# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("mthandazo/detr-resnet-50-hardhat-finetuned")
model = AutoModelForObjectDetection.from_pretrained("mthandazo/detr-resnet-50-hardhat-finetuned")Quick Links
detr-resnet-50-hardhat-finetuned
This model is a fine-tuned version of facebook/detr-resnet-50-dc5 on the anindya64/hardhat 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
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for mthandazo/detr-resnet-50-hardhat-finetuned
Base model
facebook/detr-resnet-50-dc5
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mthandazo/detr-resnet-50-hardhat-finetuned")