Instructions to use samitizerxu/swin-tiny-patch4-window7-224-finetuned-algae-rgb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samitizerxu/swin-tiny-patch4-window7-224-finetuned-algae-rgb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="samitizerxu/swin-tiny-patch4-window7-224-finetuned-algae-rgb") 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("samitizerxu/swin-tiny-patch4-window7-224-finetuned-algae-rgb") model = AutoModelForImageClassification.from_pretrained("samitizerxu/swin-tiny-patch4-window7-224-finetuned-algae-rgb") - Notebooks
- Google Colab
- Kaggle
swin-tiny-patch4-window7-224-finetuned-algae-rgb
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.9726
- eval_accuracy: 0.6180
- eval_runtime: 11.115
- eval_samples_per_second: 153.307
- eval_steps_per_second: 4.858
- epoch: 26.79
- step: 3215
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: 34
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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