--- license: apache-2.0 tags: - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: convnext-tiny-finetuned-beans results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans args: default metrics: - name: Accuracy type: accuracy value: 0.9609375 - task: type: image-classification name: Image Classification dataset: name: beans type: beans config: default split: test metrics: - name: Accuracy type: accuracy value: 0.9609375 verified: true - name: Precision Macro type: precision value: 0.9652777777777778 verified: true - name: Precision Micro type: precision value: 0.9609375 verified: true - name: Precision Weighted type: precision value: 0.9650065104166667 verified: true - name: Recall Macro type: recall value: 0.9608711701734958 verified: true - name: Recall Micro type: recall value: 0.9609375 verified: true - name: Recall Weighted type: recall value: 0.9609375 verified: true - name: F1 Macro type: f1 value: 0.9615067076130842 verified: true - name: F1 Micro type: f1 value: 0.9609375 verified: true - name: F1 Weighted type: f1 value: 0.9613965275467993 verified: true - name: loss type: loss value: 0.14331591129302979 verified: true --- # convnext-tiny-finetuned-beans This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1255 - Accuracy: 0.9609 ![pic](https://huggingface.co/proxy-datasets-preview/assets/beans/--/default/test/96/image/image.jpg) ## 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: 7171 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 37 | 0.6175 | 0.8828 | | No log | 2.0 | 74 | 0.2307 | 0.9609 | | 0.5237 | 3.0 | 111 | 0.1406 | 0.9531 | | 0.5237 | 4.0 | 148 | 0.1165 | 0.9688 | | 0.5237 | 5.0 | 185 | 0.1255 | 0.9609 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1