Instructions to use mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: fisheye8k_microsoft_conditional-detr-resnet-50
results: []
fisheye8k_microsoft_conditional-detr-resnet-50
This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.4379
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 0
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 36
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0059 | 1.0 | 5288 | 1.4506 |
| 0.9022 | 2.0 | 10576 | 1.3697 |
| 0.8475 | 3.0 | 15864 | 1.4314 |
| 0.8099 | 4.0 | 21152 | 1.4027 |
| 0.7604 | 5.0 | 26440 | 1.3805 |
| 0.7556 | 6.0 | 31728 | 1.4091 |
| 0.709 | 7.0 | 37016 | 1.4379 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0