# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("n1hal/swinv2-plantclef")
model = AutoModelForImageClassification.from_pretrained("n1hal/swinv2-plantclef")Quick Links
swinv2-plantclef
This model is a fine-tuned version of microsoft/swinv2-base-patch4-window16-256 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0548
- Accuracy: 0.8199
- F1: 0.8190
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
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.1414 | 1.0 | 897 | 0.9819 | 0.7171 | 0.7046 |
| 0.654 | 2.0 | 1794 | 0.7608 | 0.7694 | 0.7688 |
| 0.394 | 3.0 | 2691 | 0.7461 | 0.7795 | 0.7767 |
| 0.2437 | 4.0 | 3588 | 0.7369 | 0.7917 | 0.7908 |
| 0.1428 | 5.0 | 4485 | 0.7939 | 0.7945 | 0.7929 |
| 0.0878 | 6.0 | 5382 | 0.8352 | 0.7958 | 0.7950 |
| 0.0621 | 7.0 | 6279 | 0.8802 | 0.7945 | 0.7928 |
| 0.0353 | 8.0 | 7176 | 0.9028 | 0.8011 | 0.8005 |
| 0.0241 | 9.0 | 8073 | 0.9592 | 0.8043 | 0.8045 |
| 0.0241 | 10.0 | 8970 | 1.0075 | 0.8068 | 0.8047 |
| 0.0129 | 11.0 | 9867 | 1.0254 | 0.8127 | 0.8120 |
| 0.0058 | 12.0 | 10764 | 1.0340 | 0.8162 | 0.8151 |
| 0.007 | 13.0 | 11661 | 1.0661 | 0.8165 | 0.8159 |
| 0.0052 | 14.0 | 12558 | 1.0533 | 0.8168 | 0.8166 |
| 0.0049 | 15.0 | 13455 | 1.0660 | 0.8174 | 0.8164 |
| 0.015 | 16.0 | 14352 | 1.0548 | 0.8199 | 0.8190 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0
- Datasets 3.1.0
- Tokenizers 0.20.1
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Model tree for n1hal/swinv2-plantclef
Base model
microsoft/swinv2-base-patch4-window16-256
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="n1hal/swinv2-plantclef") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")