Upload fine-tuned BiomedCLIP on ROCOv2
Browse files- README.md +27 -0
- open_clip_config.json +31 -0
- open_clip_pytorch_model.bin +3 -0
- training_config.json +14 -0
README.md
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---
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library_name: open_clip
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base_model: microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224
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datasets:
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- eltorio/ROCOv2-radiology
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tags:
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- biomedclip
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- rocov2
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- medical-image-retrieval
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---
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# BiomedCLIP fine-tuned on ROCOv2
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Fine-tuned from `microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224` on `eltorio/ROCOv2-radiology` with CLIP contrastive image-caption loss.
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Main hyperparameters:
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- LR: 1e-05
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- Epochs: 4
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- Batch size: 16
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- Gradient accumulation: 4
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- Effective batch size: 64
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- Context length: 256
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Saved files:
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- `open_clip_pytorch_model.bin`
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- `open_clip_config.json`
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- `training_config.json`
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open_clip_config.json
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{
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"model_cfg": {
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"embed_dim": 512,
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"vision_cfg": {
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"timm_model_name": "vit_base_patch16_224",
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"timm_model_pretrained": false,
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"timm_pool": "",
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"timm_proj": "linear",
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"image_size": 224
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},
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"text_cfg": {
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"hf_model_name": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract",
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"hf_tokenizer_name": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract",
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"hf_proj_type": "mlp",
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"hf_pooler_type": "cls_last_hidden_state_pooler",
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"context_length": 256
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}
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},
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"preprocess_cfg": {
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"mean": [
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0.48145466,
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0.4578275,
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0.40821073
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],
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"std": [
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0.26862954,
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0.26130258,
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0.27577711
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]
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}
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}
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open_clip_pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9c10bc2a914bf2bcdae5e9e6ab2eb14dc000cac4d3d5c5b2db5b29476c7d426
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size 783762513
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training_config.json
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{
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"base_model": "microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224",
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"dataset": "eltorio/ROCOv2-radiology",
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"epoch": 4,
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"global_step": 3748,
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"learning_rate": 1e-05,
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"epochs": 4,
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"batch_size": 16,
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"grad_accum_steps": 4,
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"effective_batch_size": 64,
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"weight_decay": 0.02,
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"warmup_ratio": 0.05,
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"context_length": 256
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}
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