Instructions to use mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5") - Notebooks
- Google Colab
- Kaggle
Daniel Bogdoll commited on
Training in progress, epoch 1
Browse files- config.json +69 -0
- preprocessor_config.json +26 -0
- pytorch_model.bin +3 -0
- runs/Feb06_19-44-39_mcity-rtx-4090/events.out.tfevents.1738889080.mcity-rtx-4090.112767.2 +3 -0
- runs/Feb07_11-41-22_mcity-rtx-4090/events.out.tfevents.1738946483.mcity-rtx-4090.206624.2 +3 -0
- runs/Feb07_12-12-50_mcity-rtx-4090/events.out.tfevents.1738948371.mcity-rtx-4090.21128.0 +3 -0
- training_args.bin +3 -0
config.json
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{
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"_name_or_path": "Omnifact/conditional-detr-resnet-101-dc5",
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"activation_dropout": 0.0,
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"activation_function": "relu",
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"architectures": [
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"ConditionalDetrForObjectDetection"
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],
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"attention_dropout": 0.0,
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"auxiliary_loss": false,
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"backbone": "resnet101",
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"backbone_config": null,
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"backbone_kwargs": {
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"in_chans": 3,
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"out_indices": [
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1,
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2,
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3,
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4
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]
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},
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"bbox_cost": 5,
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"bbox_loss_coefficient": 5,
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"class_cost": 2,
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"cls_loss_coefficient": 2,
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"d_model": 256,
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"decoder_attention_heads": 8,
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"decoder_ffn_dim": 2048,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"dice_loss_coefficient": 1,
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"dilation": false,
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"dropout": 0.1,
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"encoder_attention_heads": 8,
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"encoder_ffn_dim": 2048,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"focal_alpha": 0.25,
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"giou_cost": 2,
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"giou_loss_coefficient": 2,
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"id2label": {
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"0": "Bus",
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"1": "Bike",
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"2": "Car",
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"3": "Pedestrian",
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"4": "Truck"
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},
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"init_std": 0.02,
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"init_xavier_std": 1.0,
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"is_encoder_decoder": true,
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"label2id": {
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"Bike": 1,
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"Bus": 0,
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"Car": 2,
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"Pedestrian": 3,
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"Truck": 4
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},
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"mask_loss_coefficient": 1,
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"max_position_embeddings": 1024,
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"model_type": "conditional_detr",
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"num_channels": 3,
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"num_hidden_layers": 6,
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"num_queries": 300,
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"position_embedding_type": "sine",
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"scale_embedding": false,
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"torch_dtype": "float32",
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"transformers_version": "4.48.3",
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"use_pretrained_backbone": true,
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"use_timm_backbone": true
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}
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preprocessor_config.json
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{
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"do_convert_annotations": true,
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"do_normalize": true,
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"do_pad": true,
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"do_rescale": true,
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"do_resize": false,
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"format": "coco_detection",
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "ConditionalDetrImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"pad_size": null,
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"longest_edge": 1333,
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"shortest_edge": 800
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}
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:986f839662d0009080ad117dc6d1b7043d97e71c2b38329c6f250ca1ddf56c06
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size 250480758
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runs/Feb06_19-44-39_mcity-rtx-4090/events.out.tfevents.1738889080.mcity-rtx-4090.112767.2
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version https://git-lfs.github.com/spec/v1
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size 6180
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runs/Feb07_11-41-22_mcity-rtx-4090/events.out.tfevents.1738946483.mcity-rtx-4090.206624.2
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version https://git-lfs.github.com/spec/v1
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size 6180
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runs/Feb07_12-12-50_mcity-rtx-4090/events.out.tfevents.1738948371.mcity-rtx-4090.21128.0
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version https://git-lfs.github.com/spec/v1
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size 39021
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3aa4b2d937364c9c8825b57f60a84c91ebd1b9263edff96283bc4208f7820516
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size 5624
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