metadata

tags:
  - generated_from_trainer
  - clip
  - bert
  - vision-language models
model-index:
  - name: output_8_clip14_cxrbert
    results: []
datasets:
  - MedIR/roco
language:
  - en
library_name: transformers
pipeline_tag: feature-extraction

RCLIP (Clip model fine-tuned on radiology images and their captions)

This model is a fine-tuned version of openai/clip-vit-large-patch14 as an image encoder and microsoft/BiomedVLP-CXR-BERT-general as a text encoder on the ROCO dataset. It achieves the following results on the evaluation set:

Loss: 0.3388

Heatmap

Here is the heatmap of the similarity score of the first 30 samples on the test split of the ROCO dataset of images vs their captions:

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 8.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.7951	0.09	500	1.1912
0.5887	0.18	1000	0.9833
0.5023	0.28	1500	0.8459
0.4709	0.37	2000	0.8479
0.4484	0.46	2500	0.7667
0.4319	0.55	3000	0.8092
0.4181	0.64	3500	0.6964
0.4107	0.73	4000	0.6463
0.3723	0.83	4500	0.7893
0.3746	0.92	5000	0.6863
0.3667	1.01	5500	0.6910
0.3253	1.1	6000	0.6863
0.3274	1.19	6500	0.6445
0.3065	1.28	7000	0.5908
0.2834	1.38	7500	0.6138
0.293	1.47	8000	0.6515
0.303	1.56	8500	0.5806
0.2638	1.65	9000	0.5587
0.2593	1.74	9500	0.5216
0.2451	1.83	10000	0.5283
0.2468	1.93	10500	0.5001
0.2295	2.02	11000	0.4975
0.1953	2.11	11500	0.4750
0.1954	2.2	12000	0.4572
0.1737	2.29	12500	0.4731
0.175	2.38	13000	0.4526
0.1873	2.48	13500	0.4890
0.1809	2.57	14000	0.4210
0.1711	2.66	14500	0.4197
0.1457	2.75	15000	0.3998
0.1583	2.84	15500	0.3923
0.1579	2.94	16000	0.3823
0.1339	3.03	16500	0.3654
0.1164	3.12	17000	0.3592
0.1217	3.21	17500	0.3641
0.119	3.3	18000	0.3553
0.1151	3.39	18500	0.3524
0.119	3.49	19000	0.3452
0.102	3.58	19500	0.3439
0.1085	3.67	20000	0.3422
0.1142	3.76	20500	0.3396
0.1038	3.85	21000	0.3392
0.1143	3.94	21500	0.3390
0.0983	4.04	22000	0.3390
0.0974	4.13	22500	0.3388

Framework versions

Transformers 4.31.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3