Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ricardoSLabs/paper_model_DP_1")
model = AutoModelForImageClassification.from_pretrained("ricardoSLabs/paper_model_DP_1")Quick Links
paper_model_DP_1
This model is a fine-tuned version of jordyvl/resnet50_rvl-cdip on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0283
- Accuracy: 0.9924
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1263 | 0.9954 | 162 | 0.0981 | 0.9622 |
| 0.0733 | 1.9969 | 325 | 0.0436 | 0.9891 |
| 0.0491 | 2.9985 | 488 | 0.0276 | 0.9874 |
| 0.0286 | 4.0 | 651 | 0.0266 | 0.9908 |
| 0.0321 | 4.9770 | 810 | 0.0283 | 0.9924 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for ricardoSLabs/paper_model_DP_1
Evaluation results
- Accuracy on imagefoldertest set self-reported0.992
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ricardoSLabs/paper_model_DP_1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")