# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("jamjammin/use_data_finetuning")
model = AutoModelForObjectDetection.from_pretrained("jamjammin/use_data_finetuning")Quick Links
use_data_finetuning
This model is a fine-tuned version of devonho/detr-resnet-50_finetuned_cppe5 on an unknown dataset.
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 100
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
- 5
Model tree for jamjammin/use_data_finetuning
Base model
devonho/detr-resnet-50_finetuned_cppe5
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="jamjammin/use_data_finetuning")