Instructions to use deepghs/timms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use deepghs/timms with timm:
import timm model = timm.create_model("hf_hub:deepghs/timms", pretrained=True) - Notebooks
- Google Colab
- Kaggle
narugo1992 commited on
Export model 'regnety_320.pycls_in1k', on 2025-01-21 13:14:19 JST
Browse files- README.md +3 -2
- models.parquet +2 -2
- regnety_320.pycls_in1k/meta.json +3 -0
- regnety_320.pycls_in1k/model.onnx +3 -0
- regnety_320.pycls_in1k/preprocess.json +3 -0
README.md
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# Models
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## Beit
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## RegNet
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| Name | Params | Flops | Input Size | Can Classify | Features | Classes | Dataset | Model | Architecture | Created At |
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|:---------------------------------------------------------------------------------------------|:---------|:--------|-------------:|:---------------|-----------:|----------:|:-------------|:--------|:---------------|:-------------|
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| [regnety_160.swag_ft_in1k](https://huggingface.co/timm/regnety_160.swag_ft_in1k) | 83.5M | 46.7G | 384 | True | 3024 | 1000 | imagenet-1k | RegNet | regnety_160 | 2023-03-21 |
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| [regnetx_160.tv2_in1k](https://huggingface.co/timm/regnetx_160.tv2_in1k) | 54.2M | 15.9G | 224 | True | 2048 | 1000 | imagenet-1k | RegNet | regnetx_160 | 2023-03-21 |
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| [regnetx_160.pycls_in1k](https://huggingface.co/timm/regnetx_160.pycls_in1k) | 54.2M | 15.9G | 224 | True | 2048 | 1000 | imagenet-1k | RegNet | regnetx_160 | 2023-03-21 |
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| [regnety_160.tv2_in1k](https://huggingface.co/timm/regnety_160.tv2_in1k) | 83.5M | 15.9G | 224 | True | 3024 | 1000 | imagenet-1k | RegNet | regnety_160 | 2023-03-21 |
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# Models
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1020 models exported from TIMM in total.
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## Beit
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## RegNet
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50 models with model class `RegNet`.
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| Name | Params | Flops | Input Size | Can Classify | Features | Classes | Dataset | Model | Architecture | Created At |
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|:---------------------------------------------------------------------------------------------|:---------|:--------|-------------:|:---------------|-----------:|----------:|:-------------|:--------|:---------------|:-------------|
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| 941 |
| [regnety_160.swag_ft_in1k](https://huggingface.co/timm/regnety_160.swag_ft_in1k) | 83.5M | 46.7G | 384 | True | 3024 | 1000 | imagenet-1k | RegNet | regnety_160 | 2023-03-21 |
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| [regnety_320.pycls_in1k](https://huggingface.co/timm/regnety_320.pycls_in1k) | 144.9M | 32.3G | 224 | True | 3712 | 1000 | imagenet-1k | RegNet | regnety_320 | 2023-03-21 |
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| [regnetx_160.tv2_in1k](https://huggingface.co/timm/regnetx_160.tv2_in1k) | 54.2M | 15.9G | 224 | True | 2048 | 1000 | imagenet-1k | RegNet | regnetx_160 | 2023-03-21 |
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| [regnetx_160.pycls_in1k](https://huggingface.co/timm/regnetx_160.pycls_in1k) | 54.2M | 15.9G | 224 | True | 2048 | 1000 | imagenet-1k | RegNet | regnetx_160 | 2023-03-21 |
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| [regnety_160.tv2_in1k](https://huggingface.co/timm/regnety_160.tv2_in1k) | 83.5M | 15.9G | 224 | True | 3024 | 1000 | imagenet-1k | RegNet | regnety_160 | 2023-03-21 |
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models.parquet
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regnety_320.pycls_in1k/meta.json
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regnety_320.pycls_in1k/model.onnx
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regnety_320.pycls_in1k/preprocess.json
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