Instructions to use Professor/CGIAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Professor/CGIAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Professor/CGIAR") 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("Professor/CGIAR") model = AutoModelForImageClassification.from_pretrained("Professor/CGIAR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| base_model: gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease | |
| model-index: | |
| - name: CGIAR | |
| results: [] | |
| <!-- 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. --> | |
| # CGIAR | |
| This model is a fine-tuned version of [gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease](https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7888 | |
| - Accuracy: 0.6571 | |
| ## 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: 0.001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 50 | |
| - num_epochs: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 1.0123 | 1.0 | 652 | 0.8818 | 0.6178 | | |
| | 0.8619 | 2.0 | 1304 | 0.8398 | 0.6346 | | |
| | 0.8324 | 3.0 | 1956 | 0.8233 | 0.6366 | | |
| | 0.7872 | 4.0 | 2608 | 0.7888 | 0.6571 | | |
| ### Framework versions | |
| - Transformers 4.37.1 | |
| - Pytorch 2.0.0 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |