Instructions to use mwalmsley/zoobot-encoder-evo-maxvit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/zoobot-encoder-evo-maxvit-base with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/zoobot-encoder-evo-maxvit-base", pretrained=True) - Transformers
How to use mwalmsley/zoobot-encoder-evo-maxvit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mwalmsley/zoobot-encoder-evo-maxvit-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mwalmsley/zoobot-encoder-evo-maxvit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add model
Browse files- README.md +9 -0
- config.json +36 -0
- model.safetensors +3 -0
- pytorch_model.bin +3 -0
README.md
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---
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tags:
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- image-classification
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- timm
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- transformers
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library_name: timm
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license: apache-2.0
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---
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# Model card for zoobot-encoder-evo-maxvit-base
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config.json
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{
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"architecture": "maxvit_rmlp_base_rw_224",
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"num_classes": 0,
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"num_features": 768,
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"global_pool": "avg",
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"pretrained_cfg": {
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"tag": "sw_in12k_ft_in1k",
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"custom_load": false,
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"input_size": [
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3,
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224,
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224
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],
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"fixed_input_size": true,
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"interpolation": "bicubic",
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"crop_pct": 0.95,
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"crop_mode": "center",
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"mean": [
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0.5,
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0.5,
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0.5
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],
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"std": [
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0.5,
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0.5,
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0.5
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],
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"num_classes": 1000,
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"pool_size": [
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7,
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7
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],
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"first_conv": "stem.conv1",
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"classifier": "head.fc"
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:97d7bfa921e3d9a7949253de5e5a2496a2cd24ab44dd5a8b61ecd73ce3c4cfcf
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size 462158584
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd97d960108f271f36922988602f9a8307e1d572db0d8c88b6c48dc7b516dbac
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size 462552086
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