Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use djbp/swin-tiny-patch4-window7-224-MM_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djbp/swin-tiny-patch4-window7-224-MM_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="djbp/swin-tiny-patch4-window7-224-MM_Classification", device_map="auto") 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("djbp/swin-tiny-patch4-window7-224-MM_Classification") model = AutoModelForImageClassification.from_pretrained("djbp/swin-tiny-patch4-window7-224-MM_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 20.0, | |
| "eval_accuracy": 0.8693982074263764, | |
| "eval_loss": 0.34680071473121643, | |
| "eval_runtime": 75.6434, | |
| "eval_samples_per_second": 10.325, | |
| "eval_steps_per_second": 0.093, | |
| "total_flos": 4.783917310653358e+18, | |
| "train_loss": 0.3464228366550646, | |
| "train_runtime": 17694.0061, | |
| "train_samples_per_second": 10.877, | |
| "train_steps_per_second": 0.021 | |
| } |