# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ShuaHousetable/serverless-roomsort")
model = AutoModelForImageClassification.from_pretrained("ShuaHousetable/serverless-roomsort")Quick Links
serverless-roomsort
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0394
- Accuracy: 0.9892
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7844 | 1.0 | 762 | 0.0608 | 0.9791 |
| 0.0361 | 2.0 | 1524 | 0.0626 | 0.9830 |
| 0.0149 | 3.0 | 2286 | 0.0468 | 0.9879 |
| 0.0027 | 4.0 | 3048 | 0.0394 | 0.9892 |
| 0.0017 | 5.0 | 3810 | 0.0486 | 0.9889 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.13.0
- Downloads last month
- 266
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ShuaHousetable/serverless-roomsort") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")