Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use hmrizal/recycled_waste_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hmrizal/recycled_waste_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hmrizal/recycled_waste_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("hmrizal/recycled_waste_classification") model = AutoModelForImageClassification.from_pretrained("hmrizal/recycled_waste_classification") - Notebooks
- Google Colab
- Kaggle
recycled_waste_classification
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.8487
- Accuracy: 0.8023
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 311 | 0.8894 | 0.7203 |
| 0.8566 | 2.0 | 622 | 0.8025 | 0.7572 |
| 0.8566 | 3.0 | 933 | 0.9952 | 0.7395 |
| 0.2857 | 4.0 | 1244 | 0.9670 | 0.7749 |
| 0.0541 | 5.0 | 1555 | 0.9099 | 0.7958 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for hmrizal/recycled_waste_classification
Base model
google/vit-base-patch16-224Evaluation results
- Accuracy on imagefolderself-reported0.802