Instructions to use suncy13/sample_foot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suncy13/sample_foot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="suncy13/sample_foot") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("suncy13/sample_foot") model = AutoModelForImageClassification.from_pretrained("suncy13/sample_foot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from suncy13/sample_foot: direct link, hf CLI and curl.
- Browser
- Download file 2.01 kB
-
https://huggingface.co/suncy13/sample_foot/resolve/main/README.md
- Command line
-
hf download hf://suncy13/sample_foot/README.md
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curl -L -o README.md https://huggingface.co/suncy13/sample_foot/resolve/main/README.md
2.01 kB
metadata
license: apache-2.0
base_model: facebook/dinov2-small-imagenet1k-1-layer
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sample_foot
results: []
sample_foot
This model is a fine-tuned version of facebook/dinov2-small-imagenet1k-1-layer on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.0754
- Accuracy: 0.4545
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 5.6026 | 1.0 | 21 | 4.9823 | 0.4545 |
| 3.7497 | 2.0 | 42 | 3.6049 | 0.4545 |
| 3.1278 | 3.0 | 63 | 3.7326 | 0.4545 |
| 2.4551 | 4.0 | 84 | 2.0051 | 0.4545 |
| 2.3722 | 5.0 | 105 | 3.1027 | 0.4545 |
| 2.3023 | 6.0 | 126 | 2.6815 | 0.4545 |
| 2.8063 | 7.0 | 147 | 2.9446 | 0.4545 |
| 2.2692 | 8.0 | 168 | 3.1585 | 0.4545 |
| 2.545 | 9.0 | 189 | 2.8486 | 0.4545 |
| 2.2706 | 10.0 | 210 | 3.0754 | 0.4545 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0