Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use 47ag925/trash_classifier2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 47ag925/trash_classifier2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="47ag925/trash_classifier2") 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("47ag925/trash_classifier2") model = AutoModelForImageClassification.from_pretrained("47ag925/trash_classifier2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
trash_classifier2
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 2.0483
- Accuracy: 0.0294
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8831 | 1.0 | 102 | 2.1049 | 0.0018 |
| 0.7466 | 2.0 | 204 | 2.0777 | 0.0117 |
| 1.0559 | 3.0 | 306 | 2.1094 | 0.0160 |
| 0.4889 | 4.0 | 408 | 2.0512 | 0.0245 |
| 0.5925 | 5.0 | 510 | 2.0483 | 0.0294 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for 47ag925/trash_classifier2
Base model
microsoft/resnet-50Evaluation results
- Accuracy on imagefolderself-reported0.029