commit files to HF hub
Browse files- README.md +26 -0
- config.json +47 -0
- preprocessor_config.json +18 -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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- pytorch
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metrics:
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- accuracy
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- Cohen's Kappa
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model-index:
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- name: PANDA_ConvNeXT
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results:
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- task:
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name: Image Classification
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type: image-classification
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.5491307377815247
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- name: Quadratic Cohen's Kappa
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type: Quadratic Cohen's Kappa
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value: 0.6630877256393433
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---
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# PANDA_ConvNeXT
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An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
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config.json
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{
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"_name_or_path": "facebook/convnext-xlarge-384-22k-1k",
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"architectures": [
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"ConvNextForImageClassification"
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],
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"depths": [
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3,
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3,
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27,
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3
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],
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"drop_path_rate": 0.0,
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"hidden_act": "gelu",
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"hidden_sizes": [
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256,
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512,
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1024,
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2048
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],
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"id2label": {
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"0": "Beningn",
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"1": "ISUP 1",
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"2": "ISUP 2",
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"3": "ISUP 3",
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"4": "ISUP 4",
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"5": "ISUP 5"
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},
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"image_size": 384,
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"initializer_range": 0.02,
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"label2id": {
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"Beningn": "0",
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"ISUP 1": "1",
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"ISUP 2": "2",
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"ISUP 3": "3",
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"ISUP 4": "4",
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"ISUP 5": "5"
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},
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"layer_norm_eps": 1e-12,
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"layer_scale_init_value": 1e-06,
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"model_type": "convnext",
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"num_channels": 3,
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"num_stages": 4,
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"patch_size": 4,
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.19.2"
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}
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preprocessor_config.json
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{
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"crop_pct": null,
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "ConvNextFeatureExtractor",
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"image_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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"image_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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"resample": 3,
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"size": 384
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}
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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:e4ad6b01adf343a2278590a0fc63daf546c4f07a71fae1e06ef5fd1651deed8c
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size 1392752337
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