Instructions to use gagan3012/swinv2-base-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gagan3012/swinv2-base-512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gagan3012/swinv2-base-512") 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("gagan3012/swinv2-base-512") model = AutoModelForImageClassification.from_pretrained("gagan3012/swinv2-base-512") - Notebooks
- Google Colab
- Kaggle
swinv2-base-512
This model was trained from scratch on the /lustre07/scratch/gagan30/arocr/Shamila dataset. It achieves the following results on the evaluation set:
- Loss: 1.0139
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: 2
- eval_batch_size: 2
- seed: 1337
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9887 | 1.0 | 139819 | 1.0138 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.7.1
- Tokenizers 0.11.6
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