Image Classification
Transformers
TensorBoard
Safetensors
dinov2
Generated from Trainer
Eval Results (legacy)
Instructions to use AlaaHussien/dinov2-base-tissue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlaaHussien/dinov2-base-tissue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AlaaHussien/dinov2-base-tissue") 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("AlaaHussien/dinov2-base-tissue") model = AutoModelForImageClassification.from_pretrained("AlaaHussien/dinov2-base-tissue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dinov2-base-tissue
This model is a fine-tuned version of facebook/dinov2-base on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0200
- Accuracy: 0.9952
- F1: 0.9952
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
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Model tree for AlaaHussien/dinov2-base-tissue
Base model
facebook/dinov2-baseEvaluation results
- Accuracy on imagefolderself-reported0.995
- F1 on imagefolderself-reported0.995