# Load model directly
from transformers import AutoProcessor, AutoModelForImageClassification
processor = AutoProcessor.from_pretrained("distill-lab/train_dit02_3cat_cls_5.6k_naflex")
model = AutoModelForImageClassification.from_pretrained("distill-lab/train_dit02_3cat_cls_5.6k_naflex")Quick Links
train_dit02_initials_3cat_cls_5.6k_naflex-r2
This model is a fine-tuned version of google/siglip2-so400m-patch16-naflex on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6709
- Accuracy: 0.6333
- Precision: 0.6313
- Recall: 0.6333
- F1: 0.6300
- Roc Auc: 0.7773
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-06
- train_batch_size: 96
- eval_batch_size: 96
- seed: 1337
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.3921 | 1.8868 | 100 | 0.4369 | 0.6243 | 0.5741 | 0.6243 | 0.5256 | 0.7611 |
| 0.2374 | 3.7736 | 200 | 0.4151 | 0.6558 | 0.6163 | 0.6558 | 0.6183 | 0.7908 |
| 0.1115 | 5.6604 | 300 | 0.5398 | 0.6513 | 0.6193 | 0.6513 | 0.6309 | 0.7897 |
| 0.0411 | 7.5472 | 400 | 0.6090 | 0.6277 | 0.6382 | 0.6277 | 0.6289 | 0.7803 |
| 0.026 | 9.4340 | 500 | 0.6684 | 0.6333 | 0.6330 | 0.6333 | 0.6307 | 0.7774 |
Framework versions
- Transformers 4.56.2
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for distill-lab/train_dit02_3cat_cls_5.6k_naflex
Base model
google/siglip2-so400m-patch16-naflex
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="distill-lab/train_dit02_3cat_cls_5.6k_naflex") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")