Instructions to use nonl/dfine-cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nonl/dfine-cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="nonl/dfine-cppe5")# Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("nonl/dfine-cppe5") model = AutoModelForObjectDetection.from_pretrained("nonl/dfine-cppe5") - Notebooks
- Google Colab
- Kaggle
File size: 1,027 Bytes
482f1df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"epoch": 300.0,
"test_loss": 4.271916389465332,
"test_map": 0.2447,
"test_map_50": 0.3449,
"test_map_75": 0.2657,
"test_map_Coverall": 0.5564,
"test_map_Face_Shield": 0.2347,
"test_map_Gloves": 0.0469,
"test_map_Goggles": 0.181,
"test_map_Mask": 0.2045,
"test_map_large": 0.2457,
"test_map_medium": 0.1547,
"test_map_small": 0.288,
"test_mar_1": 0.2186,
"test_mar_10": 0.5799,
"test_mar_100": 0.6928,
"test_mar_100_Coverall": 0.8051,
"test_mar_100_Face_Shield": 0.8176,
"test_mar_100_Gloves": 0.5458,
"test_mar_100_Goggles": 0.6172,
"test_mar_100_Mask": 0.6784,
"test_mar_large": 0.8429,
"test_mar_medium": 0.5042,
"test_mar_small": 0.5792,
"test_runtime": 0.7065,
"test_samples_per_second": 41.05,
"test_steps_per_second": 5.662,
"total_flos": 1.9198779273216e+19,
"train_loss": 11.328886562834647,
"train_runtime": 8885.8834,
"train_samples_per_second": 28.697,
"train_steps_per_second": 3.612
} |