Add model
Browse files- README.md +33 -0
- config.json +48 -0
- model.safetensors +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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pipeline_tag: image-classification
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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 convnextv2-tiny-k5-in1k-128-sup
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ConvNeXtV2-Tiny trained from scratch on ImageNet-1K @128x128 (k=5) using the ConvNeXt (V1) supervised recipe (Table 5; 300ep, AdamW, cosine, warmup 20, mixup 0.8, cutmix 1.0, reprob 0.25, EMA 0.9999). Trained with timm/train.py bf16.
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## Model Details
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- **Run Dir:** /mnt/data/training-runs/convnextv2_tiny-128-k5-sup-e300-bf16-gb4096
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- **Experiment:** convnextv2_tiny-128-k5-sup-e300-bf16-gb4096
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- **Model:** convnextv2_tiny
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- **Model Kwargs:**
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- kernel_sizes: 5
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- **Input Size:**
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- 3
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- 128
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- 128
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- **Checkpoint:** model_best.pth.tar
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- **Weights Key:** state_dict_ema
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- **Best Metric:** 79.73000010971069
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## Model Usage
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```python
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import timm
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model = timm.create_model('hf-hub:PRadecki/convnextv2-tiny-k5-in1k-128-sup', pretrained=True)
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model.eval()
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```
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config.json
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{
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"architecture": "convnextv2_tiny",
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"num_classes": 1000,
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"num_features": 768,
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"model_args": {
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"kernel_sizes": 5
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},
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"pretrained_cfg": {
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"tag": "convnextv2_tiny-128-k5-sup-e300-bf16-gb4096",
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"custom_load": false,
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"input_size": [
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3,
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128,
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128
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],
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"test_input_size": [
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3,
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128,
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128
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],
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"fixed_input_size": false,
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"interpolation": "bicubic",
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"crop_pct": 0.875,
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"test_crop_pct": 1.0,
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"crop_mode": "center",
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"mean": [
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0.485,
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0.456,
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0.406
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],
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"std": [
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0.229,
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0.224,
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0.225
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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.0",
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"classifier": "head.fc",
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"license": "cc-by-nc-4.0",
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"origin_url": "https://github.com/facebookresearch/ConvNeXt-V2",
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"paper_name": "ConvNeXt-V2: Co-designing and Scaling ConvNets with Masked Autoencoders",
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"paper_ids": "arXiv:2301.00808"
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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 sha256:029b099589af832fdb8bed5b18af91d4978e651b3b1b8f78eb350d205ecd13e7
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size 113925784
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