# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("dbihbka/detr_finetuned_cppe5")
model = AutoModelForObjectDetection.from_pretrained("dbihbka/detr_finetuned_cppe5")Quick Links
detr_finetuned_cppe5
This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the generator 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: 0.0005
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
Training results
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for dbihbka/detr_finetuned_cppe5
Base model
microsoft/conditional-detr-resnet-50
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="dbihbka/detr_finetuned_cppe5")