# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="djuHm/interior-design-model")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly
from transformers import AutoProcessor, AutoModelForImageClassification
processor = AutoProcessor.from_pretrained("djuHm/interior-design-model")
model = AutoModelForImageClassification.from_pretrained("djuHm/interior-design-model")Quick Links
interior-design-model
This model is a fine-tuned version of openai/clip-vit-base-patch32 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7369
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.5447 | 1.0 | 801 | 1.0743 |
| 0.7757 | 2.0 | 1602 | 0.6901 |
| 0.4293 | 3.0 | 2403 | 0.7369 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
- Downloads last month
- -
Model tree for djuHm/interior-design-model
Base model
openai/clip-vit-base-patch32
# Gated model: Login with a HF token with gated access permission hf auth login