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This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: Loss: 2.3072 Accuracy: 0.459
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This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: Loss: 2.3072 Accuracy: 0.459
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1 sourceVérifié 17 sept.
Artefacts du modèle
1 artefactExtraits de sources
2 extraits--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - trackio - trackio:https://huggingface.co/spaces/Valencio/huggingface-static-187a68 - generated_from_trainer metrics: - accuracy model-index: - name: LLM_course_ViT_model_image_classification results: [] --- <a href="https://huggingface.co/spaces/Valencio/huggingface-static-187a68" target="_blank"><img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" style="height: 40px;"/></a> <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # LLM_course_ViT_model_image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3072 - Accuracy: 0.459 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.0023 | 1.0 | 63 | 2.8601 | 0.389 | | 2.5716 | 2.0 | 126 | 2.4576 | 0.42 | | 2.3300 | 3.0 | 189 | 2.3072 | 0.459 | ### Framework versions - Transformers 5.10.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 5 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo Valencio/LLM_course_ViT_model_image_classification
