Image Classification
Transformers
TensorBoard
Safetensors
convnext
Generated from Trainer
Eval Results (legacy)
Instructions to use rukundob451/convnext-tiny-224-finetuned-papsmear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rukundob451/convnext-tiny-224-finetuned-papsmear with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rukundob451/convnext-tiny-224-finetuned-papsmear") 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("rukundob451/convnext-tiny-224-finetuned-papsmear") model = AutoModelForImageClassification.from_pretrained("rukundob451/convnext-tiny-224-finetuned-papsmear", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +92 -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/convnext-tiny-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: convnext-tiny-224-finetuned-papsmear
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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: 0.7794117647058824
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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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# convnext-tiny-224-finetuned-papsmear
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6010
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- Accuracy: 0.7794
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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: 15
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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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| 1.8151 | 0.9870 | 19 | 1.6491 | 0.3456 |
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| 1.6104 | 1.9740 | 38 | 1.4322 | 0.4265 |
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| 1.4002 | 2.9610 | 57 | 1.2286 | 0.5882 |
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| 1.203 | 4.0 | 77 | 1.0559 | 0.6544 |
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| 1.047 | 4.9870 | 96 | 0.9357 | 0.6765 |
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| 0.9083 | 5.9740 | 115 | 0.8477 | 0.7279 |
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| 0.8756 | 6.9610 | 134 | 0.7762 | 0.75 |
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| 0.7853 | 8.0 | 154 | 0.7258 | 0.7647 |
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| 0.7198 | 8.9870 | 173 | 0.7023 | 0.7574 |
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| 0.7151 | 9.9740 | 192 | 0.6756 | 0.7574 |
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| 0.7049 | 10.9610 | 211 | 0.6493 | 0.7574 |
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| 0.6387 | 12.0 | 231 | 0.6256 | 0.7721 |
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| 0.6387 | 12.9870 | 250 | 0.6295 | 0.7721 |
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| 0.6233 | 13.9740 | 269 | 0.6033 | 0.7794 |
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| 0.632 | 14.8052 | 285 | 0.6010 | 0.7794 |
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### Framework versions
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- Transformers 4.44.0
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- Pytorch 2.4.0
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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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 111320240
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version https://git-lfs.github.com/spec/v1
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oid sha256:bc789181c201b87a9e0e146eda9e6ae7eae8872567449d4999e3267c6f46637c
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size 111320240
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