Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Eval Results (legacy)
Instructions to use Angy309/swinv2-tiny-patch4-window8-256-Lego-v2-3ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angy309/swinv2-tiny-patch4-window8-256-Lego-v2-3ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Angy309/swinv2-tiny-patch4-window8-256-Lego-v2-3ep") 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("Angy309/swinv2-tiny-patch4-window8-256-Lego-v2-3ep") model = AutoModelForImageClassification.from_pretrained("Angy309/swinv2-tiny-patch4-window8-256-Lego-v2-3ep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9196428571428571, | |
| "eval_loss": 0.2128717005252838, | |
| "eval_runtime": 1.7338, | |
| "eval_samples_per_second": 64.599, | |
| "eval_steps_per_second": 2.307 | |
| } |