Instructions to use iamcode6/dinov2-l-ccmt-mi300x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use iamcode6/dinov2-l-ccmt-mi300x with timm:
import timm model = timm.create_model("hf_hub:iamcode6/dinov2-l-ccmt-mi300x", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 915 Bytes
bb69498 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | nohup: ignoring input
/opt/venv/lib/python3.10/site-packages/apex/transformer/functional/fused_rope.py:54: UserWarning: Using the native apex kernel for RoPE.
warnings.warn("Using the native apex kernel for RoPE.", UserWarning)
epoch 1 | acc=0.8586 f1=0.8633 414 img/s
epoch 2 | acc=0.8508 f1=0.8532 425 img/s
epoch 3 | acc=0.8769 f1=0.8809 425 img/s
epoch 4 | acc=0.8950 f1=0.8968 421 img/s
epoch 5 | acc=0.9106 f1=0.9113 425 img/s
epoch 6 | acc=0.9207 f1=0.9240 424 img/s
epoch 7 | acc=0.9289 f1=0.9326 425 img/s
epoch 8 | acc=0.9438 f1=0.9453 421 img/s
epoch 9 | acc=0.9498 f1=0.9492 425 img/s
epoch 10 | acc=0.9553 f1=0.9563 424 img/s
epoch 11 | acc=0.9629 f1=0.9618 421 img/s
epoch 12 | acc=0.9656 f1=0.9654 421 img/s
epoch 13 | acc=0.9677 f1=0.9670 421 img/s
epoch 14 | acc=0.9680 f1=0.9670 425 img/s
epoch 15 | acc=0.9686 f1=0.9682 426 img/s
[train] best val_macro_f1=0.9682 artifacts in runs/dinov2_l_v1
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