# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("polejowska/yolos-tiny-CD45RB-1000")
model = AutoModelForObjectDetection.from_pretrained("polejowska/yolos-tiny-CD45RB-1000", device_map="auto")Quick Links
yolos-tiny-CD45RB-1000
This model is a fine-tuned version of hustvl/yolos-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.6317
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: 0.0001
- 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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.4965 | 1.0 | 94 | 2.7799 |
| 3.4526 | 2.0 | 188 | 2.7380 |
| 3.4012 | 3.0 | 282 | 2.6721 |
| 3.2776 | 4.0 | 376 | 2.6651 |
| 3.2164 | 5.0 | 470 | 2.6555 |
| 3.2701 | 6.0 | 564 | 2.6489 |
| 3.1847 | 7.0 | 658 | 2.6993 |
| 3.0959 | 8.0 | 752 | 2.6364 |
| 3.0506 | 9.0 | 846 | 2.6464 |
| 3.0497 | 10.0 | 940 | 2.6304 |
| 3.0767 | 11.0 | 1034 | 2.6344 |
| 3.0397 | 12.0 | 1128 | 2.6142 |
| 2.982 | 13.0 | 1222 | 2.6787 |
| 2.883 | 14.0 | 1316 | 2.6492 |
| 2.8978 | 15.0 | 1410 | 2.6317 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- 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="polejowska/yolos-tiny-CD45RB-1000")