Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Danung/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Danung/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Danung/image_classification") 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("Danung/image_classification") model = AutoModelForImageClassification.from_pretrained("Danung/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224-in21k | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: image_classification | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| dataset: | |
| name: imagefolder | |
| type: imagefolder | |
| config: default | |
| split: train[:5000] | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.59375 | |
| <!-- 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. --> | |
| # image_classification | |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.2141 | |
| - Accuracy: 0.5938 | |
| ## 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: 0.0001 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 7 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 40 | 1.2602 | 0.5312 | | |
| | No log | 2.0 | 80 | 1.2212 | 0.55 | | |
| | No log | 3.0 | 120 | 1.2422 | 0.5375 | | |
| | No log | 4.0 | 160 | 1.1822 | 0.6 | | |
| | No log | 5.0 | 200 | 1.2218 | 0.55 | | |
| | No log | 6.0 | 240 | 1.1602 | 0.6125 | | |
| | No log | 7.0 | 280 | 1.2598 | 0.5687 | | |
| ### Framework versions | |
| - Transformers 4.37.2 | |
| - Pytorch 2.2.0+cu118 | |
| - Datasets 2.17.0 | |
| - Tokenizers 0.15.2 | |