# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ricardoSLabs/paper_model_DP_2")
model = AutoModelForImageClassification.from_pretrained("ricardoSLabs/paper_model_DP_2")Quick Links
ricardoSLabs/paper_model_DP_2
This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.2601
- Validation Loss: 0.2486
- Train Accuracy: 0.9320
- Epoch: 4
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 0.4287 | 0.3455 | 0.8473 | 0 |
| 0.3304 | 0.3161 | 0.8742 | 1 |
| 0.2992 | 0.2814 | 0.8943 | 2 |
| 0.2766 | 0.2632 | 0.9211 | 3 |
| 0.2601 | 0.2486 | 0.9320 | 4 |
Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for ricardoSLabs/paper_model_DP_2
Base model
facebook/convnextv2-tiny-1k-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ricardoSLabs/paper_model_DP_2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")