ConvNext-Small-224-Leicester Binary Open-source Image Classification Model - Efficiently Complete Binary Classification Tasks

Convnext Small 224 Leicester Binary

Developed by davanstrien

An image classification model fine-tuned for binary classification tasks based on facebook/convnext-small-224, achieving an F1 score of 0.9620 on the evaluation set

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision image classification #Medical image analysis #Few-shot fine-tuning

Downloads 13

Release Time : 12/6/2022

Model Overview

This model is a ConvNeXt small vision model optimized for binary classification tasks, suitable for scenarios requiring efficient image classification

Model Features

Efficient image classification

Optimized based on the ConvNeXt architecture, achieving high-precision classification while maintaining a small model size

Excellent performance

Achieved an F1 score of 0.9620 on binary classification tasks with a validation loss of only 0.1283

Transfer learning friendly

Fine-tuned from the facebook/convnext-small-224 pre-trained model, suitable for quick adaptation to new tasks

Model Capabilities

Image classification

Binary classification task processing

Visual feature extraction

Use Cases

Medical image analysis

Lesion detection

Used for detecting abnormal areas in medical images

Industrial quality inspection

Defective product identification

Automatic quality detection of products on production lines

🚀 convnext-small-224-leicester_binary

This model is a fine - tuned version of facebook/convnext-small-224 on the davanstrien/leicester_loaded_annotations_binary dataset. It can achieve good performance in image classification tasks, with specific evaluation results as follows.

🚀 Quick Start

This model is a fine - tuned version of facebook/convnext-small-224 on the davanstrien/leicester_loaded_annotations_binary dataset. It achieves the following results on the evaluation set:

Loss: 0.1283
F1: 0.9620

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 64
eval_batch_size: 128
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 30.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	7	0.5143	0.8608
0.5872	2.0	14	0.4215	0.8608
0.3903	3.0	21	0.4127	0.8608
0.3903	4.0	28	0.3605	0.8608
0.3163	5.0	35	0.3152	0.8608
0.2942	6.0	42	0.2942	0.8608
0.2942	7.0	49	0.2669	0.8608
0.2755	8.0	56	0.2316	0.8608
0.2281	9.0	63	0.2104	0.8608
0.2076	10.0	70	0.1938	0.8608
0.2076	11.0	77	0.1803	0.8608
0.1832	12.0	84	0.1704	0.8608
0.1758	13.0	91	0.1650	0.8608
0.1758	14.0	98	0.1714	0.8608
0.167	15.0	105	0.1575	0.8608
0.1519	16.0	112	0.1549	0.8608
0.1519	17.0	119	0.1705	0.8608
0.1422	18.0	126	0.1478	0.8608
0.1444	19.0	133	0.1437	0.8608
0.1396	20.0	140	0.1398	0.8608
0.1396	21.0	147	0.1351	0.8608
0.1293	22.0	154	0.1370	0.8987
0.1361	23.0	161	0.1335	0.8987
0.1361	24.0	168	0.1311	0.9367
0.1246	25.0	175	0.1289	0.9620
0.1211	26.0	182	0.1283	0.9620
0.1211	27.0	189	0.1294	0.9620
0.1182	28.0	196	0.1306	0.9620
0.1172	29.0	203	0.1312	0.9620
0.1102	30.0	210	0.1318	0.9620

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2

📄 License

This model is licensed under the Apache 2.0 license.

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