Instructions to use sudo-s/robot22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sudo-s/robot22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sudo-s/robot22") 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("sudo-s/robot22") model = AutoModelForImageClassification.from_pretrained("sudo-s/robot22") - Notebooks
- Google Colab
- Kaggle
robot22
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem6 dataset. It achieves the following results on the evaluation set:
- Loss: 2.5674
- Accuracy: 0.5077
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.9154 | 0.23 | 100 | 3.8417 | 0.2213 |
| 3.1764 | 0.47 | 200 | 3.2243 | 0.3201 |
| 2.8186 | 0.7 | 300 | 2.7973 | 0.4284 |
| 2.632 | 0.93 | 400 | 2.5674 | 0.5077 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
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