB
Documentation1 file · 1.8 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 110.1 MB | e902c8df5e4c |
| training_args.bin | Weights | 5.3 KB | e31000e20d33 |
| all_results.json | Configuration | 81 B | — |
| config.json | Configuration | 1.4 KB | — |
| eval_results.json | Configuration | 81 B | — |
| preprocessor_config.json | Configuration | 337 B | — |
| README.md | Documentation | 1.8 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 110.1 MB
Download from User Name
Released by User Name through its official repository on Hugging Face. Read the license.
Built From
- Derived from microsoft/swin-tiny-patch4-window7-224
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 110.1 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About swin-tiny-patch4-window7-224-finetuned-eurosat
How much GPU memory does swin-tiny-patch4-window7-224-finetuned-eurosat need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (28M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run swin-tiny-patch4-window7-224-finetuned-eurosat on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use swin-tiny-patch4-window7-224-finetuned-eurosat commercially?
Yes. swin-tiny-patch4-window7-224-finetuned-eurosat is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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