Image Classification
Transformers
TensorBoard
Safetensors
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use mateoluksenberg/Seed_Classifier_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateoluksenberg/Seed_Classifier_V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mateoluksenberg/Seed_Classifier_V2") 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("mateoluksenberg/Seed_Classifier_V2") model = AutoModelForImageClassification.from_pretrained("mateoluksenberg/Seed_Classifier_V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: microsoft/dit-base | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Seed_Classifier_V2 | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| dataset: | |
| name: imagefolder | |
| type: imagefolder | |
| config: default | |
| split: train | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.0 | |
| <!-- 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. --> | |
| # Seed_Classifier_V2 | |
| This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.6985 | |
| - Accuracy: 0.0 | |
| - Weighted f1: 0.0 | |
| - Micro f1: 0.0 | |
| - Macro f1: 0.0 | |
| - Weighted recall: 0.0 | |
| - Micro recall: 0.0 | |
| - Macro recall: 0.0 | |
| - Weighted precision: 0.0 | |
| - Micro precision: 0.0 | |
| - Macro precision: 0.0 | |
| ## 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: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 18 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| | |
| | 0.3829 | 1.0 | 1 | 1.0584 | 0.5 | 0.3333 | 0.5 | 0.3333 | 0.5 | 0.5 | 0.5 | 0.25 | 0.5 | 0.25 | | |
| | 0.3829 | 2.0 | 2 | 1.2877 | 0.25 | 0.2 | 0.25 | 0.2 | 0.25 | 0.25 | 0.25 | 0.1667 | 0.25 | 0.1667 | | |
| | 0.3829 | 3.0 | 3 | 2.2985 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3829 | 4.0 | 4 | 2.4998 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3829 | 5.0 | 5 | 2.2230 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3829 | 6.0 | 6 | 1.9467 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3829 | 7.0 | 7 | 1.7201 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 8.0 | 8 | 1.5736 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 9.0 | 9 | 1.5412 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 10.0 | 10 | 1.5484 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 11.0 | 11 | 1.5762 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 12.0 | 12 | 1.5907 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 13.0 | 13 | 1.6231 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 14.0 | 14 | 1.6462 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3628 | 15.0 | 15 | 1.6710 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3175 | 16.0 | 16 | 1.6883 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3175 | 17.0 | 17 | 1.6994 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 0.3175 | 18.0 | 18 | 1.6985 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.0.0 | |
| - Tokenizers 0.19.1 | |