Instructions to use Alimuddin/amazon_fish_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alimuddin/amazon_fish_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Alimuddin/amazon_fish_classification") 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("Alimuddin/amazon_fish_classification") model = AutoModelForImageClassification.from_pretrained("Alimuddin/amazon_fish_classification") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: facebook/convnext-large-224-22k-1k | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - amazonian_fish_classifier_data | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: image_classification | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| dataset: | |
| name: amazonian_fish_classifier_data | |
| type: amazonian_fish_classifier_data | |
| config: default | |
| split: train | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9332247557003257 | |
| <!-- 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. --> | |
| # image_classification | |
| This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the amazonian_fish_classifier_data dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2562 | |
| - Accuracy: 0.9332 | |
| ## 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: 7e-05 | |
| - train_batch_size: 17 | |
| - eval_batch_size: 17 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 145 | 0.6864 | 0.8420 | | |
| | No log | 2.0 | 290 | 0.5780 | 0.8306 | | |
| | No log | 3.0 | 435 | 0.4466 | 0.8860 | | |
| | 0.7812 | 4.0 | 580 | 0.3810 | 0.8958 | | |
| | 0.7812 | 5.0 | 725 | 0.4124 | 0.8860 | | |
| | 0.7812 | 6.0 | 870 | 0.3617 | 0.9007 | | |
| | 0.3315 | 7.0 | 1015 | 0.3397 | 0.8990 | | |
| | 0.3315 | 8.0 | 1160 | 0.3746 | 0.9055 | | |
| | 0.3315 | 9.0 | 1305 | 0.3379 | 0.9023 | | |
| | 0.3315 | 10.0 | 1450 | 0.3825 | 0.8958 | | |
| ### Framework versions | |
| - Transformers 4.33.3 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |