Instructions to use suncy13/sample_footColor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suncy13/sample_footColor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="suncy13/sample_footColor") 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_footColor") model = AutoModelForImageClassification.from_pretrained("suncy13/sample_footColor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from suncy13/sample_footColor: direct link, hf CLI and curl.
- Browser
- Download file 2.01 kB
-
https://huggingface.co/suncy13/sample_footColor/resolve/main/README.md
- Command line
-
hf download hf://suncy13/sample_footColor/README.md
-
curl -L -o README.md https://huggingface.co/suncy13/sample_footColor/resolve/main/README.md
2.01 kB
| license: apache-2.0 | |
| base_model: facebook/dinov2-small-imagenet1k-1-layer | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: sample_footColor | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # sample_footColor | |
| This model is a fine-tuned version of [facebook/dinov2-small-imagenet1k-1-layer](https://huggingface.co/facebook/dinov2-small-imagenet1k-1-layer) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0480 | |
| - Accuracy: 0.55 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 2.9818 | 1.0 | 12 | 1.4086 | 0.55 | | |
| | 4.0926 | 2.0 | 24 | 1.1745 | 0.55 | | |
| | 1.9427 | 3.0 | 36 | 1.8742 | 0.2 | | |
| | 4.0665 | 4.0 | 48 | 1.0673 | 0.55 | | |
| | 2.225 | 5.0 | 60 | 1.3158 | 0.55 | | |
| | 1.4044 | 6.0 | 72 | 1.1891 | 0.55 | | |
| | 1.6696 | 7.0 | 84 | 1.0104 | 0.55 | | |
| | 1.1405 | 8.0 | 96 | 1.1005 | 0.55 | | |
| | 1.5148 | 9.0 | 108 | 1.0531 | 0.55 | | |
| | 1.1416 | 10.0 | 120 | 1.0480 | 0.55 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |