# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("rjhugs/modelStructure_TT_V3")
model = AutoModelForObjectDetection.from_pretrained("rjhugs/modelStructure_TT_V3")Quick Links
modelStructure_TT_V3
This model is a fine-tuned version of microsoft/table-transformer-structure-recognition-v1.1-all 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="rjhugs/modelStructure_TT_V3")