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carolinetfls
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plant-seedlings-model-ConvNet

Image Classification
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convnext
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+ ---
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+ license: apache-2.0
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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: plant-seedlings-model-ConvNet
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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: 0.9598726114649682
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+ ---
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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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+
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+ # plant-seedlings-model-ConvNet
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+
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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: 0.2134
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+ - Accuracy: 0.9599
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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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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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.4223 | 0.8 | 100 | 0.2878 | 0.9140 |
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+ | 0.2957 | 1.6 | 200 | 0.2490 | 0.9204 |
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+ | 0.0884 | 2.4 | 300 | 0.2440 | 0.9293 |
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+ | 0.0534 | 3.2 | 400 | 0.2140 | 0.9350 |
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+ | 0.0067 | 4.0 | 500 | 0.1659 | 0.9554 |
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+ | 0.0038 | 4.8 | 600 | 0.1950 | 0.9548 |
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+ | 0.0061 | 5.6 | 700 | 0.1658 | 0.9618 |
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+ | 0.0029 | 6.4 | 800 | 0.1793 | 0.9599 |
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+ | 0.0004 | 7.2 | 900 | 0.2021 | 0.9592 |
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+ | 0.0003 | 8.0 | 1000 | 0.2115 | 0.9561 |
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+ | 0.0004 | 8.8 | 1100 | 0.2106 | 0.9561 |
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+ | 0.0002 | 9.6 | 1200 | 0.1929 | 0.9605 |
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+ | 0.0003 | 10.4 | 1300 | 0.2311 | 0.9548 |
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+ | 0.0002 | 11.2 | 1400 | 0.2091 | 0.9605 |
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+ | 0.0002 | 12.0 | 1500 | 0.2076 | 0.9586 |
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+ | 0.0001 | 12.8 | 1600 | 0.2084 | 0.9592 |
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+ | 0.0002 | 13.6 | 1700 | 0.2094 | 0.9605 |
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+ | 0.0001 | 14.4 | 1800 | 0.2104 | 0.9592 |
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+ | 0.0001 | 15.2 | 1900 | 0.2111 | 0.9592 |
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+ | 0.0001 | 16.0 | 2000 | 0.2117 | 0.9592 |
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+ | 0.0001 | 16.8 | 2100 | 0.2123 | 0.9592 |
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+ | 0.0001 | 17.6 | 2200 | 0.2128 | 0.9599 |
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+ | 0.0001 | 18.4 | 2300 | 0.2131 | 0.9599 |
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+ | 0.0001 | 19.2 | 2400 | 0.2134 | 0.9599 |
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+ | 0.0001 | 20.0 | 2500 | 0.2134 | 0.9599 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.1
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+ - Pytorch 2.0.0+cu118
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+ - Datasets 2.11.0
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+ - Tokenizers 0.13.3