Instructions to use hchcsuim/FFPP-Raw_1FPS-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hchcsuim/FFPP-Raw_1FPS-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hchcsuim/FFPP-Raw_1FPS-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hchcsuim/FFPP-Raw_1FPS-224") model = AutoModelForImageClassification.from_pretrained("hchcsuim/FFPP-Raw_1FPS-224") - Notebooks
- Google Colab
- Kaggle
FFPP-Raw_1FPS-224
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1549
- Accuracy: 0.9357
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3359 | 1.0 | 720 | 0.2850 | 0.8742 |
| 0.2596 | 2.0 | 1440 | 0.1899 | 0.9190 |
| 0.2262 | 3.0 | 2160 | 0.1549 | 0.9357 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.2.1
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for hchcsuim/FFPP-Raw_1FPS-224
Base model
microsoft/swin-tiny-patch4-window7-224Evaluation results
- Accuracy on imagefoldertest set self-reported0.936