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 'mambaout_small.in1k', on 2025-01-20 05:34:02 UTC
Browse files- README.md +4 -3
- mambaout_small.in1k/meta.json +3 -0
- mambaout_small.in1k/model.onnx +3 -0
- mambaout_small.in1k/preprocess.json +3 -0
- models.parquet +2 -2
README.md
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- timm/convformer_s18.sail_in22k_ft_in1k
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- timm/convformer_s36.sail_in1k_384
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- timm/convformer_s36.sail_in22k_ft_in1k_384
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- timm/convmixer_1024_20_ks9_p14.in1k
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- timm/convnext_nano.r384_ad_in12k
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- timm/convnext_nano.r384_in12k_ft_in1k
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- timm/convnext_zepto_rms.ra4_e3600_r224_in1k
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- timm/lambda_resnet26t.c1_in1k
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- timm/mambaout_base_short_rw.sw_e500_in1k
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- timm/mambaout_kobe.in1k
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- timm/mambaout_small_rw.sw_e450_in1k
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- timm/mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k
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- timm/mobilenetv4_conv_aa_large.e230_r384_in12k
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# Models
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## Beit
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## MambaOut
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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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| [mambaout_base_short_rw.sw_e500_in1k](https://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k) | 88.8M | 16.3G | 224 | True | 3072 | 1000 | imagenet-1k | MambaOut | mambaout_base_short_rw | 2024-10-11 |
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| [mambaout_small_rw.sw_e450_in1k](https://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k) | 48.5M | 8.9G | 224 | True | 2304 | 1000 | imagenet-1k | MambaOut | mambaout_small_rw | 2024-10-11 |
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| [mambaout_kobe.in1k](https://huggingface.co/timm/mambaout_kobe.in1k) | 9.1M | 1.5G | 224 | True | 1152 | 1000 | imagenet-1k | MambaOut | mambaout_kobe | 2024-10-11 |
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## MaxxVit
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- timm/convformer_s18.sail_in22k_ft_in1k
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- timm/convformer_s36.sail_in1k_384
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- timm/convformer_s36.sail_in22k_ft_in1k_384
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- timm/convnext_nano.r384_ad_in12k
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- timm/convnext_nano.r384_in12k_ft_in1k
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- timm/convnext_zepto_rms.ra4_e3600_r224_in1k
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- timm/lambda_resnet26t.c1_in1k
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- timm/mambaout_base_short_rw.sw_e500_in1k
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- timm/mambaout_kobe.in1k
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- timm/mambaout_small.in1k
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- timm/mambaout_small_rw.sw_e450_in1k
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- timm/mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k
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- timm/mobilenetv4_conv_aa_large.e230_r384_in12k
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# Models
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273 models exported from TIMM in total.
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## Beit
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## MambaOut
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4 models with model class `MambaOut`.
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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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| [mambaout_base_short_rw.sw_e500_in1k](https://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k) | 88.8M | 16.3G | 224 | True | 3072 | 1000 | imagenet-1k | MambaOut | mambaout_base_short_rw | 2024-10-11 |
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| [mambaout_small_rw.sw_e450_in1k](https://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k) | 48.5M | 8.9G | 224 | True | 2304 | 1000 | imagenet-1k | MambaOut | mambaout_small_rw | 2024-10-11 |
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| [mambaout_small.in1k](https://huggingface.co/timm/mambaout_small.in1k) | 48.5M | 8.9G | 224 | True | 2304 | 1000 | imagenet-1k | MambaOut | mambaout_small | 2024-10-11 |
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| [mambaout_kobe.in1k](https://huggingface.co/timm/mambaout_kobe.in1k) | 9.1M | 1.5G | 224 | True | 1152 | 1000 | imagenet-1k | MambaOut | mambaout_kobe | 2024-10-11 |
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## MaxxVit
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mambaout_small.in1k/meta.json
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version https://git-lfs.github.com/spec/v1
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size 169798
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mambaout_small.in1k/model.onnx
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size 194320648
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mambaout_small.in1k/preprocess.json
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size 734
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models.parquet
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size 25276
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