Image Classification
Transformers
Safetensors
convnextv2
Generated from Trainer
Eval Results (legacy)
Instructions to use louislu9911/convnextv2-base-22k-224-finetuned-cassava-leaf-disease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use louislu9911/convnextv2-base-22k-224-finetuned-cassava-leaf-disease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="louislu9911/convnextv2-base-22k-224-finetuned-cassava-leaf-disease") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("louislu9911/convnextv2-base-22k-224-finetuned-cassava-leaf-disease") model = AutoModelForImageClassification.from_pretrained("louislu9911/convnextv2-base-22k-224-finetuned-cassava-leaf-disease") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +93 -0
- model.safetensors +1 -1
README.md
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---
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license: apache-2.0
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base_model: facebook/convnextv2-base-22k-224
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: convnextv2-base-22k-224-finetuned-cassava-leaf-disease
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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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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8827102803738318
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# convnextv2-base-22k-224-finetuned-cassava-leaf-disease
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This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3524
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- Accuracy: 0.8827
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 360
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- eval_batch_size: 360
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 1440
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 16
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.504 | 0.96 | 13 | 0.9739 | 0.6159 |
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| 0.9073 | 2.0 | 27 | 0.5204 | 0.8187 |
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| 0.4289 | 2.96 | 40 | 0.4312 | 0.85 |
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| 0.3901 | 4.0 | 54 | 0.3916 | 0.8645 |
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| 0.34 | 4.96 | 67 | 0.3755 | 0.8715 |
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| 0.3326 | 6.0 | 81 | 0.3746 | 0.8710 |
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| 0.3153 | 6.96 | 94 | 0.3684 | 0.8771 |
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| 0.3103 | 8.0 | 108 | 0.3543 | 0.8780 |
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| 0.292 | 8.96 | 121 | 0.3620 | 0.8804 |
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| 0.2953 | 10.0 | 135 | 0.3545 | 0.8794 |
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| 0.2879 | 10.96 | 148 | 0.3550 | 0.8808 |
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| 0.2779 | 12.0 | 162 | 0.3504 | 0.8799 |
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| 0.2736 | 12.96 | 175 | 0.3554 | 0.8818 |
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| 0.2769 | 14.0 | 189 | 0.3526 | 0.8846 |
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| 0.2625 | 14.96 | 202 | 0.3527 | 0.8813 |
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| 0.2625 | 15.41 | 208 | 0.3524 | 0.8827 |
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.2.1
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- Datasets 2.18.0
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- Tokenizers 0.15.1
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model.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 350837748
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version https://git-lfs.github.com/spec/v1
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oid sha256:cb3218c491ec6b07c11724cd9bf8cb9f26f0ef9cea8f76ecc10ac405075ad04b
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size 350837748
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