Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("47ag925/trash_classifier3")
model = AutoModelForImageClassification.from_pretrained("47ag925/trash_classifier3", device_map="auto")Quick Links
trash_classifier3
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: 2.0325
- Accuracy: 0.0018
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 |
|---|---|---|---|---|
| 1.7363 | 1.0 | 102 | 1.9884 | 0.0166 |
| 1.5557 | 2.0 | 204 | 2.0147 | 0.0037 |
| 1.664 | 3.0 | 306 | 2.0527 | 0.0012 |
| 1.3446 | 4.0 | 408 | 2.0362 | 0.0018 |
| 1.3102 | 5.0 | 510 | 2.0325 | 0.0018 |
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_classifier3
Base model
microsoft/resnet-50Evaluation results
- Accuracy on imagefolderself-reported0.002
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="47ag925/trash_classifier3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")