Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("judith0/classification_INE_v2")
model = AutoModelForImageClassification.from_pretrained("judith0/classification_INE_v2")Quick Links
classification_INE_v1-finetuned-eurosat
This model is a fine-tuned version of judith0/classification_INE_v1 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0261
- Accuracy: 1.0
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.96 | 6 | 0.0528 | 0.9884 |
| 0.1411 | 1.92 | 12 | 0.0261 | 1.0 |
| 0.1411 | 2.88 | 18 | 0.0182 | 1.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for judith0/classification_INE_v2
Evaluation results
- Accuracy on imagefolderself-reported1.000
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="judith0/classification_INE_v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")