# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("clp/detr-resnet-50_finetuned_cppe5")
model = AutoModelForObjectDetection.from_pretrained("clp/detr-resnet-50_finetuned_cppe5", device_map="auto")Quick Links
detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of clp/detr-resnet-50_finetuned_cppe5 on the cppe-5 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="clp/detr-resnet-50_finetuned_cppe5", device_map="auto")