ConvNextV2-large-DogBreed Open-source Model - Accurately Identify Dog Breeds with an Evaluation Accuracy of Over 91%

Convnextv2 Large DogBreed

Developed by Pavarissy

This model is a fine-tuned version of facebook/convnextv2-large-22k-224 on a dog breed classification dataset, achieving an accuracy of 91.39% on the evaluation set.

Image Classification

Transformers

Open Source License:Apache-2.0 #Dog Breed Classification #High-precision Recognition #ConvNextV2 Architecture

Downloads 184

Release Time : 10/29/2023

Model Overview

A computer vision model for dog breed classification, fine-tuned based on the ConvNextV2 architecture.

Model Features

High Accuracy

Achieves 91.39% accuracy in dog breed classification tasks.

Based on ConvNextV2 Architecture

Uses the advanced ConvNextV2-large-22k-224 as the base model.

Fine-tuned

Optimized for dog breed classification performance through 50 epochs of fine-tuning.

Model Capabilities

Dog Breed Recognition

Image Classification

Use Cases

Pet-related Applications

Smart Pet Recognition

Used for automatic dog breed identification in pet apps or websites.

Accurately identifies 120 dog breeds (specific number not provided).

Veterinary Assistance

Assists veterinarians in quickly identifying dog breeds to understand breed characteristics.

🚀 ConvNextV2-large-DogBreed

This model is a fine - tuned version of facebook/convnextv2-large-22k-224 for dog breed classification, achieving high accuracy on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of facebook/convnextv2-large-22k-224 on dog breed classification dataset. It achieves the following results on the evaluation set:

Loss: 0.5469
Accuracy: 0.9139

📚 Documentation

Model Details

Property	Details
Model Type	ConvNextV2-large-DogBreed
Base Model	facebook/convnextv2-large-22k-224
Metrics	accuracy

Training and Evaluation

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training Results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.8578	1.0	13	4.6940	0.0671
4.6332	1.99	26	4.4169	0.1784
4.4095	2.99	39	4.1105	0.3485
3.8841	3.98	52	3.7581	0.5198
3.5964	4.98	65	3.3647	0.6647
3.2781	5.97	78	2.9442	0.7677
2.6006	6.97	91	2.5252	0.8180
2.2638	7.96	104	2.1256	0.8467
1.9609	8.96	117	1.7626	0.8766
1.3962	9.95	130	1.4453	0.9042
1.143	10.95	143	1.1818	0.9102
0.9423	11.94	156	0.9697	0.9138
0.7674	12.94	169	0.8097	0.9174
0.5007	13.93	182	0.6922	0.9186
0.4097	14.93	195	0.5999	0.9162
0.3392	16.0	209	0.5174	0.9269
0.2285	17.0	222	0.4685	0.9257
0.184	17.99	235	0.4337	0.9210
0.1587	18.99	248	0.4058	0.9257
0.1112	19.98	261	0.3824	0.9222
0.0967	20.98	274	0.3712	0.9150
0.0838	21.97	287	0.3584	0.9186
0.0665	22.97	300	0.3468	0.9174
0.0589	23.96	313	0.3428	0.9186
0.0551	24.96	326	0.3364	0.9186
0.0512	25.95	339	0.3334	0.9162
0.0441	26.95	352	0.3278	0.9210
0.0428	27.94	365	0.3275	0.9150
0.0387	28.94	378	0.3237	0.9210
0.036	29.93	391	0.3242	0.9150
0.0337	30.93	404	0.3204	0.9186
0.0328	32.0	418	0.3176	0.9198
0.0304	33.0	431	0.3183	0.9162
0.0283	33.99	444	0.3150	0.9210
0.029	34.99	457	0.3168	0.9174
0.0264	35.98	470	0.3146	0.9174
0.0259	36.98	483	0.3162	0.9174
0.0258	37.97	496	0.3126	0.9186
0.0251	38.97	509	0.3131	0.9174
0.0239	39.96	522	0.3145	0.9186
0.0234	40.96	535	0.3120	0.9198
0.023	41.95	548	0.3102	0.9198
0.0226	42.95	561	0.3123	0.9198
0.0222	43.94	574	0.3140	0.9186
0.0225	44.94	587	0.3119	0.9186
0.0215	45.93	600	0.3106	0.9198
0.0209	46.93	613	0.3113	0.9198
0.0212	48.0	627	0.3115	0.9198
0.021	49.0	640	0.3113	0.9198
0.0212	49.76	650	0.3113	0.9198

Framework Versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

📄 License

This model is licensed under the Apache 2.0 license.

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